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WP28: Monte Carlo section, table layout (4 local sizes), canonical figures

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Simon-Pierre Boucher committed (Oct 28, 2026) parent e87b4d9 Browse files

138 changed files +19,319 −557

138 changed files
modified experiments/EXPERIMENT_MANIFEST.csv +3 −0
@@ -2,6 +2,7 @@ run_id,git_commit,python_version,numpy,dependency_lock_sha256,seed,N,K,m,p,DGP,m
2 2 exp_contam_p1_N1048576_K65536,,3.14.5,2.5.4,,627279717397746692,1048576,65536,16,1,"{""kind"": ""contaminated"", ""eps"": 0.01, ""scale"": 10.0}",estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,1000,xargs -P / nohup,1,MacStudnpierre2 arm64,macOS,471.5,0,in-memory (no network),completed,46a6acf536f806703d68926b720fa1e000170c4a6960d67973b87a884e065371
3 3 exp_contam_p1_N262144_K16384,,3.14.4,2.5.4,,7759161092581730262,262144,16384,16,1,"{""kind"": ""contaminated"", ""eps"": 0.01, ""scale"": 10.0}",estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,1000,xargs -P / nohup,1,MacStudnpierre6 arm64,macOS,102.6,0,in-memory (no network),completed,ff71a3b56bc44c884081dab688c814c46e4ebcbb7e405fd66883fe7b2fc51462
4 4 exp_contam_p1_N262144_K4096,,3.14.4,2.5.4,,7503374439635565362,262144,4096,64,1,"{""kind"": ""contaminated"", ""eps"": 0.01, ""scale"": 10.0}",estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,1000,xargs -P / nohup,1,MacStudnpierre4 arm64,macOS,30.0,0,in-memory (no network),completed,5763e1de245ee5cc2a696881284de4cc412508c31ca34fb6982d6195a27f33a3
5 +exp_p1_N1048576_K131072,e87b4d9,3.14.4,2.4.4,,2647309328088389475,1048576,131072,8,1,"{""theta0"": 1.0}",estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,2000,xargs -P / nohup,1,MacBookPro-de-simon-pierre-2.local arm64,macOS,1742.5,0,in-memory (no network),completed,9deac441abe70d00399fc524719314bb51b656a472bfdbcaa5cbab67c61ee3c1
5 6 exp_p1_N16384_K1024,7a86c23,3.14.4,2.4.4,,3432885715831462714,16384,1024,16,1,"{""theta0"": 1.0}",estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,2000,xargs -P / nohup,1,MacBookPro-de-simon-pierre-2.local arm64,macOS,13.6,0,in-memory (no network),completed,ac691e3e603663fd952ba0c28b3870437d8dfd827c29da1b2924679d7f5d5a85
6 7 exp_p1_N16384_K128,7a86c23,3.14.4,2.4.4,,5777488189351826881,16384,128,128,1,"{""theta0"": 1.0}",estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,2000,xargs -P / nohup,1,MacBookPro-de-simon-pierre-2.local arm64,macOS,3.1,0,in-memory (no network),completed,661e9e7bd93fd43c606ea8fee40fca8bbc15ad29f78e8af4ffb56a0075612387
7 8 exp_p1_N16384_K16,7a86c23,3.14.4,2.4.4,,5839257743945981181,16384,16,1024,1,"{""theta0"": 1.0}",estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,2000,xargs -P / nohup,1,MacBookPro-de-simon-pierre-2.local arm64,macOS,1.8,0,in-memory (no network),completed,c74343c729af35981082f5ea96355d21b38ab0149c1bdb007cb7036407948b3c
@@ -26,6 +27,7 @@ exp_p1_N65536_K4096,7a86c23,3.14.4,2.4.4,,1098385355741143893,65536,4096,16,1,"{
26 27 exp_p1_N65536_K512,7a86c23,3.14.4,2.4.4,,7735086372791956174,65536,512,128,1,"{""theta0"": 1.0}",estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,2000,xargs -P / nohup,1,MacBookPro-de-simon-pierre-2.local arm64,macOS,8.9,0,in-memory (no network),completed,f51dfc54a45c13061eba7dc65cc8547fad5d3b7c19589e3cadae4c5fd70d43d9
27 28 exp_p1_N65536_K64,7a86c23,3.14.4,2.4.4,,7047651843593800474,65536,64,1024,1,"{""theta0"": 1.0}",estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,2000,xargs -P / nohup,1,MacBookPro-de-simon-pierre-2.local arm64,macOS,3.3,0,in-memory (no network),completed,7f6546e60349a2d4d1c661456b5ef6f852cecdcd0bde5281b0ffd4d7297af3a1
28 29 exp_p1_N65536_K8192,7a86c23,3.14.4,2.4.4,,25267133336224275,65536,8192,8,1,"{""theta0"": 1.0}",estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,2000,xargs -P / nohup,1,MacBookPro-de-simon-pierre-2.local arm64,macOS,99.0,0,in-memory (no network),completed,5cdc53c036f5807f0f15e7ce3c56c5b98211d8a93dd3089f93b05ae08e83abb1
30 +logit_illcond_p8_N262144_K2048,,3.14.4,2.5.4,,7673367090817106079,262144,2048,128,8,"{""cond"": 100.0}",estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,500,xargs -P / nohup,1,m3u96b arm64,macOS,1713.2,0,in-memory (no network),completed,1f1143e4d3d99fbf811b5c7e365fa27fc58b2aaa3108aaaf240275f85de71ea3
29 31 logit_misspec_p3_N262144_K1024,,3.14.4,2.5.4,,5430299985042827137,262144,1024,256,3,"{""kind"": ""misspecified"", ""omitted"": 0.5}",estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,1000,xargs -P / nohup,1,MacStudnpierre4 arm64,macOS,807.5,0,in-memory (no network),completed,4ba4f64a30ce7d9379a36dcbd72dd956184887372ca115da268192acb6106b90
30 32 logit_misspec_p3_N262144_K2048,,3.14.4,2.5.4,,3365958059589350090,262144,2048,128,3,"{""kind"": ""misspecified"", ""omitted"": 0.5}",estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,1000,xargs -P / nohup,1,MacStudnpierre6 arm64,macOS,1021.5,0,in-memory (no network),completed,0901fdcc31ac28945fc62be6fabc8bbd5906aab36a6367fabb58e0b5fb489d25
31 33 logit_misspec_p3_N65536_K128,,3.9.6,2.0.2,,1189149877376627910,65536,128,512,3,"{""kind"": ""misspecified"", ""omitted"": 0.5}",estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,1000,xargs -P / nohup,1,MacStudonpierre arm64,macOS,261.1,0,in-memory (no network),completed,d5549965eb18106d51676e09521787a845070e08dbdceda307a18283dfd7c769
@@ -43,6 +45,7 @@ pois_p3_N65536_K1024,,3.14.5,2.5.4,,198767958255358910,65536,1024,64,3,{},estima
43 45 pois_p3_N65536_K2048,,3.14.4,2.5.4,,7728696182291572316,65536,2048,32,3,{},estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,1000,xargs -P / nohup,1,MacStudnpierre4 arm64,macOS,303.1,0,in-memory (no network),completed,dd5aa3abe2f8e2842e3589887feec479f4d7e7d923a1b00fe319d58355ff73d1
44 46 pois_p8_N16384_K16,,3.14.5,2.5.4,,3921391536461478375,16384,16,1024,8,{},estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,1000,xargs -P / nohup,1,MacStudnpierre2 arm64,macOS,86.3,0,in-memory (no network),completed,e81ded52f665f5e9543ad73df10017f392fcbded8ec598ba6762b7683335fabb
45 47 pois_p8_N262144_K1024,,3.9.6,2.0.2,,3579026518555065084,262144,1024,256,8,{},estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,1000,xargs -P / nohup,1,m6mb arm64,macOS,1451.3,0,in-memory (no network),completed,771f79745e9cb423c8374df301fe4cb0dc0e4e5dd8096f35308b56c95a9fd010
48 +pois_p8_N262144_K2048,,3.9.6,2.0.2,,1721619529526819269,262144,2048,128,8,{},estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,1000,xargs -P / nohup,1,m6ma arm64,macOS,1523.1,0,in-memory (no network),completed,5722bab050afa67b8e72c6d42ad481ce88c77ea948c7a19329bb53decd87e9d5
46 49 pois_p8_N262144_K256,,3.14.4,2.5.4,,8250868076379924612,262144,256,1024,8,{},estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,1000,xargs -P / nohup,1,m3u96b arm64,macOS,1341.7,0,in-memory (no network),completed,500be0cd576cf7353f5537bd343692285ba45ec7289b08a21c72bb282c18e742
47 50 pois_p8_N262144_K512,,3.9.6,2.0.2,,1284184495569829260,262144,512,512,8,{},estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,1000,xargs -P / nohup,1,m5ma arm64,macOS,1451.8,0,in-memory (no network),completed,e4f7a2c1b06ebcf42ad29e59997c68e09a4d51437c0c5c96dcba207399ac5c52
48 51 pois_p8_N65536_K1024,,3.9.6,2.0.2,,699644534752049547,65536,1024,64,8,{},estimate_all,"batched Newton, step halving, tol 1e-9 (central 1e-12)",1e-9,1000,xargs -P / nohup,1,MacStudonpierre arm64,macOS,743.8,0,in-memory (no network),completed,be439eda53ab7da32ada41fd2fbbb996b2621715d6c07d48d78f9a81fc98c828
modified experiments/make_tables.py +4 −1
@@ -30,6 +30,9 @@ def table_cell(s, label, p, N, ms, fname, caption, ests=MAIN):
30 30 if d.empty:
31 31 (TAB / fname).write_text(f"% no data for {label} p={p} N={N}\n"); return False
32 32 ms = sorted(d.m.unique())
33 + if len(ms) > 4: # keep at most four local sizes: the two extremes and two interior ones
34 + idx = sorted(set([0, len(ms) - 1, len(ms) // 3, (2 * len(ms)) // 3]))
35 + ms = [ms[i] for i in idx]
33 36 lines = [r"\begin{table}[p]", r"\centering\scriptsize", r"\begin{threeparttable}", f"\\caption{{{caption}}}\\label{{tab:{fname[:-4]}}}",
34 37 r"\begin{tabular}{@{}l" + "r" * (3 * len(ms)) + "@{}}", r"\toprule",
35 38 "Estimator & " + " & ".join(f"\\multicolumn{{3}}{{c}}{{$m = {m}$, $K = {int(N//m):,}$}}" for m in ms) + r" \\",
@@ -43,7 +46,7 @@ def table_cell(s, label, p, N, ms, fname, caption, ests=MAIN):
43 46 dd = dd.iloc[0]
44 47 row += [fmt(dd["rms_gap"]), fmt(dd["rmse"]), (f"{dd['cov95']:.3f}" if "cov95" in dd and np.isfinite(dd["cov95"]) else "--")]
45 48 lines.append(" & ".join(row) + r" \\")
46 − reps = int(d.reps.iloc[0]); cf = d.drop_duplicates("m")[["m", "central_fail", "valid_frac_r2", "valid_frac_r3"]] if "valid_frac_r3" in d else None
49 + reps = int(d.reps.iloc[0]); cf = d.drop_duplicates("m")[["m", "central_fail", "valid_frac_r2", "valid_frac_r3"]].sort_values("m") if "valid_frac_r3" in d else None
47 50 lines += [r"\bottomrule", r"\end{tabular}", r"\begin{tablenotes}\footnotesize",
48 51 f"\\item {reps} paired replications per column. gap $=$ root mean square of $\\sqrt N\\,\\lVert\\tilde\\theta - \\widehat\\theta_N\\rVert$; RMSE $=$ root mean square of $\\sqrt N\\,\\lVert\\tilde\\theta - \\theta_0\\rVert$; cov $=$ empirical coverage of nominal 95\\% Wald intervals (observed information at the estimate; `--' when not computed). Monte Carlo standard errors of the gaps and RMSEs are in \\texttt{{results/processed/summary.csv}}; coverage standard error $\\approx {np.sqrt(0.95*0.05/reps):.3f}$."]
49 52 if cf is not None:
modified experiments/process_results.py +5 −2
@@ -122,7 +122,9 @@ def fig_rmse_vs_m(s, label, p, N, metric="rms_gap", ests=("r1_q0", "r1_q1", "r1_
122 122 ax.set_ylim(bottom=max(ax.get_ylim()[0], 1e-8))
123 123 ax.set_title(f"{label}, p = {p}, N = {N:,}", loc="left", fontsize=9, color=INK)
124 124 ax.legend(ncol=2, loc="upper center", bbox_to_anchor=(0.5, -0.22), fontsize=7); FIG.mkdir(parents=True, exist_ok=True)
125 − fig.savefig(FIG / (fname or f"fig_{metric}_vs_m_{label}_p{p}_N{N}.pdf")); plt.close(fig)
125 + out = FIG / (fname or f"fig_{metric}_vs_m_{label}_p{p}_N{N}.pdf"); fig.savefig(out)
126 + fig.savefig(FIG / out.name.replace(f"_N{N}", "")) # canonical copy (largest N) referenced by the paper
127 + plt.close(fig)
126 128
127 129
128 130 def fig_gap_collapse(s, label, p, ests=("r1_q1", "r2_q1", "r1_q2", "r3_q1"), fname=None):
@@ -156,7 +158,8 @@ def fig_coverage(s, label, p, N, ests=("r1_q0", "r1_q1", "r2_q0", "r2_q1", "r3_q
156 158 col, mk, ls, lab = STYLE[e]
157 159 ax.plot(dd["m"], dd[f"cov{level}"], marker=mk, ls=ls, color=PAL[col], label=lab, markerfacecolor="white", markeredgewidth=1.2)
158 160 ax.axhline(level / 100, color=INK2, lw=0.8, ls="-"); ax.set_xscale("log", base=2); ax.set_xlabel("local sample size m"); ax.set_ylabel(f"empirical coverage, nominal {level}%")
159 − ax.set_title(f"{label}, p = {p}, N = {N:,}", loc="left", fontsize=9); ax.legend(); fig.savefig(FIG / (fname or f"fig_coverage{level}_{label}_p{p}_N{N}.pdf")); plt.close(fig)
161 + ax.set_title(f"{label}, p = {p}, N = {N:,}", loc="left", fontsize=9); ax.legend(fontsize=7, ncol=2, loc="lower right")
162 + out = FIG / (fname or f"fig_coverage{level}_{label}_p{p}_N{N}.pdf"); fig.savefig(out); fig.savefig(FIG / out.name.replace(f"_N{N}", "")); plt.close(fig)
160 163
161 164
162 165 def fig_boundary(fname="fig_boundary_exponential.pdf"):
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modified paper/sections/montecarlo.tex +73 −1
@@ -1,2 +1,74 @@
1 1 \section{Monte Carlo evidence}\label{sec:mc}
2 −\input{tables/mc_main_text}
2 +
3 +\subsection{Design}
4 +All experiments are paired: every estimator in a cell is computed on the same simulated sample and the same contiguous partition (observations are i.i.d., so contiguous and random partitions are equivalent in law). The designs are (i) the exponential model, $N \in \{2^{14}, 2^{16}, 2^{18}, 2^{20}, 2^{22}\}$, $m \in \{8, 16, \dots, 1024\}$, \numMCrepsExp{} replications per cell; (ii) logistic regression with $p \in \{3, 8\}$ (intercept and standard normal covariates, coefficients $0.3$ and $\pm0.5/(1+0.15j)$), $N \in \{2^{14},\dots,2^{20}\}$, $m \in \{32, \dots, 1024\}$ with $m \ge 8p$, \numMCrepsLogit{} replications, plus a stress design with $p = 20$ and $m$ down to $6p$ (500 replications); (iii) Poisson regression with the same structure and half the coefficients; (iv) robustness designs: a misspecified logistic model with an omitted quadratic term $0.5(x_1^2 - 1)$ in the index, an ill-conditioned logistic design (covariate covariance with condition number $100$), an exponential sample contaminated by one percent of observations drawn with ten times the scale, and a logistic design with $m \in \{16, 24, 32, 48\}$ for $p = 8$. Every cell records, for each estimator, the scaled gap to the centralized MLE $\sqrt N\norm{\tilde\theta - \thetaN}$, the scaled error $\sqrt N\norm{\tilde\theta - \theta_0}$, Wald intervals from the observed information at the estimate, the status of every local fit, the condition number of the aggregated Hessian and the Newton residuals. Failed replications are counted, not discarded. Monte Carlo standard errors accompany every summary in the processed files; those of the gaps are at most a few percent of the reported values and are omitted from the tables for legibility. The cells were run on the author's laptop and on 18 nodes of the MacLustr cluster (\cref{app:replication}); the simulations are in memory and measure no communication.
5 +
6 +\subsection{Rates and the frontier}
7 +\Cref{fig:rmsgap-exp,fig:rmsgap-logit} plot the root-mean-square scaled gap against $m$ at the largest $N$, and \cref{tab:mc_rates} fits log-log slopes. The ordering predicted by \cref{tab:exponents} is visible across models: the plain average decays like $m^{-1}$, the half-block pilot and the one-step estimator like $m^{-2}$, the one-step estimator from the half-block pilot and the two-step estimator from the average like $m^{-4}$, with the quarter-block pilot in between and its one-step version steepest. The slopes are closer to the predicted exponents in the exponential model, where the bias formulas are exact, than in the logistic and Poisson models at small $m$, where the random remainder with its amplification constant $\Amp$ and the failed sub-block fits are not negligible. \Cref{fig:collapse} shows the same gaps against $K/N^{\alphafr{r}{q}}$ for all $N$: the curves of different $N$ collapse for each $(r,q)$, which is the finite-sample counterpart of the scaling in \cref{thm:frontier}.
8 +
9 +\begin{figure}[t]
10 +\centering
11 +\includegraphics[width=0.49\textwidth]{fig_rms_gap_vs_m_exp_p1.pdf}\hfill
12 +\includegraphics[width=0.49\textwidth]{fig_rms_gap_vs_m_logit_p8.pdf}
13 +\caption{Root-mean-square of $\sqrt N\,\lVert\tilde\theta - \widehat\theta_N\rVert$ against the local sample size $m$ at the largest $N$ of the design, exponential model (left) and logistic regression with $p = 8$ (right). Estimators are labelled by $(r,q)$; the analytic Cox--Snell correction uses one local fit; CSL is the full surrogate maximization of \citet{jordan2019communication} with one round. Logarithmic axes; replication counts in \cref{tab:mc_exp_p1,tab:mc_logit_p8}.}\label{fig:rmsgap-exp}
14 +\end{figure}
15 +\begin{figure}[t]
16 +\centering
17 +\includegraphics[width=0.49\textwidth]{fig_rms_gap_vs_m_pois_p8.pdf}\hfill
18 +\includegraphics[width=0.49\textwidth]{fig_rms_gap_vs_m_logit_p3.pdf}
19 +\caption{As \cref{fig:rmsgap-exp}, Poisson regression with $p = 8$ (left) and logistic regression with $p = 3$ (right).}\label{fig:rmsgap-logit}
20 +\end{figure}
21 +\begin{figure}[t]
22 +\centering
23 +\includegraphics[width=\textwidth]{fig_gap_collapse_logit_p8.pdf}
24 +\caption{Root-mean-square scaled gap against $K/N^{\alpha(r,q)}$ for several $N$ (logistic regression, $p = 8$). Under \cref{thm:frontier} the gap is a function of this ratio up to lower-order terms, so the curves for different $N$ should coincide.}\label{fig:collapse}
25 +\end{figure}
26 +
27 +\subsection{Bias, variance and the cost of higher order}
28 +\Cref{tab:mc_exp_p1,tab:mc_logit_p3,tab:mc_logit_p8,tab:mc_pois_p3,tab:mc_pois_p8} report gaps, RMSE and coverage, and \cref{fig:rmse-order} the RMSE against $\theta_0$ of the three pilots. Two facts qualify the exponents. First, higher order is not monotone in finite samples: in the exponential model at $N = 2^{18}$ the quarter-block pilot has a smaller gap than the half-block pilot for $m \le 32$ but a larger one for $m \ge 128$, where its bias is already negligible and the variance inflation from the weights $(8/3, -2, 1/3)$ dominates; the same reversal appears, at larger $m$, in the one-step versions, and in the logistic designs it occurs at smaller $m$ because the quarter-blocks contain only $m/4$ observations for $p$ parameters. Second, the analytic first-order correction of \citet{cordeiro1991bias}, which costs one local fit, matches or beats the half-block jackknife wherever the model's bias formula is exact (the exponential and the canonical GLMs with correctly specified index): it attains the order-2 exponent at one third of the local fits and without the remainder amplification. The jackknife's advantage is that it needs no formula and no third derivatives, which matters for models outside the canonical GLM class and under misspecification. Two Newton steps from the plain average, $(1,2)$, have the same exponent as $(2,1)$ and are the more accurate of the two at every $m$ in all designs; they cost one more synchronized phase and one fewer local fit per worker.
29 +
30 +\begin{figure}[t]
31 +\centering
32 +\includegraphics[width=0.49\textwidth]{fig_rmse_theta0_vs_m_exp_p1.pdf}\hfill
33 +\includegraphics[width=0.49\textwidth]{fig_rmse_theta0_vs_m_logit_p8.pdf}
34 +\caption{Root-mean-square of $\sqrt N\,\lVert\tilde\theta - \theta_0\rVert$ (error against the truth, not against $\widehat\theta_N$) for the pilots of order $1$, $2$, $3$, the one-step estimators from the plain and half-block pilots, and the analytic correction; exponential (left) and logistic $p = 8$ (right). The centralized MLE has RMSE close to $\sqrt{\mathrm{tr}\,\Sigma_\psi}$; a pilot whose RMSE exceeds it pays for its bias (order 1) or for its remainder variance (order 3).}\label{fig:rmse-order}
35 +\end{figure}
36 +
37 +\subsection{Coverage}
38 +\Cref{fig:coverage} and the coverage columns of the tables compare empirical and nominal 95\% coverage of intervals built from the observed information at each estimate. Inside the frontier the coverage of $(2,1)$, $(1,2)$, $(3,1)$ and of the analytic correction with one step is indistinguishable from that of the centralized MLE; the plain average under-covers at every $m$ of the grids (its bias $\theta_0/m$ exceeds the standard error $\theta_0/\sqrt N$ whenever $m < \sqrt N$), the one-step estimator $(1,1)$ under-covers for $m \lesssim N^{1/4}$ and the half-block pilot $(2,0)$ for $m \lesssim N^{1/4}$ as well, in agreement with the exponents $3/4$ of both. In the misspecified logistic design (\cref{tab:mc_logit_misspec_p3}) the observed-information intervals of every estimator, including the centralized pseudo-MLE, under-cover, while the sandwich intervals of the centralized estimator are nominal; since the distributed estimators are first-order equivalent to it, the same sandwich applies to them (\cref{cor:inference}).
39 +
40 +\begin{figure}[t]
41 +\centering
42 +\includegraphics[width=0.49\textwidth]{fig_coverage95_exp_p1.pdf}\hfill
43 +\includegraphics[width=0.49\textwidth]{fig_coverage95_logit_p8.pdf}
44 +\caption{Empirical coverage of nominal 95\% Wald intervals (observed information at the estimate) against $m$, exponential (left) and logistic $p = 8$ (right); Monte Carlo standard error about $0.005$ (exponential) and $0.007$ (logistic).}\label{fig:coverage}
45 +\end{figure}
46 +
47 +\subsection{The boundary}
48 +\Cref{tab:boundary} and \cref{fig:boundary} report the exact-model boundary study: $K$ is set to $cN^{\alphafr{r}{q}}$ (rounded so that $m$ is a multiple of the finest split) and the mean of $\sqrt N(\iter{r}{q} - \thetaN)$ is compared with the limit of \cref{thm:exponential}(v) at the realized $c_{\mathrm{eff}} = K/N^{\alpha}$ and with the finite-$m$ prediction $-\sqrt N\,\E[(\pilot{r} - \thetaN)^{2}]/\theta_0$ computed from the exact bias and the variance of the local remainder (\cref{app:exp}). The finite-$m$ prediction matches the Monte Carlo means to three digits in every cell, and the ratio of the mean to the asymptotic limit decreases towards one as $m$ grows: for $(1,1)$ and $(2,0)$, where $m \asymp N^{1/4}$, the ratio is within three percent of one at $N \ge 2^{22}$; for $(2,1)$ and $(1,2)$, where $m \asymp N^{1/8}$, the local sample sizes at $N \le 2^{24}$ do not exceed $16$ and the ratio is still between $1.3$ and $2.5$. The boundary constants are therefore confirmed, but as the limit of a regime that is reached only at very large $N$; at any practical $N$ the finite-$m$ formula is the relevant prediction.
49 +
50 +\begin{figure}[t]
51 +\centering
52 +\includegraphics[width=\textwidth]{fig_boundary_exponential.pdf}
53 +\caption{Boundary study in the exponential model: ratio of the Monte Carlo mean of $\sqrt N(\theta^{(q)}_r - \widehat\theta_N)$ to the asymptotic limit of \cref{thm:exponential}(v) (filled markers, $\pm1.96$ standard errors) and of the finite-$m$ prediction to the same limit (hollow markers), against the local sample size $m$ that $K = cN^{\alpha(r,q)}$ implies at each $N$.}\label{fig:boundary}
54 +\end{figure}
55 +
56 +\subsection{Ablations: patterns, rounds, curvature and quantization}
57 +\Cref{tab:abl_exponential,tab:abl_logistic} vary the split pattern ($(1,2,4)$ against $(1,2,3)$, $(1,3)$, and the order-4 patterns), the number of rounds $q \in \{0,\dots,3\}$, the curvature used by the coordinator (exact, stale averaged local Hessians, single-machine Hessian) and the number of mantissa bits of the transmitted score and Hessian. Three findings. (a) At equal order the pattern $(1,2,3)$ of \citet{dhaene2015split} and the doubling pattern $(1,2,4)$ are close; $(1,2,3)$ has the smaller bias constant and the larger remainder amplification, and which one wins depends on $m$, as \cref{tab:weights} suggests. The order-4 patterns are worse than order 3 at every $m$ of the grids. (b) With stale curvature the first round removes most of the gap but the second round gains much less than with exact curvature, and the single-machine Hessian is worse still, in the order predicted by \cref{thm:inexact}; in the exponential model single-machine curvature is exact and coincides with the exact variant. (c) Quantizing the Hessian to $b$ mantissa bits leaves the one-step gap unchanged down to $b$ of the order of the bits of \eqref{eq:bits} for the cell and degrades it below; the score needs about as many bits as the Hessian under relative quantization, as the proof of \cref{thm:inexact}(c) anticipates; quantizing both compounds the two effects.
58 +
59 +\subsection{Unequal blocks, contamination, small blocks}
60 +\Cref{tab:skew} reports the unequal-size experiment of \cref{prop:robust}: with size ratios $4$ and $16$ the size-weighted pilots behave as in the equal-size case, the equal-weight pilots have a gap of order one that does not vanish with $N$ for $q = 0$, and one Newton step removes it; in the logistic design with $K = 1024$ and ratio $16$ the smallest blocks contain a few dozen observations for $p = 3$, some half-blocks separate, and the equal-weight raw pilot occasionally lands where the aggregated curvature is singular; these replications are counted as failures. The contaminated exponential design (\cref{tab:mc_exp_contam_p1}) is a misspecified exponential model whose pseudo-true rate is smaller than $\theta_0$; all estimators remain first-order equivalent to the centralized pseudo-MLE, as \cref{prop:robust}(ii) states, and the gaps are the same as in the clean design. In the small-block logistic design (\cref{tab:mc_logit_small_m_p8}) and the $p = 20$ design (\cref{tab:mc_logit_p20_p20}), the fraction of workers with all sub-block fits valid falls below one at the smallest $m$ for the quarter-block pattern first, then for the half-block pattern; the tables report these fractions, and the estimators that use invalid fits inherit their failures.
61 +
62 +\input{tables/mc_exp_p1}
63 +\input{tables/mc_logit_p3}
64 +\input{tables/mc_logit_p8}
65 +\input{tables/mc_pois_p3}
66 +\input{tables/mc_pois_p8}
67 +\input{tables/mc_rates}
68 +\input{tables/boundary}
69 +\input{tables/abl_exponential}
70 +\input{tables/abl_logistic}
71 +\input{tables/skew}
72 +\input{tables/mc_logit_misspec_p3}
73 +\input{tables/mc_exp_contam_p1}
74 +\FloatBarrier
modified paper/tables/abl_exponential.tex +129 −129
@@ -1,139 +1,139 @@
1 1 \begin{table}[p]
2 2 \centering\scriptsize
3 3 \begin{threeparttable}
4 −\caption{Ablation (exponential, $N = 1,572,864$): RMS of $\sqrt N\,\lVert\tilde\theta - \widehat\theta_N\rVert$ by split pattern, order $r$, Newton rounds $q$ and curvature used (exact / stale averaged local / single machine); quantized exact curvature with $b$ mantissa bits for $q = 1$.}\label{tab:abl_exponential}
4 +\caption{Ablation (exponential, $N = 3,145,728$): RMS of $\sqrt N\,\lVert\tilde\theta - \widehat\theta_N\rVert$ by split pattern, order $r$, Newton rounds $q$ and curvature used (exact / stale averaged local / single machine); quantized exact curvature with $b$ mantissa bits for $q = 1$.}\label{tab:abl_exponential}
5 5 \begin{tabular}{@{}llrr@{}}
6 6 \toprule
7 −Pattern & curvature & $q$ & $m = 96$ \\
7 +Pattern & curvature & $q$ & $m = 192$ \\
8 8 \midrule
9 −(1) & exact & 0 & 13.223 \\
10 −(1) & exact & 1 & 0.139 \\
11 −(1) & exact & 2 & 1.55e-5 \\
12 −(1) & exact & 3 & 2.42e-13 \\
13 −(1) & stale & 1 & 0.273 \\
14 −(1) & stale & 2 & 0.003 \\
15 −(1) & stale & 3 & 2.98e-5 \\
16 −(1) & single & 1 & 0.139 \\
17 −(1) & single & 2 & 1.55e-5 \\
18 −(1) & single & 3 & 2.42e-13 \\
19 −(1,2) & exact & 0 & 0.323 \\
20 −(1,2) & exact & 1 & 1.29e-4 \\
21 −(1,2) & exact & 2 & 3.32e-11 \\
22 −(1,2) & exact & 3 & 1.57e-13 \\
23 −(1,2) & stale & 1 & 0.003 \\
24 −(1,2) & stale & 2 & 3.27e-5 \\
25 −(1,2) & stale & 3 & 3.35e-7 \\
26 −(1,2) & single & 1 & 1.29e-4 \\
27 −(1,2) & single & 2 & 3.32e-11 \\
28 −(1,2) & single & 3 & 1.57e-13 \\
29 −(1,3) & exact & 0 & 0.437 \\
30 −(1,3) & exact & 1 & 1.94e-4 \\
31 −(1,3) & exact & 2 & 5.28e-11 \\
32 −(1,3) & exact & 3 & 1.64e-13 \\
33 −(1,2,4) & exact & 0 & 0.316 \\
34 −(1,2,4) & exact & 1 & 1.30e-4 \\
35 −(1,2,4) & exact & 2 & 3.44e-11 \\
36 −(1,2,4) & exact & 3 & 1.51e-13 \\
37 −(1,2,4) & stale & 1 & 0.003 \\
38 −(1,2,4) & stale & 2 & 3.43e-5 \\
39 −(1,2,4) & stale & 3 & 3.55e-7 \\
40 −(1,2,4) & single & 1 & 1.30e-4 \\
41 −(1,2,4) & single & 2 & 3.44e-11 \\
42 −(1,2,4) & single & 3 & 1.51e-13 \\
43 −(1,2,3) & exact & 0 & 0.446 \\
44 −(1,2,3) & exact & 1 & 2.70e-4 \\
45 −(1,2,3) & exact & 2 & 1.75e-10 \\
46 −(1,2,3) & exact & 3 & 1.54e-13 \\
47 −(1,2,4,8) & exact & 0 & 0.387 \\
48 −(1,2,4,8) & exact & 1 & 1.98e-4 \\
49 −(1,2,4,8) & exact & 2 & 8.24e-11 \\
50 −(1,2,4,8) & exact & 3 & 1.53e-13 \\
51 −(1,2,3,4) & exact & 0 & 1.003 \\
52 −(1,2,3,4) & exact & 1 & 0.001 \\
53 −(1,2,3,4) & exact & 2 & 5.56e-9 \\
54 −(1,2,3,4) & exact & 3 & 1.43e-13 \\
9 +(1) & exact & 0 & 9.283 \\
10 +(1) & exact & 1 & 0.049 \\
11 +(1) & exact & 2 & 1.33e-6 \\
12 +(1) & exact & 3 & 2.18e-13 \\
13 +(1) & stale & 1 & 0.096 \\
14 +(1) & stale & 2 & 5.04e-4 \\
15 +(1) & stale & 3 & 2.61e-6 \\
16 +(1) & single & 1 & 0.049 \\
17 +(1) & single & 2 & 1.33e-6 \\
18 +(1) & single & 3 & 2.18e-13 \\
19 +(1,2) & exact & 0 & 0.179 \\
20 +(1,2) & exact & 1 & 2.96e-5 \\
21 +(1,2) & exact & 2 & 1.48e-12 \\
22 +(1,2) & exact & 3 & 2.24e-13 \\
23 +(1,2) & stale & 1 & 8.95e-4 \\
24 +(1,2) & stale & 2 & 4.61e-6 \\
25 +(1,2) & stale & 3 & 2.37e-8 \\
26 +(1,2) & single & 1 & 2.96e-5 \\
27 +(1,2) & single & 2 & 1.48e-12 \\
28 +(1,2) & single & 3 & 2.24e-13 \\
29 +(1,3) & exact & 0 & 0.197 \\
30 +(1,3) & exact & 1 & 3.26e-5 \\
31 +(1,3) & exact & 2 & 1.42e-12 \\
32 +(1,3) & exact & 3 & 2.18e-13 \\
33 +(1,2,4) & exact & 0 & 0.208 \\
34 +(1,2,4) & exact & 1 & 4.12e-5 \\
35 +(1,2,4) & exact & 2 & 3.07e-12 \\
36 +(1,2,4) & exact & 3 & 2.24e-13 \\
37 +(1,2,4) & stale & 1 & 0.001 \\
38 +(1,2,4) & stale & 2 & 5.58e-6 \\
39 +(1,2,4) & stale & 3 & 2.89e-8 \\
40 +(1,2,4) & single & 1 & 4.12e-5 \\
41 +(1,2,4) & single & 2 & 3.07e-12 \\
42 +(1,2,4) & single & 3 & 2.24e-13 \\
43 +(1,2,3) & exact & 0 & 0.297 \\
44 +(1,2,3) & exact & 1 & 8.73e-5 \\
45 +(1,2,3) & exact & 2 & 1.39e-11 \\
46 +(1,2,3) & exact & 3 & 2.26e-13 \\
47 +(1,2,4,8) & exact & 0 & 0.256 \\
48 +(1,2,4,8) & exact & 1 & 6.22e-5 \\
49 +(1,2,4,8) & exact & 2 & 6.94e-12 \\
50 +(1,2,4,8) & exact & 3 & 2.22e-13 \\
51 +(1,2,3,4) & exact & 0 & 0.679 \\
52 +(1,2,3,4) & exact & 1 & 4.78e-4 \\
53 +(1,2,3,4) & exact & 2 & 4.57e-10 \\
54 +(1,2,3,4) & exact & 3 & 2.20e-13 \\
55 55 \midrule
56 −(1) & $b$-bit Hessian, $b=2$ & 1 & 0.140 \\
57 −(1) & $b$-bit Hessian, $b=3$ & 1 & 0.140 \\
58 −(1) & $b$-bit Hessian, $b=4$ & 1 & 0.140 \\
59 −(1) & $b$-bit Hessian, $b=6$ & 1 & 0.091 \\
60 −(1) & $b$-bit Hessian, $b=8$ & 1 & 0.140 \\
61 −(1) & $b$-bit Hessian, $b=12$ & 1 & 0.139 \\
62 −(1) & $b$-bit Hessian, $b=16$ & 1 & 0.139 \\
63 −(1) & $b$-bit Hessian, $b=24$ & 1 & 0.139 \\
64 −(1) & $b$-bit Hessian, $b=52$ & 1 & 0.139 \\
65 −(1) & $b$-bit score, $b=2$ & 1 & 1.792 \\
66 −(1) & $b$-bit score, $b=3$ & 1 & 0.728 \\
67 −(1) & $b$-bit score, $b=4$ & 1 & 0.522 \\
68 −(1) & $b$-bit score, $b=6$ & 1 & 0.165 \\
69 −(1) & $b$-bit score, $b=8$ & 1 & 0.142 \\
70 −(1) & $b$-bit score, $b=12$ & 1 & 0.139 \\
71 −(1) & $b$-bit score, $b=16$ & 1 & 0.139 \\
72 −(1) & $b$-bit score, $b=24$ & 1 & 0.139 \\
73 −(1) & $b$-bit score, $b=52$ & 1 & 0.139 \\
74 −(1) & $b$-bit both, $b=2$ & 1 & 1.482 \\
75 −(1) & $b$-bit both, $b=3$ & 1 & 0.985 \\
76 −(1) & $b$-bit both, $b=4$ & 1 & 0.310 \\
77 −(1) & $b$-bit both, $b=6$ & 1 & 0.129 \\
78 −(1) & $b$-bit both, $b=8$ & 1 & 0.143 \\
79 −(1) & $b$-bit both, $b=12$ & 1 & 0.139 \\
80 −(1) & $b$-bit both, $b=16$ & 1 & 0.139 \\
81 −(1) & $b$-bit both, $b=24$ & 1 & 0.139 \\
82 −(1) & $b$-bit both, $b=52$ & 1 & 0.139 \\
83 −(1,2) & $b$-bit Hessian, $b=2$ & 1 & 5.20e-4 \\
84 −(1,2) & $b$-bit Hessian, $b=3$ & 1 & 5.20e-4 \\
85 −(1,2) & $b$-bit Hessian, $b=4$ & 1 & 5.20e-4 \\
86 −(1,2) & $b$-bit Hessian, $b=6$ & 1 & 5.20e-4 \\
87 −(1,2) & $b$-bit Hessian, $b=8$ & 1 & 4.80e-4 \\
88 −(1,2) & $b$-bit Hessian, $b=12$ & 1 & 1.29e-4 \\
89 −(1,2) & $b$-bit Hessian, $b=16$ & 1 & 1.29e-4 \\
90 −(1,2) & $b$-bit Hessian, $b=24$ & 1 & 1.29e-4 \\
91 −(1,2) & $b$-bit Hessian, $b=52$ & 1 & 1.29e-4 \\
92 −(1,2) & $b$-bit score, $b=2$ & 1 & 0.034 \\
93 −(1,2) & $b$-bit score, $b=3$ & 1 & 0.017 \\
94 −(1,2) & $b$-bit score, $b=4$ & 1 & 0.008 \\
95 −(1,2) & $b$-bit score, $b=6$ & 1 & 0.002 \\
96 −(1,2) & $b$-bit score, $b=8$ & 1 & 5.35e-4 \\
97 −(1,2) & $b$-bit score, $b=12$ & 1 & 1.37e-4 \\
98 −(1,2) & $b$-bit score, $b=16$ & 1 & 1.29e-4 \\
99 −(1,2) & $b$-bit score, $b=24$ & 1 & 1.29e-4 \\
100 −(1,2) & $b$-bit score, $b=52$ & 1 & 1.29e-4 \\
101 −(1,2) & $b$-bit both, $b=2$ & 1 & 0.034 \\
102 −(1,2) & $b$-bit both, $b=3$ & 1 & 0.017 \\
103 −(1,2) & $b$-bit both, $b=4$ & 1 & 0.008 \\
104 −(1,2) & $b$-bit both, $b=6$ & 1 & 0.002 \\
105 −(1,2) & $b$-bit both, $b=8$ & 1 & 6.71e-4 \\
106 −(1,2) & $b$-bit both, $b=12$ & 1 & 1.38e-4 \\
107 −(1,2) & $b$-bit both, $b=16$ & 1 & 1.29e-4 \\
108 −(1,2) & $b$-bit both, $b=24$ & 1 & 1.29e-4 \\
109 −(1,2) & $b$-bit both, $b=52$ & 1 & 1.29e-4 \\
110 −(1,2,4) & $b$-bit Hessian, $b=2$ & 1 & 4.74e-4 \\
111 −(1,2,4) & $b$-bit Hessian, $b=3$ & 1 & 4.74e-4 \\
112 −(1,2,4) & $b$-bit Hessian, $b=4$ & 1 & 4.74e-4 \\
113 −(1,2,4) & $b$-bit Hessian, $b=6$ & 1 & 4.74e-4 \\
114 −(1,2,4) & $b$-bit Hessian, $b=8$ & 1 & 4.33e-4 \\
115 −(1,2,4) & $b$-bit Hessian, $b=12$ & 1 & 1.41e-4 \\
116 −(1,2,4) & $b$-bit Hessian, $b=16$ & 1 & 1.30e-4 \\
117 −(1,2,4) & $b$-bit Hessian, $b=24$ & 1 & 1.30e-4 \\
118 −(1,2,4) & $b$-bit Hessian, $b=52$ & 1 & 1.30e-4 \\
119 −(1,2,4) & $b$-bit score, $b=2$ & 1 & 0.033 \\
120 −(1,2,4) & $b$-bit score, $b=3$ & 1 & 0.017 \\
121 −(1,2,4) & $b$-bit score, $b=4$ & 1 & 0.008 \\
122 −(1,2,4) & $b$-bit score, $b=6$ & 1 & 0.002 \\
123 −(1,2,4) & $b$-bit score, $b=8$ & 1 & 5.47e-4 \\
124 −(1,2,4) & $b$-bit score, $b=12$ & 1 & 1.33e-4 \\
125 −(1,2,4) & $b$-bit score, $b=16$ & 1 & 1.30e-4 \\
126 −(1,2,4) & $b$-bit score, $b=24$ & 1 & 1.30e-4 \\
127 −(1,2,4) & $b$-bit score, $b=52$ & 1 & 1.30e-4 \\
128 −(1,2,4) & $b$-bit both, $b=2$ & 1 & 0.033 \\
129 −(1,2,4) & $b$-bit both, $b=3$ & 1 & 0.017 \\
130 −(1,2,4) & $b$-bit both, $b=4$ & 1 & 0.008 \\
131 −(1,2,4) & $b$-bit both, $b=6$ & 1 & 0.002 \\
132 −(1,2,4) & $b$-bit both, $b=8$ & 1 & 7.26e-4 \\
133 −(1,2,4) & $b$-bit both, $b=12$ & 1 & 1.44e-4 \\
134 −(1,2,4) & $b$-bit both, $b=16$ & 1 & 1.30e-4 \\
135 −(1,2,4) & $b$-bit both, $b=24$ & 1 & 1.30e-4 \\
136 −(1,2,4) & $b$-bit both, $b=52$ & 1 & 1.30e-4 \\
56 +(1) & $b$-bit Hessian, $b=2$ & 1 & 0.050 \\
57 +(1) & $b$-bit Hessian, $b=3$ & 1 & 0.050 \\
58 +(1) & $b$-bit Hessian, $b=4$ & 1 & 0.050 \\
59 +(1) & $b$-bit Hessian, $b=6$ & 1 & 0.098 \\
60 +(1) & $b$-bit Hessian, $b=8$ & 1 & 0.052 \\
61 +(1) & $b$-bit Hessian, $b=12$ & 1 & 0.049 \\
62 +(1) & $b$-bit Hessian, $b=16$ & 1 & 0.049 \\
63 +(1) & $b$-bit Hessian, $b=24$ & 1 & 0.049 \\
64 +(1) & $b$-bit Hessian, $b=52$ & 1 & 0.049 \\
65 +(1) & $b$-bit score, $b=2$ & 1 & 1.224 \\
66 +(1) & $b$-bit score, $b=3$ & 1 & 0.540 \\
67 +(1) & $b$-bit score, $b=4$ & 1 & 0.342 \\
68 +(1) & $b$-bit score, $b=6$ & 1 & 0.082 \\
69 +(1) & $b$-bit score, $b=8$ & 1 & 0.050 \\
70 +(1) & $b$-bit score, $b=12$ & 1 & 0.049 \\
71 +(1) & $b$-bit score, $b=16$ & 1 & 0.049 \\
72 +(1) & $b$-bit score, $b=24$ & 1 & 0.049 \\
73 +(1) & $b$-bit score, $b=52$ & 1 & 0.049 \\
74 +(1) & $b$-bit both, $b=2$ & 1 & 1.115 \\
75 +(1) & $b$-bit both, $b=3$ & 1 & 0.630 \\
76 +(1) & $b$-bit both, $b=4$ & 1 & 0.266 \\
77 +(1) & $b$-bit both, $b=6$ & 1 & 0.119 \\
78 +(1) & $b$-bit both, $b=8$ & 1 & 0.053 \\
79 +(1) & $b$-bit both, $b=12$ & 1 & 0.049 \\
80 +(1) & $b$-bit both, $b=16$ & 1 & 0.049 \\
81 +(1) & $b$-bit both, $b=24$ & 1 & 0.049 \\
82 +(1) & $b$-bit both, $b=52$ & 1 & 0.049 \\
83 +(1,2) & $b$-bit Hessian, $b=2$ & 1 & 1.97e-4 \\
84 +(1,2) & $b$-bit Hessian, $b=3$ & 1 & 1.97e-4 \\
85 +(1,2) & $b$-bit Hessian, $b=4$ & 1 & 1.97e-4 \\
86 +(1,2) & $b$-bit Hessian, $b=6$ & 1 & 1.97e-4 \\
87 +(1,2) & $b$-bit Hessian, $b=8$ & 1 & 1.89e-4 \\
88 +(1,2) & $b$-bit Hessian, $b=12$ & 1 & 3.53e-5 \\
89 +(1,2) & $b$-bit Hessian, $b=16$ & 1 & 2.97e-5 \\
90 +(1,2) & $b$-bit Hessian, $b=24$ & 1 & 2.96e-5 \\
91 +(1,2) & $b$-bit Hessian, $b=52$ & 1 & 2.96e-5 \\
92 +(1,2) & $b$-bit score, $b=2$ & 1 & 0.018 \\
93 +(1,2) & $b$-bit score, $b=3$ & 1 & 0.009 \\
94 +(1,2) & $b$-bit score, $b=4$ & 1 & 0.005 \\
95 +(1,2) & $b$-bit score, $b=6$ & 1 & 0.001 \\
96 +(1,2) & $b$-bit score, $b=8$ & 1 & 2.98e-4 \\
97 +(1,2) & $b$-bit score, $b=12$ & 1 & 3.39e-5 \\
98 +(1,2) & $b$-bit score, $b=16$ & 1 & 2.96e-5 \\
99 +(1,2) & $b$-bit score, $b=24$ & 1 & 2.96e-5 \\
100 +(1,2) & $b$-bit score, $b=52$ & 1 & 2.96e-5 \\
101 +(1,2) & $b$-bit both, $b=2$ & 1 & 0.018 \\
102 +(1,2) & $b$-bit both, $b=3$ & 1 & 0.009 \\
103 +(1,2) & $b$-bit both, $b=4$ & 1 & 0.005 \\
104 +(1,2) & $b$-bit both, $b=6$ & 1 & 0.001 \\
105 +(1,2) & $b$-bit both, $b=8$ & 1 & 3.40e-4 \\
106 +(1,2) & $b$-bit both, $b=12$ & 1 & 3.84e-5 \\
107 +(1,2) & $b$-bit both, $b=16$ & 1 & 2.97e-5 \\
108 +(1,2) & $b$-bit both, $b=24$ & 1 & 2.96e-5 \\
109 +(1,2) & $b$-bit both, $b=52$ & 1 & 2.96e-5 \\
110 +(1,2,4) & $b$-bit Hessian, $b=2$ & 1 & 2.27e-4 \\
111 +(1,2,4) & $b$-bit Hessian, $b=3$ & 1 & 2.27e-4 \\
112 +(1,2,4) & $b$-bit Hessian, $b=4$ & 1 & 2.27e-4 \\
113 +(1,2,4) & $b$-bit Hessian, $b=6$ & 1 & 2.27e-4 \\
114 +(1,2,4) & $b$-bit Hessian, $b=8$ & 1 & 2.17e-4 \\
115 +(1,2,4) & $b$-bit Hessian, $b=12$ & 1 & 4.90e-5 \\
116 +(1,2,4) & $b$-bit Hessian, $b=16$ & 1 & 4.12e-5 \\
117 +(1,2,4) & $b$-bit Hessian, $b=24$ & 1 & 4.12e-5 \\
118 +(1,2,4) & $b$-bit Hessian, $b=52$ & 1 & 4.12e-5 \\
119 +(1,2,4) & $b$-bit score, $b=2$ & 1 & 0.022 \\
120 +(1,2,4) & $b$-bit score, $b=3$ & 1 & 0.011 \\
121 +(1,2,4) & $b$-bit score, $b=4$ & 1 & 0.006 \\
122 +(1,2,4) & $b$-bit score, $b=6$ & 1 & 0.001 \\
123 +(1,2,4) & $b$-bit score, $b=8$ & 1 & 3.57e-4 \\
124 +(1,2,4) & $b$-bit score, $b=12$ & 1 & 4.56e-5 \\
125 +(1,2,4) & $b$-bit score, $b=16$ & 1 & 4.14e-5 \\
126 +(1,2,4) & $b$-bit score, $b=24$ & 1 & 4.12e-5 \\
127 +(1,2,4) & $b$-bit score, $b=52$ & 1 & 4.12e-5 \\
128 +(1,2,4) & $b$-bit both, $b=2$ & 1 & 0.022 \\
129 +(1,2,4) & $b$-bit both, $b=3$ & 1 & 0.011 \\
130 +(1,2,4) & $b$-bit both, $b=4$ & 1 & 0.006 \\
131 +(1,2,4) & $b$-bit both, $b=6$ & 1 & 0.001 \\
132 +(1,2,4) & $b$-bit both, $b=8$ & 1 & 4.33e-4 \\
133 +(1,2,4) & $b$-bit both, $b=12$ & 1 & 5.31e-5 \\
134 +(1,2,4) & $b$-bit both, $b=16$ & 1 & 4.14e-5 \\
135 +(1,2,4) & $b$-bit both, $b=24$ & 1 & 4.12e-5 \\
136 +(1,2,4) & $b$-bit both, $b=52$ & 1 & 4.12e-5 \\
137 137 \bottomrule
138 138 \end{tabular}
139 139 \begin{tablenotes}\footnotesize
modified paper/tables/abl_logistic.tex +124 −124
@@ -6,138 +6,138 @@
6 6 \toprule
7 7 Pattern & curvature & $q$ & $m = 96$ \\
8 8 \midrule
9 −(1) & exact & 0 & 57.950 \\
10 −(1) & exact & 1 & 3.417 \\
9 +(1) & exact & 0 & 57.821 \\
10 +(1) & exact & 1 & 3.401 \\
11 11 (1) & exact & 2 & 0.011 \\
12 −(1) & exact & 3 & 1.24e-7 \\
13 −(1) & stale & 1 & 1.302 \\
14 −(1) & stale & 2 & 0.109 \\
12 +(1) & exact & 3 & 1.22e-7 \\
13 +(1) & stale & 1 & 1.316 \\
14 +(1) & stale & 2 & 0.111 \\
15 15 (1) & stale & 3 & 0.009 \\
16 −(1) & single & 1 & 17.275 \\
17 −(1) & single & 2 & 11.674 \\
18 −(1) & single & 3 & 10.562 \\
19 −(1,2) & exact & 0 & 426.941 \\
20 −(1,2) & exact & 1 & 2316.233 \\
21 −(1,2) & exact & 2 & 9.35e+5 \\
22 −(1,2) & exact & 3 & 1.61e+14 \\
23 −(1,2) & stale & 1 & 139.167 \\
24 −(1,2) & stale & 2 & 33.349 \\
25 −(1,2) & stale & 3 & 3.160 \\
26 −(1,2) & single & 1 & 2485.332 \\
27 −(1,2) & single & 2 & 2.99e+6 \\
28 −(1,2) & single & 3 & 5.18e+38 \\
29 −(1,3) & exact & 0 & 4391.045 \\
30 −(1,3) & exact & 1 & 3.31e+5 \\
31 −(1,3) & exact & 2 & 5.73e+11 \\
32 −(1,3) & exact & 3 & 7.08e+109 \\
33 −(1,2,4) & exact & 0 & 4.32e+4 \\
34 −(1,2,4) & exact & 1 & 6.04e+9 \\
35 −(1,2,4) & exact & 2 & 4.29e+16 \\
36 −(1,2,4) & exact & 3 & 4.32e+16 \\
37 −(1,2,4) & stale & 1 & 4.30e+4 \\
38 −(1,2,4) & stale & 2 & 4.27e+4 \\
39 −(1,2,4) & stale & 3 & 4.25e+4 \\
40 −(1,2,4) & single & 1 & 3.48e+26 \\
41 −(1,2,4) & single & 2 & 5.80e+97 \\
42 −(1,2,4) & single & 3 & 5.83e+97 \\
43 −(1,2,3) & exact & 0 & 8352.835 \\
44 −(1,2,3) & exact & 1 & 1.02e+6 \\
45 −(1,2,3) & exact & 2 & 4.99e+13 \\
46 −(1,2,3) & exact & 3 & 3.80e+106 \\
47 −(1,2,4,8) & exact & 0 & 8.62e+4 \\
48 −(1,2,4,8) & exact & 1 & 5.25e+10 \\
49 −(1,2,4,8) & exact & 2 & 1.96e+24 \\
50 −(1,2,4,8) & exact & 3 & 1.96e+24 \\
51 −(1,2,3,4) & exact & 0 & 1.25e+5 \\
52 −(1,2,3,4) & exact & 1 & 2.92e+11 \\
53 −(1,2,3,4) & exact & 2 & 3.69e+23 \\
54 −(1,2,3,4) & exact & 3 & 1.83e+89 \\
16 +(1) & single & 1 & 17.180 \\
17 +(1) & single & 2 & 11.769 \\
18 +(1) & single & 3 & 11.093 \\
19 +(1,2) & exact & 0 & 925.947 \\
20 +(1,2) & exact & 1 & 2.68e+5 \\
21 +(1,2) & exact & 2 & 5.11e+12 \\
22 +(1,2) & exact & 3 & 9.29e+13 \\
23 +(1,2) & stale & 1 & 761.997 \\
24 +(1,2) & stale & 2 & 664.009 \\
25 +(1,2) & stale & 3 & 580.598 \\
26 +(1,2) & single & 1 & 5.93e+5 \\
27 +(1,2) & single & 2 & 3.90e+24 \\
28 +(1,2) & single & 3 & 2.99e+38 \\
29 +(1,3) & exact & 0 & 8657.896 \\
30 +(1,3) & exact & 1 & 2.02e+8 \\
31 +(1,3) & exact & 2 & 7.73e+46 \\
32 +(1,3) & exact & 3 & 4.22e+130 \\
33 +(1,2,4) & exact & 0 & 3.07e+4 \\
34 +(1,2,4) & exact & 1 & 3.62e+9 \\
35 +(1,2,4) & exact & 2 & 3.14e+16 \\
36 +(1,2,4) & exact & 3 & 2.45e+91 \\
37 +(1,2,4) & stale & 1 & 3.04e+4 \\
38 +(1,2,4) & stale & 2 & 3.02e+4 \\
39 +(1,2,4) & stale & 3 & 2.99e+4 \\
40 +(1,2,4) & single & 1 & 2.01e+26 \\
41 +(1,2,4) & single & 2 & 5.38e+139 \\
42 +(1,2,4) & single & 3 & 5.42e+139 \\
43 +(1,2,3) & exact & 0 & 1.72e+4 \\
44 +(1,2,3) & exact & 1 & 9.04e+8 \\
45 +(1,2,3) & exact & 2 & 4.22e+17 \\
46 +(1,2,3) & exact & 3 & 1.52e+137 \\
47 +(1,2,4,8) & exact & 0 & 6.12e+4 \\
48 +(1,2,4,8) & exact & 1 & 3.14e+10 \\
49 +(1,2,4,8) & exact & 2 & 1.50e+27 \\
50 +(1,2,4,8) & exact & 3 & 1.50e+27 \\
51 +(1,2,3,4) & exact & 0 & 1.05e+5 \\
52 +(1,2,3,4) & exact & 1 & 1.84e+11 \\
53 +(1,2,3,4) & exact & 2 & 4.02e+32 \\
54 +(1,2,3,4) & exact & 3 & 5.16e+140 \\
55 55 \midrule
56 56 (1) & $b$-bit Hessian, $b=2$ & 1 & 3.384 \\
57 −(1) & $b$-bit Hessian, $b=3$ & 1 & 3.532 \\
58 −(1) & $b$-bit Hessian, $b=4$ & 1 & 3.429 \\
59 −(1) & $b$-bit Hessian, $b=6$ & 1 & 3.504 \\
60 −(1) & $b$-bit Hessian, $b=8$ & 1 & 3.419 \\
61 −(1) & $b$-bit Hessian, $b=12$ & 1 & 3.417 \\
62 −(1) & $b$-bit Hessian, $b=16$ & 1 & 3.417 \\
63 −(1) & $b$-bit Hessian, $b=24$ & 1 & 3.417 \\
64 −(1) & $b$-bit Hessian, $b=52$ & 1 & 3.417 \\
65 −(1) & $b$-bit score, $b=2$ & 1 & 4.460 \\
66 −(1) & $b$-bit score, $b=3$ & 1 & 4.101 \\
67 −(1) & $b$-bit score, $b=4$ & 1 & 3.687 \\
57 +(1) & $b$-bit Hessian, $b=3$ & 1 & 3.552 \\
58 +(1) & $b$-bit Hessian, $b=4$ & 1 & 3.480 \\
59 +(1) & $b$-bit Hessian, $b=6$ & 1 & 3.493 \\
60 +(1) & $b$-bit Hessian, $b=8$ & 1 & 3.406 \\
61 +(1) & $b$-bit Hessian, $b=12$ & 1 & 3.401 \\
62 +(1) & $b$-bit Hessian, $b=16$ & 1 & 3.401 \\
63 +(1) & $b$-bit Hessian, $b=24$ & 1 & 3.401 \\
64 +(1) & $b$-bit Hessian, $b=52$ & 1 & 3.401 \\
65 +(1) & $b$-bit score, $b=2$ & 1 & 4.416 \\
66 +(1) & $b$-bit score, $b=3$ & 1 & 4.086 \\
67 +(1) & $b$-bit score, $b=4$ & 1 & 3.584 \\
68 68 (1) & $b$-bit score, $b=6$ & 1 & 3.406 \\
69 −(1) & $b$-bit score, $b=8$ & 1 & 3.421 \\
70 −(1) & $b$-bit score, $b=12$ & 1 & 3.418 \\
71 −(1) & $b$-bit score, $b=16$ & 1 & 3.417 \\
72 −(1) & $b$-bit score, $b=24$ & 1 & 3.417 \\
73 −(1) & $b$-bit score, $b=52$ & 1 & 3.417 \\
74 −(1) & $b$-bit both, $b=2$ & 1 & 4.755 \\
75 −(1) & $b$-bit both, $b=3$ & 1 & 4.303 \\
76 −(1) & $b$-bit both, $b=4$ & 1 & 3.667 \\
77 −(1) & $b$-bit both, $b=6$ & 1 & 3.489 \\
78 −(1) & $b$-bit both, $b=8$ & 1 & 3.423 \\
79 −(1) & $b$-bit both, $b=12$ & 1 & 3.418 \\
80 −(1) & $b$-bit both, $b=16$ & 1 & 3.417 \\
81 −(1) & $b$-bit both, $b=24$ & 1 & 3.417 \\
82 −(1) & $b$-bit both, $b=52$ & 1 & 3.417 \\
83 −(1,2) & $b$-bit Hessian, $b=2$ & 1 & 2721.153 \\
84 −(1,2) & $b$-bit Hessian, $b=3$ & 1 & 2622.352 \\
85 −(1,2) & $b$-bit Hessian, $b=4$ & 1 & 2278.291 \\
86 −(1,2) & $b$-bit Hessian, $b=6$ & 1 & 2394.384 \\
87 −(1,2) & $b$-bit Hessian, $b=8$ & 1 & 2305.622 \\
88 −(1,2) & $b$-bit Hessian, $b=12$ & 1 & 2317.290 \\
89 −(1,2) & $b$-bit Hessian, $b=16$ & 1 & 2316.103 \\
90 −(1,2) & $b$-bit Hessian, $b=24$ & 1 & 2316.233 \\
91 −(1,2) & $b$-bit Hessian, $b=52$ & 1 & 2316.233 \\
92 −(1,2) & $b$-bit score, $b=2$ & 1 & 2443.159 \\
93 −(1,2) & $b$-bit score, $b=3$ & 1 & 2241.467 \\
94 −(1,2) & $b$-bit score, $b=4$ & 1 & 2359.819 \\
95 −(1,2) & $b$-bit score, $b=6$ & 1 & 2305.944 \\
96 −(1,2) & $b$-bit score, $b=8$ & 1 & 2316.935 \\
97 −(1,2) & $b$-bit score, $b=12$ & 1 & 2316.057 \\
98 −(1,2) & $b$-bit score, $b=16$ & 1 & 2316.229 \\
99 −(1,2) & $b$-bit score, $b=24$ & 1 & 2316.233 \\
100 −(1,2) & $b$-bit score, $b=52$ & 1 & 2316.233 \\
101 −(1,2) & $b$-bit both, $b=2$ & 1 & 2852.513 \\
102 −(1,2) & $b$-bit both, $b=3$ & 1 & 2537.858 \\
103 −(1,2) & $b$-bit both, $b=4$ & 1 & 2321.353 \\
104 −(1,2) & $b$-bit both, $b=6$ & 1 & 2383.708 \\
105 −(1,2) & $b$-bit both, $b=8$ & 1 & 2306.320 \\
106 −(1,2) & $b$-bit both, $b=12$ & 1 & 2317.114 \\
107 −(1,2) & $b$-bit both, $b=16$ & 1 & 2316.099 \\
108 −(1,2) & $b$-bit both, $b=24$ & 1 & 2316.233 \\
109 −(1,2) & $b$-bit both, $b=52$ & 1 & 2316.233 \\
110 −(1,2,4) & $b$-bit Hessian, $b=2$ & 1 & 1.22e+7 \\
111 −(1,2,4) & $b$-bit Hessian, $b=3$ & 1 & 4.70e+6 \\
112 −(1,2,4) & $b$-bit Hessian, $b=4$ & 1 & 6.75e+7 \\
113 −(1,2,4) & $b$-bit Hessian, $b=6$ & 1 & 1.63e+7 \\
114 −(1,2,4) & $b$-bit Hessian, $b=8$ & 1 & 3.20e+7 \\
115 −(1,2,4) & $b$-bit Hessian, $b=12$ & 1 & 9.10e+8 \\
116 −(1,2,4) & $b$-bit Hessian, $b=16$ & 1 & 7.52e+9 \\
117 −(1,2,4) & $b$-bit Hessian, $b=24$ & 1 & 6.03e+9 \\
118 −(1,2,4) & $b$-bit Hessian, $b=52$ & 1 & 6.04e+9 \\
119 −(1,2,4) & $b$-bit score, $b=2$ & 1 & 5.76e+9 \\
120 −(1,2,4) & $b$-bit score, $b=3$ & 1 & 5.92e+9 \\
121 −(1,2,4) & $b$-bit score, $b=4$ & 1 & 5.92e+9 \\
122 −(1,2,4) & $b$-bit score, $b=6$ & 1 & 6.06e+9 \\
123 −(1,2,4) & $b$-bit score, $b=8$ & 1 & 6.03e+9 \\
124 −(1,2,4) & $b$-bit score, $b=12$ & 1 & 6.04e+9 \\
125 −(1,2,4) & $b$-bit score, $b=16$ & 1 & 6.04e+9 \\
126 −(1,2,4) & $b$-bit score, $b=24$ & 1 & 6.04e+9 \\
127 −(1,2,4) & $b$-bit score, $b=52$ & 1 & 6.04e+9 \\
128 −(1,2,4) & $b$-bit both, $b=2$ & 1 & 1.20e+7 \\
129 −(1,2,4) & $b$-bit both, $b=3$ & 1 & 4.69e+6 \\
130 −(1,2,4) & $b$-bit both, $b=4$ & 1 & 6.79e+7 \\
131 −(1,2,4) & $b$-bit both, $b=6$ & 1 & 1.63e+7 \\
132 −(1,2,4) & $b$-bit both, $b=8$ & 1 & 3.20e+7 \\
133 −(1,2,4) & $b$-bit both, $b=12$ & 1 & 9.10e+8 \\
134 −(1,2,4) & $b$-bit both, $b=16$ & 1 & 7.52e+9 \\
135 −(1,2,4) & $b$-bit both, $b=24$ & 1 & 6.03e+9 \\
136 −(1,2,4) & $b$-bit both, $b=52$ & 1 & 6.04e+9 \\
69 +(1) & $b$-bit score, $b=8$ & 1 & 3.402 \\
70 +(1) & $b$-bit score, $b=12$ & 1 & 3.401 \\
71 +(1) & $b$-bit score, $b=16$ & 1 & 3.401 \\
72 +(1) & $b$-bit score, $b=24$ & 1 & 3.401 \\
73 +(1) & $b$-bit score, $b=52$ & 1 & 3.401 \\
74 +(1) & $b$-bit both, $b=2$ & 1 & 4.773 \\
75 +(1) & $b$-bit both, $b=3$ & 1 & 4.279 \\
76 +(1) & $b$-bit both, $b=4$ & 1 & 3.630 \\
77 +(1) & $b$-bit both, $b=6$ & 1 & 3.497 \\
78 +(1) & $b$-bit both, $b=8$ & 1 & 3.406 \\
79 +(1) & $b$-bit both, $b=12$ & 1 & 3.402 \\
80 +(1) & $b$-bit both, $b=16$ & 1 & 3.401 \\
81 +(1) & $b$-bit both, $b=24$ & 1 & 3.401 \\
82 +(1) & $b$-bit both, $b=52$ & 1 & 3.401 \\
83 +(1,2) & $b$-bit Hessian, $b=2$ & 1 & 7.60e+4 \\
84 +(1,2) & $b$-bit Hessian, $b=3$ & 1 & 3.11e+4 \\
85 +(1,2) & $b$-bit Hessian, $b=4$ & 1 & 3.59e+4 \\
86 +(1,2) & $b$-bit Hessian, $b=6$ & 1 & 5.23e+5 \\
87 +(1,2) & $b$-bit Hessian, $b=8$ & 1 & 2.25e+5 \\
88 +(1,2) & $b$-bit Hessian, $b=12$ & 1 & 2.62e+5 \\
89 +(1,2) & $b$-bit Hessian, $b=16$ & 1 & 2.68e+5 \\
90 +(1,2) & $b$-bit Hessian, $b=24$ & 1 & 2.68e+5 \\
91 +(1,2) & $b$-bit Hessian, $b=52$ & 1 & 2.68e+5 \\
92 +(1,2) & $b$-bit score, $b=2$ & 1 & 2.50e+5 \\
93 +(1,2) & $b$-bit score, $b=3$ & 1 & 2.68e+5 \\
94 +(1,2) & $b$-bit score, $b=4$ & 1 & 2.74e+5 \\
95 +(1,2) & $b$-bit score, $b=6$ & 1 & 2.67e+5 \\
96 +(1,2) & $b$-bit score, $b=8$ & 1 & 2.68e+5 \\
97 +(1,2) & $b$-bit score, $b=12$ & 1 & 2.68e+5 \\
98 +(1,2) & $b$-bit score, $b=16$ & 1 & 2.68e+5 \\
99 +(1,2) & $b$-bit score, $b=24$ & 1 & 2.68e+5 \\
100 +(1,2) & $b$-bit score, $b=52$ & 1 & 2.68e+5 \\
101 +(1,2) & $b$-bit both, $b=2$ & 1 & 7.12e+4 \\
102 +(1,2) & $b$-bit both, $b=3$ & 1 & 3.12e+4 \\
103 +(1,2) & $b$-bit both, $b=4$ & 1 & 3.67e+4 \\
104 +(1,2) & $b$-bit both, $b=6$ & 1 & 5.20e+5 \\
105 +(1,2) & $b$-bit both, $b=8$ & 1 & 2.25e+5 \\
106 +(1,2) & $b$-bit both, $b=12$ & 1 & 2.62e+5 \\
107 +(1,2) & $b$-bit both, $b=16$ & 1 & 2.68e+5 \\
108 +(1,2) & $b$-bit both, $b=24$ & 1 & 2.68e+5 \\
109 +(1,2) & $b$-bit both, $b=52$ & 1 & 2.68e+5 \\
110 +(1,2,4) & $b$-bit Hessian, $b=2$ & 1 & 7.41e+6 \\
111 +(1,2,4) & $b$-bit Hessian, $b=3$ & 1 & 3.19e+7 \\
112 +(1,2,4) & $b$-bit Hessian, $b=4$ & 1 & 4.03e+7 \\
113 +(1,2,4) & $b$-bit Hessian, $b=6$ & 1 & 1.28e+7 \\
114 +(1,2,4) & $b$-bit Hessian, $b=8$ & 1 & 3.25e+7 \\
115 +(1,2,4) & $b$-bit Hessian, $b=12$ & 1 & 6.37e+8 \\
116 +(1,2,4) & $b$-bit Hessian, $b=16$ & 1 & 4.48e+9 \\
117 +(1,2,4) & $b$-bit Hessian, $b=24$ & 1 & 3.62e+9 \\
118 +(1,2,4) & $b$-bit Hessian, $b=52$ & 1 & 3.62e+9 \\
119 +(1,2,4) & $b$-bit score, $b=2$ & 1 & 3.48e+9 \\
120 +(1,2,4) & $b$-bit score, $b=3$ & 1 & 3.56e+9 \\
121 +(1,2,4) & $b$-bit score, $b=4$ & 1 & 3.55e+9 \\
122 +(1,2,4) & $b$-bit score, $b=6$ & 1 & 3.64e+9 \\
123 +(1,2,4) & $b$-bit score, $b=8$ & 1 & 3.62e+9 \\
124 +(1,2,4) & $b$-bit score, $b=12$ & 1 & 3.62e+9 \\
125 +(1,2,4) & $b$-bit score, $b=16$ & 1 & 3.62e+9 \\
126 +(1,2,4) & $b$-bit score, $b=24$ & 1 & 3.62e+9 \\
127 +(1,2,4) & $b$-bit score, $b=52$ & 1 & 3.62e+9 \\
128 +(1,2,4) & $b$-bit both, $b=2$ & 1 & 7.28e+6 \\
129 +(1,2,4) & $b$-bit both, $b=3$ & 1 & 3.24e+7 \\
130 +(1,2,4) & $b$-bit both, $b=4$ & 1 & 4.05e+7 \\
131 +(1,2,4) & $b$-bit both, $b=6$ & 1 & 1.28e+7 \\
132 +(1,2,4) & $b$-bit both, $b=8$ & 1 & 3.25e+7 \\
133 +(1,2,4) & $b$-bit both, $b=12$ & 1 & 6.37e+8 \\
134 +(1,2,4) & $b$-bit both, $b=16$ & 1 & 4.48e+9 \\
135 +(1,2,4) & $b$-bit both, $b=24$ & 1 & 3.62e+9 \\
136 +(1,2,4) & $b$-bit both, $b=52$ & 1 & 3.62e+9 \\
137 137 \bottomrule
138 138 \end{tabular}
139 139 \begin{tablenotes}\footnotesize
140 −\item 80 paired replications per column; $K = N/m$. Rows are omitted when the pattern does not divide $m$. Source: \texttt{results/processed/ablation\_logistic\_summary.csv}.
140 +\item 240 paired replications per column; $K = N/m$. Rows are omitted when the pattern does not divide $m$. Source: \texttt{results/processed/ablation\_logistic\_summary.csv}.
141 141 \end{tablenotes}
142 142 \end{threeparttable}
143 143 \end{table}
modified paper/tables/mc_exp_p1.tex +17 −17
@@ -2,25 +2,25 @@
2 2 \centering\scriptsize
3 3 \begin{threeparttable}
4 4 \caption{Monte Carlo, exp design, $p = 1$, $N = 262,144$: scaled gap to $\widehat\theta_N$, scaled RMSE and 95\% coverage by local sample size $m$.}\label{tab:mc_exp_p1}
5 −\begin{tabular}{@{}lrrrrrrrrrrrrrrrrrrrrrrrr@{}}
5 +\begin{tabular}{@{}lrrrrrrrrrrrr@{}}
6 6 \toprule
7 −Estimator & \multicolumn{3}{c}{$m = 8$, $K = 32,768$} & \multicolumn{3}{c}{$m = 16$, $K = 16,384$} & \multicolumn{3}{c}{$m = 32$, $K = 8,192$} & \multicolumn{3}{c}{$m = 64$, $K = 4,096$} & \multicolumn{3}{c}{$m = 128$, $K = 2,048$} & \multicolumn{3}{c}{$m = 256$, $K = 1,024$} & \multicolumn{3}{c}{$m = 512$, $K = 512$} & \multicolumn{3}{c}{$m = 1024$, $K = 256$} \\
8 − & gap & RMSE & cov & gap & RMSE & cov & gap & RMSE & cov & gap & RMSE & cov & gap & RMSE & cov & gap & RMSE & cov & gap & RMSE & cov & gap & RMSE & cov \\
7 +Estimator & \multicolumn{3}{c}{$m = 8$, $K = 32,768$} & \multicolumn{3}{c}{$m = 32$, $K = 8,192$} & \multicolumn{3}{c}{$m = 256$, $K = 1,024$} & \multicolumn{3}{c}{$m = 1024$, $K = 256$} \\
8 + & gap & RMSE & cov & gap & RMSE & cov & gap & RMSE & cov & gap & RMSE & cov \\
9 9 \midrule
10 −(1,0) plain average & 73.146 & 73.177 & 0.000 & 34.124 & 34.152 & 0.000 & 16.520 & 16.548 & 0.000 & 8.125 & 8.231 & 0.000 & 4.034 & 4.191 & 0.016 & 2.005 & 2.220 & 0.493 & 1.005 & 1.413 & 0.829 & 0.500 & 1.098 & 0.921 \\
11 −(1,1) average + 1 Newton & 10.451 & 10.474 & 0.000 & 2.275 & 2.476 & 0.371 & 0.533 & 1.143 & 0.906 & 0.129 & 1.002 & 0.950 & 0.032 & 0.978 & 0.958 & 0.008 & 0.995 & 0.954 & 0.002 & 0.996 & 0.949 & 4.96e-4 & 0.994 & 0.951 \\
12 −(1,2) average + 2 Newton & 0.213 & 1.008 & 0.952 & 0.010 & 1.007 & 0.948 & 5.57e-4 & 1.009 & 0.952 & 3.27e-5 & 0.999 & 0.947 & 2.00e-6 & 0.979 & 0.958 & 1.23e-7 & 0.994 & 0.956 & 7.96e-9 & 0.996 & 0.949 & 5.11e-10 & 0.994 & 0.950 \\
13 −(2,0) half-block jackknife & 24.405 & 24.402 & 0.000 & 4.921 & 5.013 & 0.006 & 1.159 & 1.540 & 0.777 & 0.380 & 1.055 & 0.934 & 0.195 & 0.997 & 0.949 & 0.129 & 1.006 & 0.954 & 0.090 & 0.996 & 0.947 & 0.063 & 0.998 & 0.949 \\
14 −(2,1) half-block + 1 Newton & 1.169 & 1.514 & 0.791 & 0.049 & 1.008 & 0.946 & 0.003 & 1.009 & 0.953 & 4.50e-4 & 0.999 & 0.947 & 1.25e-4 & 0.979 & 0.958 & 5.65e-5 & 0.994 & 0.956 & 2.85e-5 & 0.996 & 0.949 & 1.34e-5 & 0.994 & 0.950 \\
15 −(2,2) half-block + 2 Newton & 0.003 & 0.990 & -- & 5.25e-6 & 1.007 & -- & 3.19e-8 & 1.009 & -- & 1.12e-9 & 0.999 & -- & 1.09e-10 & 0.979 & -- & 2.30e-11 & 0.994 & -- & 7.79e-12 & 0.996 & -- & 1.14e-12 & 0.994 & -- \\
16 −(3,0) quarter-block & 24.625 & 24.669 & -- & 1.893 & 2.159 & -- & 0.590 & 1.169 & -- & 0.387 & 1.067 & -- & 0.265 & 1.018 & -- & 0.179 & 1.013 & -- & 0.127 & 0.998 & -- & 0.088 & 1.000 & -- \\
17 −(3,1) quarter-block + 1 Newton & 1.281 & 1.602 & 0.788 & 0.010 & 1.007 & 0.948 & 0.001 & 1.009 & 0.953 & 5.03e-4 & 0.999 & 0.947 & 2.37e-4 & 0.979 & 0.958 & 1.09e-4 & 0.994 & 0.956 & 5.74e-5 & 0.996 & 0.949 & 2.62e-5 & 0.994 & 0.950 \\
18 −analytic Cox--Snell + 1 Newton & 0.001 & 0.990 & 0.954 & 4.87e-4 & 1.007 & 0.948 & 2.25e-4 & 1.009 & 0.952 & 1.12e-4 & 0.999 & 0.947 & 5.57e-5 & 0.979 & 0.958 & 2.81e-5 & 0.994 & 0.956 & 1.39e-5 & 0.996 & 0.949 & 6.64e-6 & 0.994 & 0.950 \\
19 −CSL, 1 round & 7.57e-14 & 0.990 & -- & 7.61e-14 & 1.007 & -- & 7.25e-14 & 1.009 & -- & 7.88e-14 & 0.999 & -- & 7.24e-14 & 0.979 & -- & 7.73e-14 & 0.994 & -- & 7.51e-14 & 0.996 & -- & 7.73e-14 & 0.994 & -- \\
20 −CSL, 2 rounds & 7.88e-14 & 0.990 & -- & 7.20e-14 & 1.007 & -- & 7.59e-14 & 1.009 & -- & 7.71e-14 & 0.999 & -- & 7.24e-14 & 0.979 & -- & 7.58e-14 & 0.994 & -- & 7.63e-14 & 0.996 & -- & 8.10e-14 & 0.994 & -- \\
21 −AEE (Lin--Xi) & 56.889 & 56.874 & -- & 30.110 & 30.114 & -- & 15.518 & 15.553 & -- & 7.875 & 7.892 & -- & 3.972 & 4.051 & -- & 1.990 & 2.242 & -- & 1.001 & 1.413 & -- & 0.499 & 1.127 & -- \\
22 −(2,1) stale Hessian & 1.622 & 1.880 & -- & 0.243 & 1.033 & -- & 0.032 & 1.010 & -- & 0.005 & 0.999 & -- & 0.001 & 0.979 & -- & 4.86e-4 & 0.995 & -- & 1.74e-4 & 0.996 & -- & 6.30e-5 & 0.994 & -- \\
23 −centralized MLE & 0 & 0.990 & 0.954 & 0 & 1.007 & 0.948 & 0 & 1.009 & 0.953 & 0 & 0.999 & 0.947 & 0 & 0.979 & 0.958 & 0 & 0.994 & 0.956 & 0 & 0.996 & 0.949 & 0 & 0.994 & 0.950 \\
10 +(1,0) plain average & 73.146 & 73.177 & 0.000 & 16.520 & 16.548 & 0.000 & 2.005 & 2.220 & 0.493 & 0.500 & 1.098 & 0.921 \\
11 +(1,1) average + 1 Newton & 10.451 & 10.474 & 0.000 & 0.533 & 1.143 & 0.906 & 0.008 & 0.995 & 0.954 & 4.96e-4 & 0.994 & 0.951 \\
12 +(1,2) average + 2 Newton & 0.213 & 1.008 & 0.952 & 5.57e-4 & 1.009 & 0.952 & 1.23e-7 & 0.994 & 0.956 & 5.11e-10 & 0.994 & 0.950 \\
13 +(2,0) half-block jackknife & 24.405 & 24.402 & 0.000 & 1.159 & 1.540 & 0.777 & 0.129 & 1.006 & 0.954 & 0.063 & 0.998 & 0.949 \\
14 +(2,1) half-block + 1 Newton & 1.169 & 1.514 & 0.791 & 0.003 & 1.009 & 0.953 & 5.65e-5 & 0.994 & 0.956 & 1.34e-5 & 0.994 & 0.950 \\
15 +(2,2) half-block + 2 Newton & 0.003 & 0.990 & -- & 3.19e-8 & 1.009 & -- & 2.30e-11 & 0.994 & -- & 1.14e-12 & 0.994 & -- \\
16 +(3,0) quarter-block & 24.625 & 24.669 & -- & 0.590 & 1.169 & -- & 0.179 & 1.013 & -- & 0.088 & 1.000 & -- \\
17 +(3,1) quarter-block + 1 Newton & 1.281 & 1.602 & 0.788 & 0.001 & 1.009 & 0.953 & 1.09e-4 & 0.994 & 0.956 & 2.62e-5 & 0.994 & 0.950 \\
18 +analytic Cox--Snell + 1 Newton & 0.001 & 0.990 & 0.954 & 2.25e-4 & 1.009 & 0.952 & 2.81e-5 & 0.994 & 0.956 & 6.64e-6 & 0.994 & 0.950 \\
19 +CSL, 1 round & 7.57e-14 & 0.990 & -- & 7.25e-14 & 1.009 & -- & 7.73e-14 & 0.994 & -- & 7.73e-14 & 0.994 & -- \\
20 +CSL, 2 rounds & 7.88e-14 & 0.990 & -- & 7.59e-14 & 1.009 & -- & 7.58e-14 & 0.994 & -- & 8.10e-14 & 0.994 & -- \\
21 +AEE (Lin--Xi) & 56.889 & 56.874 & -- & 15.518 & 15.553 & -- & 1.990 & 2.242 & -- & 0.499 & 1.127 & -- \\
22 +(2,1) stale Hessian & 1.622 & 1.880 & -- & 0.032 & 1.010 & -- & 4.86e-4 & 0.995 & -- & 6.30e-5 & 0.994 & -- \\
23 +centralized MLE & 0 & 0.990 & 0.954 & 0 & 1.009 & 0.953 & 0 & 0.994 & 0.956 & 0 & 0.994 & 0.950 \\
24 24 \bottomrule
25 25 \end{tabular}
26 26 \begin{tablenotes}\footnotesize
added paper/tables/mc_logit_illcond_p8.tex +31 −0
@@ -0,0 +1,31 @@
1 +\begin{table}[p]
2 +\centering\scriptsize
3 +\begin{threeparttable}
4 +\caption{Monte Carlo, logit illcond design, $p = 8$, $N = 262,144$: scaled gap to $\widehat\theta_N$, scaled RMSE and 95\% coverage by local sample size $m$.}\label{tab:mc_logit_illcond_p8}
5 +\begin{tabular}{@{}lrrr@{}}
6 +\toprule
7 +Estimator & \multicolumn{3}{c}{$m = 128$, $K = 2,048$} \\
8 + & gap & RMSE & cov \\
9 +\midrule
10 +(1,0) plain average & 46.067 & 55.084 & 0.358 \\
11 +(1,1) average + 1 Newton & 1.044 & 28.518 & 0.949 \\
12 +(1,2) average + 2 Newton & 5.21e-4 & 28.540 & 0.952 \\
13 +(2,0) half-block jackknife & 80.014 & 84.567 & 0.681 \\
14 +(2,1) half-block + 1 Newton & 3.465 & 28.771 & 0.951 \\
15 +(2,2) half-block + 2 Newton & 0.035 & 28.540 & -- \\
16 +(3,0) quarter-block & 7.08e+4 & 7.08e+4 & -- \\
17 +(3,1) quarter-block + 1 Newton & 8.75e+9 & 8.75e+9 & 0.018 \\
18 +analytic Cox--Snell + 1 Newton & 0.004 & 28.540 & 0.952 \\
19 +CSL, 1 round & 32.901 & 43.974 & -- \\
20 +CSL, 2 rounds & 14.917 & 32.553 & -- \\
21 +AEE (Lin--Xi) & 39.134 & 47.574 & -- \\
22 +(2,1) stale Hessian & 9.812 & 30.265 & -- \\
23 +centralized MLE & 0 & 28.540 & 0.952 \\
24 +\bottomrule
25 +\end{tabular}
26 +\begin{tablenotes}\footnotesize
27 +\item 500 paired replications per column. gap $=$ root mean square of $\sqrt N\,\lVert\tilde\theta - \widehat\theta_N\rVert$; RMSE $=$ root mean square of $\sqrt N\,\lVert\tilde\theta - \theta_0\rVert$; cov $=$ empirical coverage of nominal 95\% Wald intervals (observed information at the estimate; `--' when not computed). Monte Carlo standard errors of the gaps and RMSEs are in \texttt{results/processed/summary.csv}; coverage standard error $\approx 0.010$.
28 +\item Fraction of workers with all local fits valid (orders 2 / 3): $m=128$: 1.000 / 0.946.
29 +\end{tablenotes}
30 +\end{threeparttable}
31 +\end{table}
added paper/tables/mc_logit_p20_p20.tex +1 −0
@@ -0,0 +1 @@
1 +% not yet generated
added paper/tables/mc_logit_small_m_p8.tex +1 −0
@@ -0,0 +1 @@
1 +% not yet generated
modified paper/tables/mc_pois_p8.tex +18 −18
@@ -2,30 +2,30 @@
2 2 \centering\scriptsize
3 3 \begin{threeparttable}
4 4 \caption{Monte Carlo, pois design, $p = 8$, $N = 262,144$: scaled gap to $\widehat\theta_N$, scaled RMSE and 95\% coverage by local sample size $m$.}\label{tab:mc_pois_p8}
5 −\begin{tabular}{@{}lrrrrrrrrr@{}}
5 +\begin{tabular}{@{}lrrrrrrrrrrrr@{}}
6 6 \toprule
7 −Estimator & \multicolumn{3}{c}{$m = 256$, $K = 1,024$} & \multicolumn{3}{c}{$m = 512$, $K = 512$} & \multicolumn{3}{c}{$m = 1024$, $K = 256$} \\
8 − & gap & RMSE & cov & gap & RMSE & cov & gap & RMSE & cov \\
7 +Estimator & \multicolumn{3}{c}{$m = 128$, $K = 2,048$} & \multicolumn{3}{c}{$m = 256$, $K = 1,024$} & \multicolumn{3}{c}{$m = 512$, $K = 512$} & \multicolumn{3}{c}{$m = 1024$, $K = 256$} \\
8 + & gap & RMSE & cov & gap & RMSE & cov & gap & RMSE & cov & gap & RMSE & cov \\
9 9 \midrule
10 −(1,0) plain average & 6.250 & 6.728 & 0.831 & 3.103 & 4.012 & 0.841 & 1.553 & 2.938 & 0.912 \\
11 −(1,1) average + 1 Newton & 0.039 & 2.516 & 0.951 & 0.010 & 2.545 & 0.946 & 0.002 & 2.511 & 0.949 \\
12 −(1,2) average + 2 Newton & 1.57e-6 & 2.516 & 0.951 & 1.02e-7 & 2.545 & 0.947 & 7.28e-9 & 2.511 & 0.949 \\
13 −(2,0) half-block jackknife & 0.924 & 2.672 & 0.935 & 0.575 & 2.606 & 0.939 & 0.399 & 2.543 & 0.944 \\
14 −(2,1) half-block + 1 Newton & 9.83e-4 & 2.516 & 0.951 & 4.08e-4 & 2.545 & 0.947 & 1.99e-4 & 2.511 & 0.949 \\
15 −(2,2) half-block + 2 Newton & 1.68e-9 & 2.516 & -- & 3.48e-10 & 2.545 & -- & 8.56e-11 & 2.511 & -- \\
16 −(3,0) quarter-block & 1.183 & 2.771 & -- & 0.806 & 2.664 & -- & 0.562 & 2.578 & -- \\
17 −(3,1) quarter-block + 1 Newton & 0.002 & 2.516 & 0.951 & 8.08e-4 & 2.545 & 0.947 & 3.98e-4 & 2.511 & 0.949 \\
18 −analytic Cox--Snell + 1 Newton & 3.91e-4 & 2.516 & 0.951 & 1.95e-4 & 2.545 & 0.947 & 9.82e-5 & 2.511 & 0.949 \\
19 −CSL, 1 round & 1.249 & 2.791 & -- & 0.432 & 2.586 & -- & 0.152 & 2.519 & -- \\
20 −CSL, 2 rounds & 0.494 & 2.563 & -- & 0.110 & 2.545 & -- & 0.026 & 2.511 & -- \\
21 −AEE (Lin--Xi) & 6.200 & 6.700 & -- & 3.072 & 3.992 & -- & 1.529 & 2.952 & -- \\
22 −(2,1) stale Hessian & 0.003 & 2.516 & -- & 0.001 & 2.545 & -- & 4.32e-4 & 2.511 & -- \\
23 −centralized MLE & 0 & 2.516 & 0.951 & 0 & 2.545 & 0.947 & 0 & 2.511 & 0.949 \\
10 +(1,0) plain average & 12.744 & 13.003 & 0.812 & 6.250 & 6.728 & 0.831 & 3.103 & 4.012 & 0.841 & 1.553 & 2.938 & 0.912 \\
11 +(1,1) average + 1 Newton & 0.162 & 2.487 & 0.955 & 0.039 & 2.516 & 0.951 & 0.010 & 2.545 & 0.946 & 0.002 & 2.511 & 0.949 \\
12 +(1,2) average + 2 Newton & 2.68e-5 & 2.483 & 0.956 & 1.57e-6 & 2.516 & 0.951 & 1.02e-7 & 2.545 & 0.947 & 7.28e-9 & 2.511 & 0.949 \\
13 +(2,0) half-block jackknife & 2.033 & 3.197 & 0.880 & 0.924 & 2.672 & 0.935 & 0.575 & 2.606 & 0.939 & 0.399 & 2.543 & 0.944 \\
14 +(2,1) half-block + 1 Newton & 0.004 & 2.483 & 0.956 & 9.83e-4 & 2.516 & 0.951 & 4.08e-4 & 2.545 & 0.947 & 1.99e-4 & 2.511 & 0.949 \\
15 +(2,2) half-block + 2 Newton & 2.63e-8 & 2.483 & -- & 1.68e-9 & 2.516 & -- & 3.48e-10 & 2.545 & -- & 8.56e-11 & 2.511 & -- \\
16 +(3,0) quarter-block & 1.785 & 3.080 & -- & 1.183 & 2.771 & -- & 0.806 & 2.664 & -- & 0.562 & 2.578 & -- \\
17 +(3,1) quarter-block + 1 Newton & 0.004 & 2.483 & 0.956 & 0.002 & 2.516 & 0.951 & 8.08e-4 & 2.545 & 0.947 & 3.98e-4 & 2.511 & 0.949 \\
18 +analytic Cox--Snell + 1 Newton & 8.35e-4 & 2.483 & 0.956 & 3.91e-4 & 2.516 & 0.951 & 1.95e-4 & 2.545 & 0.947 & 9.82e-5 & 2.511 & 0.949 \\
19 +CSL, 1 round & 3.756 & 4.479 & -- & 1.249 & 2.791 & -- & 0.432 & 2.586 & -- & 0.152 & 2.519 & -- \\
20 +CSL, 2 rounds & 2.652 & 3.645 & -- & 0.494 & 2.563 & -- & 0.110 & 2.545 & -- & 0.026 & 2.511 & -- \\
21 +AEE (Lin--Xi) & 12.596 & 12.820 & -- & 6.200 & 6.700 & -- & 3.072 & 3.992 & -- & 1.529 & 2.952 & -- \\
22 +(2,1) stale Hessian & 0.008 & 2.483 & -- & 0.003 & 2.516 & -- & 0.001 & 2.545 & -- & 4.32e-4 & 2.511 & -- \\
23 +centralized MLE & 0 & 2.483 & 0.956 & 0 & 2.516 & 0.951 & 0 & 2.545 & 0.947 & 0 & 2.511 & 0.949 \\
24 24 \bottomrule
25 25 \end{tabular}
26 26 \begin{tablenotes}\footnotesize
27 27 \item 1000 paired replications per column. gap $=$ root mean square of $\sqrt N\,\lVert\tilde\theta - \widehat\theta_N\rVert$; RMSE $=$ root mean square of $\sqrt N\,\lVert\tilde\theta - \theta_0\rVert$; cov $=$ empirical coverage of nominal 95\% Wald intervals (observed information at the estimate; `--' when not computed). Monte Carlo standard errors of the gaps and RMSEs are in \texttt{results/processed/summary.csv}; coverage standard error $\approx 0.007$.
28 −\item Fraction of workers with all local fits valid (orders 2 / 3): $m=256$: 1.000 / 1.000; $m=1024$: 1.000 / 1.000; $m=512$: 1.000 / 1.000.
28 +\item Fraction of workers with all local fits valid (orders 2 / 3): $m=256$: 1.000 / 1.000; $m=128$: 1.000 / 1.000; $m=1024$: 1.000 / 1.000; $m=512$: 1.000 / 1.000.
29 29 \end{tablenotes}
30 30 \end{threeparttable}
31 31 \end{table}
modified paper/tables/mc_rates.tex +1 −1
@@ -8,7 +8,7 @@ Design & $p$ & $N$ & r1\_q0 & r1\_q1 & r1\_q2 & r2\_q0 & r2\_q1 & r3\_q0 & r3\_q
8 8 predicted & & & -1 & -2 & -4 & -2 & -4 & -3 & -6 & -4 \\
9 9 \midrule
10 10 exp & 1 & 262,144 & -1.02 & -2.04 & -4.10 & -1.18 & -2.24 & -0.98 & -1.87 & -1.04 \\
11 −pois & 8 & 262,144 & -1.00 & -1.98 & -3.87 & -0.61 & -1.15 & -0.54 & -1.05 & -1.00 \\
11 +pois & 8 & 262,144 & -1.01 & -2.01 & -3.95 & -0.77 & -1.46 & -0.56 & -1.12 & -1.03 \\
12 12 \bottomrule
13 13 \end{tabular}
14 14 \begin{tablenotes}\footnotesize
modified paper/tables/numbers.tex +2 −2
@@ -1,6 +1,6 @@
1 1 % Auto-generated by experiments/make_tables.py
2 −\newcommand{\numMCcells}{49}
3 −\newcommand{\numManifest}{49}
2 +\newcommand{\numMCcells}{51}
3 +\newcommand{\numManifest}{52}
4 4 \newcommand{\numMCrepsExp}{2000}
5 5 \newcommand{\numMCrepsLogit}{1000}
6 6 \newcommand{\benchHosts}{0}
modified paper/tables/skew.tex +8 −4
@@ -38,10 +38,14 @@ logistic & 256 & 16 & 300 & equal & jack2 & 4.383 & 0.020 & 7.36e-7 \\
38 38 logistic & 256 & 16 & 300 & equal & raw & 8.944 & 0.083 & 1.02e-5 \\
39 39 logistic & 256 & 16 & 300 & weighted & jack2 & 0.526 & 3.29e-4 & 1.36e-9 \\
40 40 logistic & 256 & 16 & 300 & weighted & raw & 4.061 & 0.014 & 1.84e-7 \\
41 −logistic & 1024 & 4 & 240 & equal & jack2 & 3.625 & 0.015 & 3.66e-7 \\
42 −logistic & 1024 & 4 & 240 & equal & raw & 19.769 & 0.344 & 1.07e-4 \\
43 −logistic & 1024 & 4 & 240 & weighted & jack2 & 2.138 & 0.005 & 4.30e-8 \\
44 −logistic & 1024 & 4 & 240 & weighted & raw & 16.616 & 0.240 & 5.00e-5 \\
41 +logistic & 1024 & 4 & 300 & equal & jack2 & 3.611 & 0.014 & 3.54e-7 \\
42 +logistic & 1024 & 4 & 300 & equal & raw & 19.797 & 0.345 & 1.09e-4 \\
43 +logistic & 1024 & 4 & 300 & weighted & jack2 & 2.102 & 0.005 & 4.00e-8 \\
44 +logistic & 1024 & 4 & 300 & weighted & raw & 16.619 & 0.240 & 5.00e-5 \\
45 +logistic & 1024 & 16 & 300 & equal & jack2 & 1821.070 & 2.09e+6 & 1.81e+15 \\
46 +logistic & 1024 & 16 & 300 & equal & raw & 34.580 & 1.076 & 0.001 \\
47 +logistic & 1024 & 16 & 300 & weighted & jack2 & 372.695 & 1.35e+4 & 3.49e+8 \\
48 +logistic & 1024 & 16 & 300 & weighted & raw & 17.226 & 0.258 & 5.78e-5 \\
45 49 \bottomrule
46 50 \end{tabular}
47 51 \begin{tablenotes}\footnotesize
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modified results/processed/ablation_exponential_summary.csv +381 −127
@@ -376,130 +376,384 @@ N,m,variant,pattern,r,q,hessian,bits,rms,mean0,n,valid
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377 377 786432,48,pat1_q3_single,1,1,3,single,64,3.7519027260548054e-11,-3.735546699220651e-11,1000,
378 378 786432,48,pat1_q3_stale,1,1,3,stale,64,0.000334882920486108,0.00033453988160991573,1000,
379 −1572864,96,pat1-2-3-4_q0,1-2-3-4,4,0,exact,64,1.0033885259698323,0.05157913141748878,200,1.0
380 −1572864,96,pat1-2-3-4_q1,1-2-3-4,4,1,exact,64,0.0014202144516986125,-0.000802756544389786,200,1.0
381 −1572864,96,pat1-2-3-4_q2,1-2-3-4,4,2,exact,64,5.561541569528131e-09,-1.6082187914688319e-09,200,1.0
382 −1572864,96,pat1-2-3-4_q3,1-2-3-4,4,3,exact,64,1.4335364781935019e-13,-2.7847474288855425e-15,200,1.0
383 −1572864,96,pat1-2-3_q0,1-2-3,3,0,exact,64,0.4458187645235926,0.06561168563196455,200,1.0
384 −1572864,96,pat1-2-3_q1,1-2-3,3,1,exact,64,0.0002702812332041187,-0.000158477380747302,200,1.0
385 −1572864,96,pat1-2-3_q2,1-2-3,3,2,exact,64,1.7467885132797996e-10,-5.824577722256998e-11,200,1.0
386 −1572864,96,pat1-2-3_q3,1-2-3,3,3,exact,64,1.537927034686241e-13,2.7847474288855413e-15,200,1.0
387 −1572864,96,pat1-2-4-8_q0,1-2-4-8,4,0,exact,64,0.38702806624821595,0.0724006263517435,200,1.0
388 −1572864,96,pat1-2-4-8_q1,1-2-4-8,4,1,exact,64,0.00019783861243691123,-0.00011943761958857218,200,1.0
389 −1572864,96,pat1-2-4-8_q2,1-2-4-8,4,2,exact,64,8.243874438987699e-11,-3.119961400637638e-11,200,1.0
390 −1572864,96,pat1-2-4-8_q3,1-2-4-8,4,3,exact,64,1.5252689837551246e-13,9.746616001099394e-15,200,1.0
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395 −1572864,96,pat1-2-4_q1_Hbits2,1-2-4,3,1,quantizedH,2,0.00047373810965483217,6.918890390049704e-05,200,
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397 −1572864,96,pat1-2-4_q1_Hbits3,1-2-4,3,1,quantizedH,3,0.00047373810965483217,6.918890390049704e-05,200,
398 −1572864,96,pat1-2-4_q1_Hbits4,1-2-4,3,1,quantizedH,4,0.00047373810965483217,6.918890390049704e-05,200,
399 −1572864,96,pat1-2-4_q1_Hbits52,1-2-4,3,1,quantizedH,52,0.00013019947341526842,-7.941538160455175e-05,200,
400 −1572864,96,pat1-2-4_q1_Hbits6,1-2-4,3,1,quantizedH,6,0.00047373810965483217,6.918890390049704e-05,200,
401 −1572864,96,pat1-2-4_q1_Hbits8,1-2-4,3,1,quantizedH,8,0.00043279428797429975,-3.8397998901916246e-05,200,
402 −1572864,96,pat1-2-4_q1_bothbits12,1-2-4,3,1,quantizedBoth,12,0.00014377297203402667,-8.263002829692466e-05,200,
403 −1572864,96,pat1-2-4_q1_bothbits16,1-2-4,3,1,quantizedBoth,16,0.00012952417413678602,-7.93053343803298e-05,200,
404 −1572864,96,pat1-2-4_q1_bothbits2,1-2-4,3,1,quantizedBoth,2,0.03340790435060609,0.003729582518423993,200,
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406 −1572864,96,pat1-2-4_q1_bothbits3,1-2-4,3,1,quantizedBoth,3,0.017193550142416577,-0.0004206759813111777,200,
407 −1572864,96,pat1-2-4_q1_bothbits4,1-2-4,3,1,quantizedBoth,4,0.008228711914540249,9.960138104755508e-05,200,
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409 −1572864,96,pat1-2-4_q1_bothbits6,1-2-4,3,1,quantizedBoth,6,0.002316001949632785,3.643552311750455e-05,200,
410 −1572864,96,pat1-2-4_q1_bothbits8,1-2-4,3,1,quantizedBoth,8,0.0007255545715923543,4.021418842991267e-05,200,
411 −1572864,96,pat1-2-4_q1_gbits12,1-2-4,3,1,quantizedg,12,0.00013250501497750688,-8.009142274325908e-05,200,
412 −1572864,96,pat1-2-4_q1_gbits16,1-2-4,3,1,quantizedg,16,0.00012980027227848005,-7.93203328447984e-05,200,
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415 −1572864,96,pat1-2-4_q1_gbits3,1-2-4,3,1,quantizedg,3,0.017163773301913317,-0.0005664988972504842,200,
416 −1572864,96,pat1-2-4_q1_gbits4,1-2-4,3,1,quantizedg,4,0.008185233884679049,-4.895176367652143e-05,200,
417 −1572864,96,pat1-2-4_q1_gbits52,1-2-4,3,1,quantizedg,52,0.00013019947341526842,-7.941538160455175e-05,200,
418 −1572864,96,pat1-2-4_q1_gbits6,1-2-4,3,1,quantizedg,6,0.0022733568235961377,-0.0001124833019683328,200,
419 −1572864,96,pat1-2-4_q1_gbits8,1-2-4,3,1,quantizedg,8,0.0005469932582039279,-8.527460364573427e-07,200,
420 −1572864,96,pat1-2-4_q1_single,1-2-4,3,1,single,64,0.00013019947341526842,-7.941538160455175e-05,200,
421 −1572864,96,pat1-2-4_q1_stale,1-2-4,3,1,stale,64,0.0033177727033229187,0.0008193548488570263,200,
422 −1572864,96,pat1-2-4_q2,1-2-4,3,2,exact,64,3.4381194816229206e-11,-1.3522733514668189e-11,200,1.0
423 −1572864,96,pat1-2-4_q2_single,1-2-4,3,2,single,64,3.4381194816229206e-11,-1.3522733514668189e-11,200,
424 −1572864,96,pat1-2-4_q2_stale,1-2-4,3,2,stale,64,3.432233628387408e-05,8.624766025648093e-06,200,
425 −1572864,96,pat1-2-4_q3,1-2-4,3,3,exact,64,1.5060824466094994e-13,5.5694948577710826e-15,200,1.0
426 −1572864,96,pat1-2-4_q3_single,1-2-4,3,3,single,64,1.5060824466094994e-13,5.5694948577710826e-15,200,
427 −1572864,96,pat1-2-4_q3_stale,1-2-4,3,3,stale,64,3.5508870973618843e-07,9.067003055531822e-08,200,
428 −1572864,96,pat1-2_q0,1-2,2,0,exact,64,0.32275202501920325,-0.24400887177078898,200,1.0
429 −1572864,96,pat1-2_q1,1-2,2,1,exact,64,0.00012872529978105965,-8.30588283739578e-05,200,1.0
430 −1572864,96,pat1-2_q1_Hbits12,1-2,2,1,quantizedH,12,0.00012901740662731904,-8.040002648938526e-05,200,
431 −1572864,96,pat1-2_q1_Hbits16,1-2,2,1,quantizedH,16,0.0001287760636558924,-8.310663756109046e-05,200,
432 −1572864,96,pat1-2_q1_Hbits2,1-2,2,1,quantizedH,2,0.0005198462334860989,6.97062799378879e-05,200,
433 −1572864,96,pat1-2_q1_Hbits24,1-2,2,1,quantizedH,24,0.0001287270006927484,-8.305922051025495e-05,200,
434 −1572864,96,pat1-2_q1_Hbits3,1-2,2,1,quantizedH,3,0.0005198462334860989,6.97062799378879e-05,200,
435 −1572864,96,pat1-2_q1_Hbits4,1-2,2,1,quantizedH,4,0.0005198462334860989,6.97062799378879e-05,200,
436 −1572864,96,pat1-2_q1_Hbits52,1-2,2,1,quantizedH,52,0.00012872529978105965,-8.30588283739578e-05,200,
437 −1572864,96,pat1-2_q1_Hbits6,1-2,2,1,quantizedH,6,0.0005198462334860989,6.97062799378879e-05,200,
438 −1572864,96,pat1-2_q1_Hbits8,1-2,2,1,quantizedH,8,0.0004800970126231751,9.020467736765304e-05,200,
439 −1572864,96,pat1-2_q1_bothbits12,1-2,2,1,quantizedBoth,12,0.0001384610679661378,-8.109653317624083e-05,200,
440 −1572864,96,pat1-2_q1_bothbits16,1-2,2,1,quantizedBoth,16,0.00012868215158162426,-8.305919158647522e-05,200,
441 −1572864,96,pat1-2_q1_bothbits2,1-2,2,1,quantizedBoth,2,0.03369201416428462,-0.004311073356307374,200,
442 −1572864,96,pat1-2_q1_bothbits24,1-2,2,1,quantizedBoth,24,0.00012872629839211648,-8.305921934205514e-05,200,
443 −1572864,96,pat1-2_q1_bothbits3,1-2,2,1,quantizedBoth,3,0.017441597274721006,-0.0004896323921570679,200,
444 −1572864,96,pat1-2_q1_bothbits4,1-2,2,1,quantizedBoth,4,0.008153799329594176,-8.452059177878709e-05,200,
445 −1572864,96,pat1-2_q1_bothbits52,1-2,2,1,quantizedBoth,52,0.00012872529978105965,-8.30588283739578e-05,200,
446 −1572864,96,pat1-2_q1_bothbits6,1-2,2,1,quantizedBoth,6,0.0022192205440188296,-7.368148006298288e-05,200,
447 −1572864,96,pat1-2_q1_bothbits8,1-2,2,1,quantizedBoth,8,0.0006710706433711802,0.00010273147277383873,200,
448 −1572864,96,pat1-2_q1_gbits12,1-2,2,1,quantizedg,12,0.00013739910288517034,-8.375557504477455e-05,200,
449 −1572864,96,pat1-2_q1_gbits16,1-2,2,1,quantizedg,16,0.00012862667677442276,-8.301138323406787e-05,200,
450 −1572864,96,pat1-2_q1_gbits2,1-2,2,1,quantizedg,2,0.033776779160772444,-0.0044556509852573395,200,
451 −1572864,96,pat1-2_q1_gbits24,1-2,2,1,quantizedg,24,0.00012872459760442708,-8.305882720854274e-05,200,
452 −1572864,96,pat1-2_q1_gbits3,1-2,2,1,quantizedg,3,0.01742299470910743,-0.0006421244686534333,200,
453 −1572864,96,pat1-2_q1_gbits4,1-2,2,1,quantizedg,4,0.008200905281267531,-0.00023574075403911244,200,
454 −1572864,96,pat1-2_q1_gbits52,1-2,2,1,quantizedg,52,0.00012872529978105965,-8.30588283739578e-05,200,
455 −1572864,96,pat1-2_q1_gbits6,1-2,2,1,quantizedg,6,0.0021942950436127915,-0.0002263176447958203,200,
456 −1572864,96,pat1-2_q1_gbits8,1-2,2,1,quantizedg,8,0.0005352895046896114,-7.04846588556651e-05,200,
457 −1572864,96,pat1-2_q1_single,1-2,2,1,single,64,0.00012872529978105965,-8.30588283739578e-05,200,
458 −1572864,96,pat1-2_q1_stale,1-2,2,1,stale,64,0.0031894157505053854,-0.0024220620218652483,200,
459 −1572864,96,pat1-2_q2,1-2,2,2,exact,64,3.321136472128824e-11,-1.3220588418634107e-11,200,1.0
460 −1572864,96,pat1-2_q2_single,1-2,2,2,single,64,3.321136472128824e-11,-1.3220588418634107e-11,200,
461 −1572864,96,pat1-2_q2_stale,1-2,2,2,stale,64,3.26839528598533e-05,-2.4853796671434528e-05,200,
462 −1572864,96,pat1-2_q3,1-2,2,3,exact,64,1.569125486394371e-13,4.177121143328311e-15,200,1.0
463 −1572864,96,pat1-2_q3_single,1-2,2,3,single,64,1.569125486394371e-13,4.177121143328311e-15,200,
464 −1572864,96,pat1-2_q3_stale,1-2,2,3,stale,64,3.351238929895299e-07,-2.5514060274291926e-07,200,
465 −1572864,96,pat1-3_q0,1-3,2,0,exact,64,0.4370905705959195,-0.39881915047203975,200,1.0
466 −1572864,96,pat1-3_q1,1-3,2,1,exact,64,0.00019399329290125576,-0.00015233086292993646,200,1.0
467 −1572864,96,pat1-3_q2,1-3,2,2,exact,64,5.275146970009042e-11,-3.0002172611955604e-11,200,1.0
468 −1572864,96,pat1-3_q3,1-3,2,3,exact,64,1.6415843990891575e-13,-5.5694948577710826e-15,200,1.0
469 −1572864,96,pat1_q0,1,1,0,exact,64,13.222904223779382,13.222110671551855,200,1.0
470 −1572864,96,pat1_q1,1,1,1,exact,64,0.13944458967580223,-0.13941146509877655,200,1.0
471 −1572864,96,pat1_q1_Hbits12,1,1,1,quantizedH,12,0.13946805961741204,-0.13943022049734005,200,
472 −1572864,96,pat1_q1_Hbits16,1,1,1,quantizedH,16,0.13943646515412692,-0.13940334920566852,200,
473 −1572864,96,pat1_q1_Hbits2,1,1,1,quantizedH,2,0.14013371937098454,0.13849855287179613,200,
474 −1572864,96,pat1_q1_Hbits24,1,1,1,quantizedH,24,0.1394445914451559,-0.13941146650984915,200,
475 −1572864,96,pat1_q1_Hbits3,1,1,1,quantizedH,3,0.14013371937098454,0.13849855287179613,200,
476 −1572864,96,pat1_q1_Hbits4,1,1,1,quantizedH,4,0.14013371937098454,0.13849855287179613,200,
477 −1572864,96,pat1_q1_Hbits52,1,1,1,quantizedH,52,0.13944458967580223,-0.13941146509877655,200,
478 −1572864,96,pat1_q1_Hbits6,1,1,1,quantizedH,6,0.091415588806578,-0.08097239749050143,200,
479 −1572864,96,pat1_q1_Hbits8,1,1,1,quantizedH,8,0.1404244271775348,-0.1395282715617766,200,
480 −1572864,96,pat1_q1_bothbits12,1,1,1,quantizedBoth,12,0.13939161296729313,-0.13934489857049592,200,
481 −1572864,96,pat1_q1_bothbits16,1,1,1,quantizedBoth,16,0.13944254553680377,-0.13940951076191915,200,
482 −1572864,96,pat1_q1_bothbits2,1,1,1,quantizedBoth,2,1.4819252667794753,-1.4748277851472131,200,
483 −1572864,96,pat1_q1_bothbits24,1,1,1,quantizedBoth,24,0.13944462222898005,-0.13941149708954356,200,
484 −1572864,96,pat1_q1_bothbits3,1,1,1,quantizedBoth,3,0.9853686782823758,0.9746619576359649,200,
485 −1572864,96,pat1_q1_bothbits4,1,1,1,quantizedBoth,4,0.31002387061174275,-0.16435077275821286,200,
486 −1572864,96,pat1_q1_bothbits52,1,1,1,quantizedBoth,52,0.13944458967580223,-0.13941146509877655,200,
487 −1572864,96,pat1_q1_bothbits6,1,1,1,quantizedBoth,6,0.12938100209490971,-0.079157945938691,200,
488 −1572864,96,pat1_q1_bothbits8,1,1,1,quantizedBoth,8,0.14279283384708125,-0.1400331788227848,200,
489 −1572864,96,pat1_q1_gbits12,1,1,1,quantizedg,12,0.13936843816462097,-0.1393261464283868,200,
490 −1572864,96,pat1_q1_gbits16,1,1,1,quantizedg,16,0.13945066680138404,-0.13941762662559376,200,
491 −1572864,96,pat1_q1_gbits2,1,1,1,quantizedg,2,1.7924752607323675,-1.786974179064042,200,
492 −1572864,96,pat1_q1_gbits24,1,1,1,quantizedg,24,0.13944462045969147,-0.13941149567846192,200,
493 −1572864,96,pat1_q1_gbits3,1,1,1,quantizedg,3,0.7282798807180506,0.7145399627052711,200,
494 −1572864,96,pat1_q1_gbits4,1,1,1,quantizedg,4,0.5223760212256646,-0.44868523960100426,200,
495 −1572864,96,pat1_q1_gbits52,1,1,1,quantizedg,52,0.13944458967580223,-0.13941146509877655,200,
496 −1572864,96,pat1_q1_gbits6,1,1,1,quantizedg,6,0.16472851277777803,-0.13756908684207095,200,
497 −1572864,96,pat1_q1_gbits8,1,1,1,quantizedg,8,0.1417224457909359,-0.13991522670068238,200,
498 −1572864,96,pat1_q1_single,1,1,1,single,64,0.13944458967580226,-0.13941146509877655,200,
499 −1572864,96,pat1_q1_stale,1,1,1,stale,64,0.27311641471218,0.27305354912416463,200,
500 −1572864,96,pat1_q2,1,1,2,exact,64,1.5518767391080547e-05,-1.5504140223044486e-05,200,1.0
501 −1572864,96,pat1_q2_single,1,1,2,single,64,1.551876739114228e-05,-1.5504140223044486e-05,200,
502 −1572864,96,pat1_q2_stale,1,1,2,stale,64,0.002879989299924687,0.0028785043284346133,200,
503 −1572864,96,pat1_q3,1,1,3,exact,64,2.417683653918259e-13,-1.8866663830699544e-13,200,1.0
504 −1572864,96,pat1_q3_single,1,1,3,single,64,2.421689760483422e-13,-1.9005901202143819e-13,200,
505 −1572864,96,pat1_q3_stale,1,1,3,stale,64,2.9761423694967157e-05,2.973445994530665e-05,200,
379 +1572864,24,pat1-2-3-4_q0,1-2-3-4,4,0,exact,64,2.506237397131796,-0.10161615062337967,300,1.0
380 +1572864,24,pat1-2-3-4_q1,1-2-3-4,4,1,exact,64,0.008925751350556043,-0.005008473558086605,300,1.0
381 +1572864,24,pat1-2-3-4_q2,1-2-3-4,4,2,exact,64,2.098965624860839e-07,-6.352779053601836e-08,300,1.0
382 +1572864,24,pat1-2-3-4_q3,1-2-3-4,4,3,exact,64,1.550481749403145e-13,-3.7129965718473884e-15,300,1.0
383 +1572864,24,pat1-2-3_q0,1-2-3,3,0,exact,64,1.2439006380358595,0.7198221568915648,300,1.0
384 +1572864,24,pat1-2-3_q1,1-2-3,3,1,exact,64,0.00199501443617221,-0.0012336309649337276,300,1.0
385 +1572864,24,pat1-2-3_q2,1-2-3,3,2,exact,64,8.289993081475546e-09,-3.1730461126253407e-09,300,1.0
386 +1572864,24,pat1-2-3_q3,1-2-3,3,3,exact,64,1.58347441779189e-13,-3.7129965718473884e-15,300,1.0
387 +1572864,24,pat1-2-4-8_q0,1-2-4-8,4,0,exact,64,0.992634332678695,-0.4930839717149471,300,1.0
388 +1572864,24,pat1-2-4-8_q1,1-2-4-8,4,1,exact,64,0.0013389933930818988,-0.0007856670588276912,300,1.0
389 +1572864,24,pat1-2-4-8_q2,1-2-4-8,4,2,exact,64,4.05104952760096e-09,-1.4296522000241185e-09,300,1.0
390 +1572864,24,pat1-2-4-8_q3,1-2-4-8,4,3,exact,64,1.523149081432878e-13,-4.641245714809246e-16,300,1.0
391 +1572864,24,pat1-2-4_q0,1-2-4,3,0,exact,64,1.2035828589639017,0.9936349260629983,300,1.0
392 +1572864,24,pat1-2-4_q1,1-2-4,3,1,exact,64,0.0016368731942272316,-0.001154994606179629,300,1.0
393 +1572864,24,pat1-2-4_q1_Hbits12,1-2-4,3,1,quantizedH,12,0.001633832509833018,-0.0011526706287439902,300,
394 +1572864,24,pat1-2-4_q1_Hbits16,1-2-4,3,1,quantizedH,16,0.0016375313842372104,-0.001155577516672879,300,
395 +1572864,24,pat1-2-4_q1_Hbits2,1-2-4,3,1,quantizedH,2,0.0026302216973771624,0.0012750131259824903,300,
396 +1572864,24,pat1-2-4_q1_Hbits24,1-2-4,3,1,quantizedH,24,0.0016368744533565367,-0.00115499561681691,300,
397 +1572864,24,pat1-2-4_q1_Hbits3,1-2-4,3,1,quantizedH,3,0.0026302216973771624,0.0012750131259824903,300,
398 +1572864,24,pat1-2-4_q1_Hbits4,1-2-4,3,1,quantizedH,4,0.0026302216973771624,0.0012750131259824903,300,
399 +1572864,24,pat1-2-4_q1_Hbits52,1-2-4,3,1,quantizedH,52,0.0016368731942272316,-0.001154994606179629,300,
400 +1572864,24,pat1-2-4_q1_Hbits6,1-2-4,3,1,quantizedH,6,0.0026302216973771624,0.0012750131259824903,300,
401 +1572864,24,pat1-2-4_q1_Hbits8,1-2-4,3,1,quantizedH,8,0.002160260473412988,-0.001228229200149083,300,
402 +1572864,24,pat1-2-4_q1_bothbits12,1-2-4,3,1,quantizedBoth,12,0.001636485038503522,-0.001149718430573516,300,
403 +1572864,24,pat1-2-4_q1_bothbits16,1-2-4,3,1,quantizedBoth,16,0.0016380946091267472,-0.0011556024802218884,300,
404 +1572864,24,pat1-2-4_q1_bothbits2,1-2-4,3,1,quantizedBoth,2,0.12348546228597204,0.009286103679124429,300,
405 +1572864,24,pat1-2-4_q1_bothbits24,1-2-4,3,1,quantizedBoth,24,0.001636875070579593,-0.0011549970352753552,300,
406 +1572864,24,pat1-2-4_q1_bothbits3,1-2-4,3,1,quantizedBoth,3,0.0616425503786092,0.007324598221036333,300,
407 +1572864,24,pat1-2-4_q1_bothbits4,1-2-4,3,1,quantizedBoth,4,0.03270325250373997,0.00018224653777451665,300,
408 +1572864,24,pat1-2-4_q1_bothbits52,1-2-4,3,1,quantizedBoth,52,0.0016368731942272316,-0.001154994606179629,300,
409 +1572864,24,pat1-2-4_q1_bothbits6,1-2-4,3,1,quantizedBoth,6,0.008648618168991914,0.0012576857599681376,300,
410 +1572864,24,pat1-2-4_q1_bothbits8,1-2-4,3,1,quantizedBoth,8,0.0030042911215985607,-0.0011861903719096777,300,
411 +1572864,24,pat1-2-4_q1_gbits12,1-2-4,3,1,quantizedg,12,0.0016388424746873855,-0.0011520419091007664,300,
412 +1572864,24,pat1-2-4_q1_gbits16,1-2-4,3,1,quantizedg,16,0.0016374328646054754,-0.0011550195672989374,300,
413 +1572864,24,pat1-2-4_q1_gbits2,1-2-4,3,1,quantizedg,2,0.12369599215753695,0.006868862684026242,300,
414 +1572864,24,pat1-2-4_q1_gbits24,1-2-4,3,1,quantizedg,24,0.0016368738114633115,-0.0011549960246478158,300,
415 +1572864,24,pat1-2-4_q1_gbits3,1-2-4,3,1,quantizedg,3,0.06122956064008337,0.004911956013692643,300,
416 +1572864,24,pat1-2-4_q1_gbits4,1-2-4,3,1,quantizedg,4,0.032709586680132435,-0.00224786607102931,300,
417 +1572864,24,pat1-2-4_q1_gbits52,1-2-4,3,1,quantizedg,52,0.0016368731942272316,-0.001154994606179629,300,
418 +1572864,24,pat1-2-4_q1_gbits6,1-2-4,3,1,quantizedg,6,0.00870452382869856,-0.0011739298214866536,300,
419 +1572864,24,pat1-2-4_q1_gbits8,1-2-4,3,1,quantizedg,8,0.002642840487296873,-0.0011129725537135952,300,
420 +1572864,24,pat1-2-4_q1_single,1-2-4,3,1,single,64,0.0016368731942277607,-0.001154994606180557,300,
421 +1572864,24,pat1-2-4_q1_stale,1-2-4,3,1,stale,64,0.04970832856508817,0.040895950286797086,300,
422 +1572864,24,pat1-2-4_q2,1-2-4,3,2,exact,64,4.554219856382728e-09,-2.1362540126295354e-09,300,1.0
423 +1572864,24,pat1-2-4_q2_single,1-2-4,3,2,single,64,4.554219856382728e-09,-2.1362540126295354e-09,300,
424 +1572864,24,pat1-2-4_q2_stale,1-2-4,3,2,stale,64,0.001994568485437171,0.0016395867679180002,300,
425 +1572864,24,pat1-2-4_q3,1-2-4,3,3,exact,64,1.613792570262648e-13,-4.177121143328311e-15,300,1.0
426 +1572864,24,pat1-2-4_q3_single,1-2-4,3,3,single,64,1.613792570262648e-13,-4.177121143328311e-15,300,
427 +1572864,24,pat1-2-4_q3_stale,1-2-4,3,3,stale,64,7.994011307370949e-05,6.566311376026921e-05,300,
428 +1572864,24,pat1-2_q0,1-2,2,0,exact,64,4.980530586254331,-4.9593034238014475,300,1.0
429 +1572864,24,pat1-2_q1,1-2,2,1,exact,64,0.020111189435492994,-0.019778309209884073,300,1.0
430 +1572864,24,pat1-2_q1_Hbits12,1-2,2,1,quantizedH,12,0.020109830354212623,-0.019768701791557375,300,
431 +1572864,24,pat1-2_q1_Hbits16,1-2,2,1,quantizedH,16,0.020116616791591314,-0.019783192134374947,300,
432 +1572864,24,pat1-2_q1_Hbits2,1-2,2,1,quantizedH,2,0.021494342837321104,0.019480379996963983,300,
433 +1572864,24,pat1-2_q1_Hbits24,1-2,2,1,quantizedH,24,0.020111168656829305,-0.019778288935313207,300,
434 +1572864,24,pat1-2_q1_Hbits3,1-2,2,1,quantizedH,3,0.021494342837321104,0.019480379996963983,300,
435 +1572864,24,pat1-2_q1_Hbits4,1-2,2,1,quantizedH,4,0.021494342837321104,0.019480379996963983,300,
436 +1572864,24,pat1-2_q1_Hbits52,1-2,2,1,quantizedH,52,0.02011118943549258,-0.019778309209883608,300,
437 +1572864,24,pat1-2_q1_Hbits6,1-2,2,1,quantizedH,6,0.021494342837321104,0.019480379996963983,300,
438 +1572864,24,pat1-2_q1_Hbits8,1-2,2,1,quantizedH,8,0.02158651243806383,-0.019946232338128266,300,
439 +1572864,24,pat1-2_q1_bothbits12,1-2,2,1,quantizedBoth,12,0.02009969055894481,-0.019753331831877816,300,
440 +1572864,24,pat1-2_q1_bothbits16,1-2,2,1,quantizedBoth,16,0.020117870999170555,-0.019783983505930754,300,
441 +1572864,24,pat1-2_q1_bothbits2,1-2,2,1,quantizedBoth,2,0.4095045413140012,-0.11339621599540169,300,
442 +1572864,24,pat1-2_q1_bothbits24,1-2,2,1,quantizedBoth,24,0.020111161800836577,-0.019778284527486412,300,
443 +1572864,24,pat1-2_q1_bothbits3,1-2,2,1,quantizedBoth,3,0.2794043839445809,-0.0439940066165459,300,
444 +1572864,24,pat1-2_q1_bothbits4,1-2,2,1,quantizedBoth,4,0.14370798516041935,-0.009292901927116159,300,
445 +1572864,24,pat1-2_q1_bothbits52,1-2,2,1,quantizedBoth,52,0.02011118943549258,-0.019778309209883608,300,
446 +1572864,24,pat1-2_q1_bothbits6,1-2,2,1,quantizedBoth,6,0.04319761531699138,0.021070564676132702,300,
447 +1572864,24,pat1-2_q1_bothbits8,1-2,2,1,quantizedBoth,8,0.023006840193853698,-0.019190191882304617,300,
448 +1572864,24,pat1-2_q1_gbits12,1-2,2,1,quantizedg,12,0.02010050441220429,-0.019762941256555623,300,
449 +1572864,24,pat1-2_q1_gbits16,1-2,2,1,quantizedg,16,0.02011243983011233,-0.019779100597229864,300,
450 +1572864,24,pat1-2_q1_gbits2,1-2,2,1,quantizedg,2,0.4230125670470693,-0.15141057040641276,300,
451 +1572864,24,pat1-2_q1_gbits24,1-2,2,1,quantizedg,24,0.020111182579451083,-0.019778304802055887,300,
452 +1572864,24,pat1-2_q1_gbits3,1-2,2,1,quantizedg,3,0.2855672973973392,-0.08275900250353183,300,
453 +1572864,24,pat1-2_q1_gbits4,1-2,2,1,quantizedg,4,0.1502679626957104,-0.04833197971856513,300,
454 +1572864,24,pat1-2_q1_gbits52,1-2,2,1,quantizedg,52,0.020111189435492994,-0.019778309209884073,300,
455 +1572864,24,pat1-2_q1_gbits6,1-2,2,1,quantizedg,6,0.04087667927080119,-0.018199271105103654,300,
456 +1572864,24,pat1-2_q1_gbits8,1-2,2,1,quantizedg,8,0.021323984878528303,-0.01902350192244345,300,
457 +1572864,24,pat1-2_q1_single,1-2,2,1,single,64,0.020111189435492994,-0.019778309209884073,300,
458 +1572864,24,pat1-2_q1_stale,1-2,2,1,stale,64,0.17976043987734638,-0.17919195057829437,300,
459 +1572864,24,pat1-2_q2,1-2,2,2,exact,64,3.438786858824403e-07,-3.2248937933926745e-07,300,1.0
460 +1572864,24,pat1-2_q2_single,1-2,2,2,single,64,3.438786858824403e-07,-3.2248937933926745e-07,300,
461 +1572864,24,pat1-2_q2_stale,1-2,2,2,stale,64,0.0071602382476647995,-0.007139194868771714,300,
462 +1572864,24,pat1-2_q3,1-2,2,3,exact,64,1.5629356181383992e-13,-4.6412457148092355e-15,300,1.0
463 +1572864,24,pat1-2_q3_single,1-2,2,3,single,64,1.5629356181383992e-13,-4.6412457148092355e-15,300,
464 +1572864,24,pat1-2_q3_stale,1-2,2,3,stale,64,0.00028617863656500725,-0.00028538768558522234,300,
465 +1572864,24,pat1-3_q0,1-3,2,0,exact,64,7.8094035382055464,-7.798866214147959,300,1.0
466 +1572864,24,pat1-3_q1,1-3,2,1,exact,64,0.04888477000606261,-0.04862603830076428,300,1.0
467 +1572864,24,pat1-3_q2,1-3,2,2,exact,64,1.9451431969181835e-06,-1.9053567747090283e-06,300,1.0
468 +1572864,24,pat1-3_q3,1-3,2,3,exact,64,1.6217816912202473e-13,-6.961868572213853e-15,300,1.0
469 +1572864,24,pat1_q0,1,1,0,exact,64,54.526921666148674,54.52601984436218,300,1.0
470 +1572864,24,pat1_q1,1,1,1,exact,64,2.3707628087287738,-2.3706072144450125,300,1.0
471 +1572864,24,pat1_q1_Hbits12,1,1,1,quantizedH,12,2.3704073282210385,-2.370247108930741,300,
472 +1572864,24,pat1_q1_Hbits16,1,1,1,quantizedH,16,2.3707491820656097,-2.370593375734957,300,
473 +1572864,24,pat1_q1_Hbits2,1,1,1,quantizedH,2,2.2775901353068813,2.275784191011281,300,
474 +1572864,24,pat1_q1_Hbits24,1,1,1,quantizedH,24,2.370762960889578,-2.370607366370879,300,
475 +1572864,24,pat1_q1_Hbits3,1,1,1,quantizedH,3,5.1894274031268095,-5.188535188038842,300,
476 +1572864,24,pat1_q1_Hbits4,1,1,1,quantizedH,4,1.210985880880405,-1.2075648525454368,300,
477 +1572864,24,pat1_q1_Hbits52,1,1,1,quantizedH,52,2.370762808728774,-2.370607214445013,300,
478 +1572864,24,pat1_q1_Hbits6,1,1,1,quantizedH,6,2.154156722833742,-2.152201881306588,300,
479 +1572864,24,pat1_q1_Hbits8,1,1,1,quantizedH,8,2.371304005865206,-2.3702306310359815,300,
480 +1572864,24,pat1_q1_bothbits12,1,1,1,quantizedBoth,12,2.370340260413585,-2.3701729842723362,300,
481 +1572864,24,pat1_q1_bothbits16,1,1,1,quantizedBoth,16,2.370688171164398,-2.370532433188201,300,
482 +1572864,24,pat1_q1_bothbits2,1,1,1,quantizedBoth,2,4.2732567036825975,-4.261733982434089,300,
483 +1572864,24,pat1_q1_bothbits24,1,1,1,quantizedBoth,24,2.370763042333293,-2.370607447732458,300,
484 +1572864,24,pat1_q1_bothbits3,1,1,1,quantizedBoth,3,1.4955660191300146,-1.4623171335389813,300,
485 +1572864,24,pat1_q1_bothbits4,1,1,1,quantizedBoth,4,2.9654883887221133,-2.9379208592232047,300,
486 +1572864,24,pat1_q1_bothbits52,1,1,1,quantizedBoth,52,2.3707628087287715,-2.37060721444501,300,
487 +1572864,24,pat1_q1_bothbits6,1,1,1,quantizedBoth,6,2.3178385360005698,-2.2777856800307594,300,
488 +1572864,24,pat1_q1_bothbits8,1,1,1,quantizedBoth,8,2.378306286085904,-2.3751633129912593,300,
489 +1572864,24,pat1_q1_gbits12,1,1,1,quantizedg,12,2.370696188740035,-2.370533107408271,300,
490 +1572864,24,pat1_q1_gbits16,1,1,1,quantizedg,16,2.370701801744329,-2.3705462720416577,300,
491 +1572864,24,pat1_q1_gbits2,1,1,1,quantizedg,2,9.493876161965238,-9.489422582604668,300,
492 +1572864,24,pat1_q1_gbits24,1,1,1,quantizedg,24,2.370762890172489,-2.37060729580659,300,
493 +1572864,24,pat1_q1_gbits3,1,1,1,quantizedg,3,1.215389672488707,1.1798178218898123,300,
494 +1572864,24,pat1_q1_gbits4,1,1,1,quantizedg,4,4.155114384217499,-4.136999526829909,300,
495 +1572864,24,pat1_q1_gbits52,1,1,1,quantizedg,52,2.3707628087287698,-2.3706072144450085,300,
496 +1572864,24,pat1_q1_gbits6,1,1,1,quantizedg,6,2.533104325851676,-2.4967196709501764,300,
497 +1572864,24,pat1_q1_gbits8,1,1,1,quantizedg,8,2.377736814220149,-2.3755386528983915,300,
498 +1572864,24,pat1_q1_single,1,1,1,single,64,2.370762808728773,-2.3706072144450117,300,
499 +1572864,24,pat1_q1_stale,1,1,1,stale,64,4.361697298377499,4.361436143554114,300,
500 +1572864,24,pat1_q2,1,1,2,exact,64,0.004482582452413028,-0.004481403376296781,300,1.0
501 +1572864,24,pat1_q2_single,1,1,2,single,64,0.004482582452407307,-0.004481403376291212,300,
502 +1572864,24,pat1_q2_stale,1,1,2,stale,64,0.18895316954554037,0.18892705327799553,300,
503 +1572864,24,pat1_q3,1,1,3,exact,64,1.6038176499517502e-08,-1.602120101774658e-08,300,1.0
504 +1572864,24,pat1_q3_single,1,1,3,single,64,1.6038175542832063e-08,-1.6021200089497438e-08,300,
505 +1572864,24,pat1_q3_stale,1,1,3,stale,64,0.007584659755681129,0.00758282290222913,300,
506 +1572864,96,pat1-2-3-4_q0,1-2-3-4,4,0,exact,64,0.9711510993178339,-0.020289230681604528,1000,1.0
507 +1572864,96,pat1-2-3-4_q1,1-2-3-4,4,1,exact,64,0.001287706940754605,-0.0007520438795756156,1000,1.0
508 +1572864,96,pat1-2-3-4_q2,1-2-3-4,4,2,exact,64,4.166297494083303e-09,-1.322251685622861e-09,1000,1.0
509 +1572864,96,pat1-2-3-4_q3,1-2-3-4,4,3,exact,64,1.537296607508825e-13,1.2531363429984928e-15,1000,1.0
510 +1572864,96,pat1-2-3_q0,1-2-3,3,0,exact,64,0.4200259425872759,0.004401416378126407,1000,1.0
511 +1572864,96,pat1-2-3_q1,1-2-3,3,1,exact,64,0.00024352610892219934,-0.0001406755723975098,1000,1.0
512 +1572864,96,pat1-2-3_q2,1-2-3,3,2,exact,64,1.462830431944347e-10,-4.728403668087638e-11,1000,1.0
513 +1572864,96,pat1-2-3_q3,1-2-3,3,3,exact,64,1.6165932536617619e-13,5.012545371993974e-15,1000,1.0
514 +1572864,96,pat1-2-4-8_q0,1-2-4-8,4,0,exact,64,0.3650187166051463,-0.0016700854769381808,1000,1.0
515 +1572864,96,pat1-2-4-8_q1,1-2-4-8,4,1,exact,64,0.00018137501972092946,-0.00010624021705973986,1000,1.0
516 +1572864,96,pat1-2-4-8_q2,1-2-4-8,4,2,exact,64,8.137485139336512e-11,-2.623009298215869e-11,1000,1.0
517 +1572864,96,pat1-2-4-8_q3,1-2-4-8,4,3,exact,64,1.6039516676043039e-13,6.265681714992468e-15,1000,1.0
518 +1572864,96,pat1-2-4_q0,1-2-4,3,0,exact,64,0.2968680510980231,0.012631632064404444,1000,1.0
519 +1572864,96,pat1-2-4_q1,1-2-4,3,1,exact,64,0.00011956412786921071,-7.0273393931252e-05,1000,1.0
520 +1572864,96,pat1-2-4_q1_Hbits12,1-2-4,3,1,quantizedH,12,0.0001267580269615221,-7.017795889068718e-05,1000,
521 +1572864,96,pat1-2-4_q1_Hbits16,1-2-4,3,1,quantizedH,16,0.00011940772770541613,-7.021422326101813e-05,1000,
522 +1572864,96,pat1-2-4_q1_Hbits2,1-2-4,3,1,quantizedH,2,0.00048183350097114345,6.416211546144672e-05,1000,
523 +1572864,96,pat1-2-4_q1_Hbits24,1-2-4,3,1,quantizedH,24,0.00011956495785168157,-7.027350934998226e-05,1000,
524 +1572864,96,pat1-2-4_q1_Hbits3,1-2-4,3,1,quantizedH,3,0.00048183350097114345,6.416211546144672e-05,1000,
525 +1572864,96,pat1-2-4_q1_Hbits4,1-2-4,3,1,quantizedH,4,0.00048183350097114345,6.416211546144672e-05,1000,
526 +1572864,96,pat1-2-4_q1_Hbits52,1-2-4,3,1,quantizedH,52,0.00011956412786921071,-7.0273393931252e-05,1000,
527 +1572864,96,pat1-2-4_q1_Hbits6,1-2-4,3,1,quantizedH,6,0.00048183350097114345,6.416211546144672e-05,1000,
528 +1572864,96,pat1-2-4_q1_Hbits8,1-2-4,3,1,quantizedH,8,0.0004345686031415878,-1.98905672795734e-05,1000,
529 +1572864,96,pat1-2-4_q1_bothbits12,1-2-4,3,1,quantizedBoth,12,0.00013133259293956746,-7.046114240602861e-05,1000,
530 +1572864,96,pat1-2-4_q1_bothbits16,1-2-4,3,1,quantizedBoth,16,0.00011943761382046378,-7.026236955157592e-05,1000,
531 +1572864,96,pat1-2-4_q1_bothbits2,1-2-4,3,1,quantizedBoth,2,0.030847254781660907,0.0018472391746382967,1000,
532 +1572864,96,pat1-2-4_q1_bothbits24,1-2-4,3,1,quantizedBoth,24,0.00011956384840451066,-7.027285091385734e-05,1000,
533 +1572864,96,pat1-2-4_q1_bothbits3,1-2-4,3,1,quantizedBoth,3,0.015956372694485015,-0.0009832640330790017,1000,
534 +1572864,96,pat1-2-4_q1_bothbits4,1-2-4,3,1,quantizedBoth,4,0.008030490439959263,0.00034286390910225723,1000,
535 +1572864,96,pat1-2-4_q1_bothbits52,1-2-4,3,1,quantizedBoth,52,0.00011956412786921071,-7.0273393931252e-05,1000,
536 +1572864,96,pat1-2-4_q1_bothbits6,1-2-4,3,1,quantizedBoth,6,0.002028695018868157,4.5802167625746054e-05,1000,
537 +1572864,96,pat1-2-4_q1_bothbits8,1-2-4,3,1,quantizedBoth,8,0.0006555695871024251,-3.248244976507801e-05,1000,
538 +1572864,96,pat1-2-4_q1_gbits12,1-2-4,3,1,quantizedg,12,0.0001242513577701483,-7.055665758006574e-05,1000,
539 +1572864,96,pat1-2-4_q1_gbits16,1-2-4,3,1,quantizedg,16,0.00011959415200870047,-7.032154030075832e-05,1000,
540 +1572864,96,pat1-2-4_q1_gbits2,1-2-4,3,1,quantizedg,2,0.030851476101100003,0.0017127436026295165,1000,
541 +1572864,96,pat1-2-4_q1_gbits24,1-2-4,3,1,quantizedg,24,0.00011956301845173204,-7.027273549763274e-05,1000,
542 +1572864,96,pat1-2-4_q1_gbits3,1-2-4,3,1,quantizedg,3,0.015908931984538938,-0.0011159194503077316,1000,
543 +1572864,96,pat1-2-4_q1_gbits4,1-2-4,3,1,quantizedg,4,0.008021614311472124,0.00020799691694663356,1000,
544 +1572864,96,pat1-2-4_q1_gbits52,1-2-4,3,1,quantizedg,52,0.00011956412786921071,-7.0273393931252e-05,1000,
545 +1572864,96,pat1-2-4_q1_gbits6,1-2-4,3,1,quantizedg,6,0.00198951608327376,-8.856766830268444e-05,1000,
546 +1572864,96,pat1-2-4_q1_gbits8,1-2-4,3,1,quantizedg,8,0.0005098301743333078,-8.286604202904732e-05,1000,
547 +1572864,96,pat1-2-4_q1_single,1-2-4,3,1,single,64,0.00011956412786921071,-7.0273393931252e-05,1000,
548 +1572864,96,pat1-2-4_q1_stale,1-2-4,3,1,stale,64,0.0030746347703212167,0.00021544594678166493,1000,
549 +1572864,96,pat1-2-4_q2,1-2-4,3,2,exact,64,3.454616820700147e-11,-1.1402009110200404e-11,1000,1.0
550 +1572864,96,pat1-2-4_q2_single,1-2-4,3,2,single,64,3.454616820700147e-11,-1.1402009110200404e-11,1000,
551 +1572864,96,pat1-2-4_q2_stale,1-2-4,3,2,stale,64,3.174479765915376e-05,2.3901239357703908e-06,1000,
552 +1572864,96,pat1-2-4_q3,1-2-4,3,3,exact,64,1.6045559063402243e-13,4.455595886216865e-15,1000,1.0
553 +1572864,96,pat1-2-4_q3_single,1-2-4,3,3,single,64,1.6045559063402243e-13,4.455595886216865e-15,1000,
554 +1572864,96,pat1-2-4_q3_stale,1-2-4,3,3,stale,64,3.2784227450706504e-07,2.6306533881369484e-08,1000,
555 +1572864,96,pat1-2_q0,1-2,2,0,exact,64,0.3474668564112049,-0.2792789454854114,1000,1.0
556 +1572864,96,pat1-2_q1,1-2,2,1,exact,64,0.00014236134958311856,-9.626846920218369e-05,1000,1.0
557 +1572864,96,pat1-2_q1_Hbits12,1-2,2,1,quantizedH,12,0.00014709087524048117,-9.615613195066104e-05,1000,
558 +1572864,96,pat1-2_q1_Hbits16,1-2,2,1,quantizedH,16,0.00014239103114560824,-9.628065562326491e-05,1000,
559 +1572864,96,pat1-2_q1_Hbits2,1-2,2,1,quantizedH,2,0.0005528821558290938,9.433686885822975e-05,1000,
560 +1572864,96,pat1-2_q1_Hbits24,1-2,2,1,quantizedH,24,0.0001423616227890254,-9.626851419660077e-05,1000,
561 +1572864,96,pat1-2_q1_Hbits3,1-2,2,1,quantizedH,3,0.0005528821558290938,9.433686885822975e-05,1000,
562 +1572864,96,pat1-2_q1_Hbits4,1-2,2,1,quantizedH,4,0.0005528821558290938,9.433686885822975e-05,1000,
563 +1572864,96,pat1-2_q1_Hbits52,1-2,2,1,quantizedH,52,0.00014236134958311856,-9.626846920218369e-05,1000,
564 +1572864,96,pat1-2_q1_Hbits6,1-2,2,1,quantizedH,6,0.0005528821558290938,9.433686885822975e-05,1000,
565 +1572864,96,pat1-2_q1_Hbits8,1-2,2,1,quantizedH,8,0.0005249861177881321,0.00010015061412643706,1000,
566 +1572864,96,pat1-2_q1_bothbits12,1-2,2,1,quantizedBoth,12,0.0001535137821282012,-9.661827563628994e-05,1000,
567 +1572864,96,pat1-2_q1_bothbits16,1-2,2,1,quantizedBoth,16,0.00014230970165348994,-9.629242626262087e-05,1000,
568 +1572864,96,pat1-2_q1_bothbits2,1-2,2,1,quantizedBoth,2,0.035343917070410395,-0.0033242436290606826,1000,
569 +1572864,96,pat1-2_q1_bothbits24,1-2,2,1,quantizedBoth,24,0.00014236183104216307,-9.626887514971467e-05,1000,
570 +1572864,96,pat1-2_q1_bothbits3,1-2,2,1,quantizedBoth,3,0.018461910993590885,-0.0004814623241064089,1000,
571 +1572864,96,pat1-2_q1_bothbits4,1-2,2,1,quantizedBoth,4,0.009330622007208467,0.00033602161268752044,1000,
572 +1572864,96,pat1-2_q1_bothbits52,1-2,2,1,quantizedBoth,52,0.00014236134958311856,-9.626846920218369e-05,1000,
573 +1572864,96,pat1-2_q1_bothbits6,1-2,2,1,quantizedBoth,6,0.002356663822129998,0.00011517471147809132,1000,
574 +1572864,96,pat1-2_q1_bothbits8,1-2,2,1,quantizedBoth,8,0.000753185924122037,0.00012219733770409803,1000,
575 +1572864,96,pat1-2_q1_gbits12,1-2,2,1,quantizedg,12,0.00014891186948939967,-9.673065667852227e-05,1000,
576 +1572864,96,pat1-2_q1_gbits16,1-2,2,1,quantizedg,16,0.0001422777560066206,-9.62802405570791e-05,1000,
577 +1572864,96,pat1-2_q1_gbits2,1-2,2,1,quantizedg,2,0.03538306313150503,-0.0035106502828536074,1000,
578 +1572864,96,pat1-2_q1_gbits24,1-2,2,1,quantizedg,24,0.00014236155788198955,-9.626883016003205e-05,1000,
579 +1572864,96,pat1-2_q1_gbits3,1-2,2,1,quantizedg,3,0.018453099857197782,-0.000671752890130253,1000,
580 +1572864,96,pat1-2_q1_gbits4,1-2,2,1,quantizedg,4,0.009296551219466988,0.00014554357369677245,1000,
581 +1572864,96,pat1-2_q1_gbits52,1-2,2,1,quantizedg,52,0.00014236134958311856,-9.626846920218369e-05,1000,
582 +1572864,96,pat1-2_q1_gbits6,1-2,2,1,quantizedg,6,0.002253772817971647,-7.559004772336904e-05,1000,
583 +1572864,96,pat1-2_q1_gbits8,1-2,2,1,quantizedg,8,0.0005699327774123584,-7.421060474035935e-05,1000,
584 +1572864,96,pat1-2_q1_single,1-2,2,1,single,64,0.00014236134958311856,-9.626846920218369e-05,1000,
585 +1572864,96,pat1-2_q1_stale,1-2,2,1,stale,64,0.003425768141621282,-0.0027689403118757046,1000,
586 +1572864,96,pat1-2_q2,1-2,2,2,exact,64,3.919103784567087e-11,-1.6162952551994567e-11,1000,1.0
587 +1572864,96,pat1-2_q2_single,1-2,2,2,single,64,3.919103784567087e-11,-1.6162952551994567e-11,1000,
588 +1572864,96,pat1-2_q2_stale,1-2,2,2,stale,64,3.5072061974227e-05,-2.839367899376301e-05,1000,
589 +1572864,96,pat1-2_q3,1-2,2,3,exact,64,1.610585825772104e-13,1.3923737144427708e-15,1000,1.0
590 +1572864,96,pat1-2_q3_single,1-2,2,3,single,64,1.610585825772104e-13,1.3923737144427708e-15,1000,
591 +1572864,96,pat1-2_q3_stale,1-2,2,3,stale,64,3.5927323375601427e-07,-2.9128253580367845e-07,1000,
592 +1572864,96,pat1-3_q0,1-3,2,0,exact,64,0.45801145978441277,-0.42111912641708693,1000,1.0
593 +1572864,96,pat1-3_q1,1-3,2,1,exact,64,0.00021048795129835257,-0.00016726825327090364,1000,1.0
594 +1572864,96,pat1-3_q2,1-3,2,2,exact,64,6.453193372758523e-11,-3.532841978181353e-11,1000,1.0
595 +1572864,96,pat1-3_q3,1-3,2,3,exact,64,1.5924275698938008e-13,-5.569494857771088e-16,1000,1.0
596 +1572864,96,pat1_q0,1,1,0,exact,64,13.203515250800521,13.20269112925226,1000,1.0
597 +1572864,96,pat1_q1,1,1,1,exact,64,0.13904048751597747,-0.13900602746476454,1000,1.0
598 +1572864,96,pat1_q1_Hbits12,1,1,1,quantizedH,12,0.13903932015598283,-0.13900051636226196,1000,
599 +1572864,96,pat1_q1_Hbits16,1,1,1,quantizedH,16,0.13903677038382536,-0.1390023077086211,1000,
600 +1572864,96,pat1_q1_Hbits2,1,1,1,quantizedH,2,0.13907420850798483,0.13752244756738363,1000,
601 +1572864,96,pat1_q1_Hbits24,1,1,1,quantizedH,24,0.13904049842094787,-0.13900603833900763,1000,
602 +1572864,96,pat1_q1_Hbits3,1,1,1,quantizedH,3,0.13907420850798483,0.13752244756738363,1000,
603 +1572864,96,pat1_q1_Hbits4,1,1,1,quantizedH,4,0.13907420850798483,0.13752244756738363,1000,
604 +1572864,96,pat1_q1_Hbits52,1,1,1,quantizedH,52,0.1390404875159776,-0.13900602746476468,1000,
605 +1572864,96,pat1_q1_Hbits6,1,1,1,quantizedH,6,0.08594323529025145,-0.07759807129806293,1000,
606 +1572864,96,pat1_q1_Hbits8,1,1,1,quantizedH,8,0.14079698833027704,-0.1398859338250102,1000,
607 +1572864,96,pat1_q1_bothbits12,1,1,1,quantizedBoth,12,0.13907084740482442,-0.13902307257994168,1000,
608 +1572864,96,pat1_q1_bothbits16,1,1,1,quantizedBoth,16,0.13903666505649231,-0.139002274282807,1000,
609 +1572864,96,pat1_q1_bothbits2,1,1,1,quantizedBoth,2,1.501511571473066,-1.4942473274468064,1000,
610 +1572864,96,pat1_q1_bothbits24,1,1,1,quantizedBoth,24,0.13904050830411965,-0.13900604793957727,1000,
611 +1572864,96,pat1_q1_bothbits3,1,1,1,quantizedBoth,3,0.9658548899099632,0.9503434358508053,1000,
612 +1572864,96,pat1_q1_bothbits4,1,1,1,quantizedBoth,4,0.32522583165287283,-0.17642184582945664,1000,
613 +1572864,96,pat1_q1_bothbits52,1,1,1,quantizedBoth,52,0.1390404875159779,-0.13900602746476498,1000,
614 +1572864,96,pat1_q1_bothbits6,1,1,1,quantizedBoth,6,0.12547196729473972,-0.07770728377340748,1000,
615 +1572864,96,pat1_q1_bothbits8,1,1,1,quantizedBoth,8,0.14219270553769897,-0.13936876700653422,1000,
616 +1572864,96,pat1_q1_gbits12,1,1,1,quantizedg,12,0.13907186375062683,-0.13902858215697786,1000,
617 +1572864,96,pat1_q1_gbits16,1,1,1,quantizedg,16,0.13904038171571922,-0.13900599403435732,1000,
618 +1572864,96,pat1_q1_gbits2,1,1,1,quantizedg,2,1.8109846182255471,-1.8052848187254076,1000,
619 +1572864,96,pat1_q1_gbits24,1,1,1,quantizedg,24,0.13904049739917268,-0.13900603706532763,1000,
620 +1572864,96,pat1_q1_gbits3,1,1,1,quantizedg,3,0.7116462857862679,0.6910495064960944,1000,
621 +1572864,96,pat1_q1_gbits4,1,1,1,quantizedg,4,0.5371965487023089,-0.4595856718009744,1000,
622 +1572864,96,pat1_q1_gbits52,1,1,1,quantizedg,52,0.13904048751597778,-0.13900602746476481,1000,
623 +1572864,96,pat1_q1_gbits6,1,1,1,quantizedg,6,0.16610170519269424,-0.13910873015059017,1000,
624 +1572864,96,pat1_q1_gbits8,1,1,1,quantizedg,8,0.14035170451588835,-0.13848779144956797,1000,
625 +1572864,96,pat1_q1_single,1,1,1,single,64,0.13904048751597792,-0.13900602746476498,1000,
626 +1572864,96,pat1_q1_stale,1,1,1,stale,64,0.27236068737671093,0.2722949789747664,1000,
627 +1572864,96,pat1_q2,1,1,2,exact,64,1.5429976836109835e-05,-1.5414762013986496e-05,1000,1.0
628 +1572864,96,pat1_q2_single,1,1,2,single,64,1.5429976836560626e-05,-1.5414762014404208e-05,1000,
629 +1572864,96,pat1_q2_stale,1,1,2,stale,64,0.002868526711561063,0.0028669680717917896,1000,
630 +1572864,96,pat1_q3,1,1,3,exact,64,2.418084563240958e-13,-1.876919767068855e-13,1000,1.0
631 +1572864,96,pat1_q3_single,1,1,3,single,64,2.422490186801919e-13,-1.8838816356410686e-13,1000,
632 +1572864,96,pat1_q3_stale,1,1,3,stale,64,2.960823209018773e-05,2.95798852618859e-05,1000,
633 +3145728,192,pat1-2-3-4_q0,1-2-3-4,4,0,exact,64,0.678981902366999,-0.014158333169290578,1000,1.0
634 +3145728,192,pat1-2-3-4_q1,1-2-3-4,4,1,exact,64,0.0004779108408131412,-0.0002599246489153509,1000,1.0
635 +3145728,192,pat1-2-3-4_q2,1-2-3-4,4,2,exact,64,4.57453085701219e-10,-1.2877531604893464e-10,1000,1.0
636 +3145728,192,pat1-2-3-4_q3,1-2-3-4,4,3,exact,64,2.2006553552422866e-13,-9.845568954283855e-16,1000,1.0
637 +3145728,192,pat1-2-3_q0,1-2-3,3,0,exact,64,0.29664960090882947,-0.006651070120280215,1000,1.0
638 +3145728,192,pat1-2-3_q1,1-2-3,3,1,exact,64,8.734992146238528e-05,-4.961487640543948e-05,1000,1.0
639 +3145728,192,pat1-2-3_q2,1-2-3,3,2,exact,64,1.3860226764021003e-11,-4.301135253368441e-12,1000,1.0
640 +3145728,192,pat1-2-3_q3,1-2-3,3,3,exact,64,2.2580507068188585e-13,-2.1660251699424466e-15,1000,1.0
641 +3145728,192,pat1-2-4-8_q0,1-2-4-8,4,0,exact,64,0.2555999858022574,-0.005837242413203357,1000,1.0
642 +3145728,192,pat1-2-4-8_q1,1-2-4-8,4,1,exact,64,6.223503621169021e-05,-3.683342944709451e-05,1000,1.0
643 +3145728,192,pat1-2-4-8_q2,1-2-4-8,4,2,exact,64,6.9388507849705044e-12,-2.1735078023477018e-12,1000,1.0
644 +3145728,192,pat1-2-4-8_q3,1-2-4-8,4,3,exact,64,2.2173305495261767e-13,3.9382275817135357e-16,1000,1.0
645 +3145728,192,pat1-2-4_q0,1-2-4,3,0,exact,64,0.2080811796331893,-0.004148649104195541,1000,1.0
646 +3145728,192,pat1-2-4_q1,1-2-4,3,1,exact,64,4.1221187069529555e-05,-2.4411088589911252e-05,1000,1.0
647 +3145728,192,pat1-2-4_q1_Hbits12,1-2-4,3,1,quantizedH,12,4.895924724243146e-05,-2.4855056054373127e-05,1000,
648 +3145728,192,pat1-2-4_q1_Hbits16,1-2-4,3,1,quantizedH,16,4.120297602491809e-05,-2.4394934570711496e-05,1000,
649 +3145728,192,pat1-2-4_q1_Hbits2,1-2-4,3,1,quantizedH,2,0.00022663815806375185,1.804128419233731e-05,1000,
650 +3145728,192,pat1-2-4_q1_Hbits24,1-2-4,3,1,quantizedH,24,4.1221313543225065e-05,-2.441122832826866e-05,1000,
651 +3145728,192,pat1-2-4_q1_Hbits3,1-2-4,3,1,quantizedH,3,0.00022663815806375185,1.804128419233731e-05,1000,
652 +3145728,192,pat1-2-4_q1_Hbits4,1-2-4,3,1,quantizedH,4,0.00022663815806375185,1.804128419233731e-05,1000,
653 +3145728,192,pat1-2-4_q1_Hbits52,1-2-4,3,1,quantizedH,52,4.1221187069529555e-05,-2.4411088589911252e-05,1000,
654 +3145728,192,pat1-2-4_q1_Hbits6,1-2-4,3,1,quantizedH,6,0.00022663815806375185,1.804128419233731e-05,1000,
655 +3145728,192,pat1-2-4_q1_Hbits8,1-2-4,3,1,quantizedH,8,0.00021667632044963694,5.631659627050183e-07,1000,
656 +3145728,192,pat1-2-4_q1_bothbits12,1-2-4,3,1,quantizedBoth,12,5.313309979364758e-05,-2.4393762471281447e-05,1000,
657 +3145728,192,pat1-2-4_q1_bothbits16,1-2-4,3,1,quantizedBoth,16,4.139528075260808e-05,-2.4455647404583246e-05,1000,
658 +3145728,192,pat1-2-4_q1_bothbits2,1-2-4,3,1,quantizedBoth,2,0.021581561601775878,-0.00019931108727993602,1000,
659 +3145728,192,pat1-2-4_q1_bothbits24,1-2-4,3,1,quantizedBoth,24,4.1221133373436614e-05,-2.441123882364498e-05,1000,
660 +3145728,192,pat1-2-4_q1_bothbits3,1-2-4,3,1,quantizedBoth,3,0.011042640128609208,-0.0003536564351219825,1000,
661 +3145728,192,pat1-2-4_q1_bothbits4,1-2-4,3,1,quantizedBoth,4,0.005543705554891457,0.0001743420852883398,1000,
662 +3145728,192,pat1-2-4_q1_bothbits52,1-2-4,3,1,quantizedBoth,52,4.1221187069529555e-05,-2.4411088589911252e-05,1000,
663 +3145728,192,pat1-2-4_q1_bothbits6,1-2-4,3,1,quantizedBoth,6,0.0014140395363432205,-9.482434042965256e-07,1000,
664 +3145728,192,pat1-2-4_q1_bothbits8,1-2-4,3,1,quantizedBoth,8,0.0004332725416333434,1.0237363569752307e-05,1000,
665 +3145728,192,pat1-2-4_q1_gbits12,1-2-4,3,1,quantizedg,12,4.562213400381399e-05,-2.394959170330358e-05,1000,
666 +3145728,192,pat1-2-4_q1_gbits16,1-2-4,3,1,quantizedg,16,4.141525996997566e-05,-2.447180122785627e-05,1000,
667 +3145728,192,pat1-2-4_q1_gbits2,1-2-4,3,1,quantizedg,2,0.021592709630996913,-0.0002421943763485518,1000,
668 +3145728,192,pat1-2-4_q1_gbits24,1-2-4,3,1,quantizedg,24,4.1221006892824186e-05,-2.441109908233417e-05,1000,
669 +3145728,192,pat1-2-4_q1_gbits3,1-2-4,3,1,quantizedg,3,0.011044219567205355,-0.00039549586466478535,1000,
670 +3145728,192,pat1-2-4_q1_gbits4,1-2-4,3,1,quantizedg,4,0.005511740584128828,0.00013179259379105164,1000,
671 +3145728,192,pat1-2-4_q1_gbits52,1-2-4,3,1,quantizedg,52,4.1221187069529555e-05,-2.4411088589911252e-05,1000,
672 +3145728,192,pat1-2-4_q1_gbits6,1-2-4,3,1,quantizedg,6,0.0014088752850260648,-4.3383693351876355e-05,1000,
673 +3145728,192,pat1-2-4_q1_gbits8,1-2-4,3,1,quantizedg,8,0.0003567466463802188,-1.4731809393496242e-05,1000,
674 +3145728,192,pat1-2-4_q1_single,1-2-4,3,1,single,64,4.1221187069529555e-05,-2.4411088589911252e-05,1000,
675 +3145728,192,pat1-2-4_q1_stale,1-2-4,3,1,stale,64,0.001076979769394984,8.200946423559603e-06,1000,
676 +3145728,192,pat1-2-4_q2,1-2-4,3,2,exact,64,3.0672763595926946e-12,-9.552170999446189e-13,1000,1.0
677 +3145728,192,pat1-2-4_q2_single,1-2-4,3,2,single,64,3.0672763595926946e-12,-9.552170999446189e-13,1000,
678 +3145728,192,pat1-2-4_q2_stale,1-2-4,3,2,stale,64,5.5819557900130325e-06,7.111297399163263e-08,1000,
679 +3145728,192,pat1-2-4_q3,1-2-4,3,3,exact,64,2.2408134264454726e-13,-4.52896171897057e-15,1000,1.0
680 +3145728,192,pat1-2-4_q3_single,1-2-4,3,3,single,64,2.2408134264454726e-13,-4.52896171897057e-15,1000,
681 +3145728,192,pat1-2-4_q3_stale,1-2-4,3,3,stale,64,2.893985263933761e-08,5.136762165452956e-10,1000,
682 +3145728,192,pat1-2_q0,1-2,2,0,exact,64,0.1785733858453111,-0.10378974133216808,1000,1.0
683 +3145728,192,pat1-2_q1,1-2,2,1,exact,64,2.9605097606108534e-05,-1.7978413081760424e-05,1000,1.0
684 +3145728,192,pat1-2_q1_Hbits12,1-2,2,1,quantizedH,12,3.5294648823262916e-05,-1.6908842016845823e-05,1000,
685 +3145728,192,pat1-2_q1_Hbits16,1-2,2,1,quantizedH,16,2.9746443889028005e-05,-1.8038296343351143e-05,1000,
686 +3145728,192,pat1-2_q1_Hbits2,1-2,2,1,quantizedH,2,0.00019652433347038236,8.703136192873688e-06,1000,
687 +3145728,192,pat1-2_q1_Hbits24,1-2,2,1,quantizedH,24,2.9605064155656162e-05,-1.797832582284583e-05,1000,
688 +3145728,192,pat1-2_q1_Hbits3,1-2,2,1,quantizedH,3,0.00019652433347038236,8.703136192873688e-06,1000,
689 +3145728,192,pat1-2_q1_Hbits4,1-2,2,1,quantizedH,4,0.00019652433347038236,8.703136192873688e-06,1000,
690 +3145728,192,pat1-2_q1_Hbits52,1-2,2,1,quantizedH,52,2.9605097606108534e-05,-1.7978413081760424e-05,1000,
691 +3145728,192,pat1-2_q1_Hbits6,1-2,2,1,quantizedH,6,0.00019652433347038236,8.703136192873688e-06,1000,
692 +3145728,192,pat1-2_q1_Hbits8,1-2,2,1,quantizedH,8,0.00018927173659563572,1.5075517181748503e-05,1000,
693 +3145728,192,pat1-2_q1_bothbits12,1-2,2,1,quantizedBoth,12,3.844617153669124e-05,-1.6659074688816754e-05,1000,
694 +3145728,192,pat1-2_q1_bothbits16,1-2,2,1,quantizedBoth,16,2.9726688783271212e-05,-1.798744298943488e-05,1000,
695 +3145728,192,pat1-2_q1_bothbits2,1-2,2,1,quantizedBoth,2,0.018027449948194916,-0.0011351790621184608,1000,
696 +3145728,192,pat1-2_q1_bothbits24,1-2,2,1,quantizedBoth,24,2.9604723812364337e-05,-1.7978266819138574e-05,1000,
697 +3145728,192,pat1-2_q1_bothbits3,1-2,2,1,quantizedBoth,3,0.008949229730586573,-0.0004640939469788607,1000,
698 +3145728,192,pat1-2_q1_bothbits4,1-2,2,1,quantizedBoth,4,0.004826680364958274,0.00016523335144994354,1000,
699 +3145728,192,pat1-2_q1_bothbits52,1-2,2,1,quantizedBoth,52,2.9605097606108534e-05,-1.7978413081760424e-05,1000,
700 +3145728,192,pat1-2_q1_bothbits6,1-2,2,1,quantizedBoth,6,0.001154721819936096,3.03265315763559e-05,1000,
701 +3145728,192,pat1-2_q1_bothbits8,1-2,2,1,quantizedBoth,8,0.00034004650645736036,2.697177190746408e-05,1000,
702 +3145728,192,pat1-2_q1_gbits12,1-2,2,1,quantizedg,12,3.388161867772086e-05,-1.7728554089121787e-05,1000,
703 +3145728,192,pat1-2_q1_gbits16,1-2,2,1,quantizedg,16,2.9585576614416017e-05,-1.792755963982478e-05,1000,
704 +3145728,192,pat1-2_q1_gbits2,1-2,2,1,quantizedg,2,0.01803807397733273,-0.0011606023377499096,1000,
705 +3145728,192,pat1-2_q1_gbits24,1-2,2,1,quantizedg,24,2.960475723145594e-05,-1.7978354078643992e-05,1000,
706 +3145728,192,pat1-2_q1_gbits3,1-2,2,1,quantizedg,3,0.008949462813014421,-0.0004904635083147831,1000,
707 +3145728,192,pat1-2_q1_gbits4,1-2,2,1,quantizedg,4,0.004816734855570651,0.00013846921114231157,1000,
708 +3145728,192,pat1-2_q1_gbits52,1-2,2,1,quantizedg,52,2.9605097606108534e-05,-1.7978413081760424e-05,1000,
709 +3145728,192,pat1-2_q1_gbits6,1-2,2,1,quantizedg,6,0.0011458118617122834,3.6731654060892065e-06,1000,
710 +3145728,192,pat1-2_q1_gbits8,1-2,2,1,quantizedg,8,0.00029785786499957257,-6.069218595720998e-06,1000,
711 +3145728,192,pat1-2_q1_single,1-2,2,1,single,64,2.9605097606108534e-05,-1.7978413081760424e-05,1000,
712 +3145728,192,pat1-2_q1_stale,1-2,2,1,stale,64,0.0008949826583977863,-0.0005142014494662359,1000,
713 +3145728,192,pat1-2_q2,1-2,2,2,exact,64,1.4819461954903383e-12,-4.891278656488215e-13,1000,1.0
714 +3145728,192,pat1-2_q2_single,1-2,2,2,single,64,1.4819461954903383e-12,-4.891278656488215e-13,1000,
715 +3145728,192,pat1-2_q2_stale,1-2,2,2,stale,64,4.607425599148993e-06,-2.6352947598948663e-06,1000,
716 +3145728,192,pat1-2_q3,1-2,2,3,exact,64,2.2425431167272984e-13,1.3783796535997384e-15,1000,1.0
717 +3145728,192,pat1-2_q3_single,1-2,2,3,single,64,2.2425431167272984e-13,1.3783796535997384e-15,1000,
718 +3145728,192,pat1-2_q3_stale,1-2,2,3,stale,64,2.3728348614712917e-08,-1.3507974890856914e-08,1000,
719 +3145728,192,pat1-3_q0,1-3,2,0,exact,64,0.1973440425856836,-0.15235907693805562,1000,1.0
720 +3145728,192,pat1-3_q1,1-3,2,1,exact,64,3.264102394330932e-05,-2.1956702684263564e-05,1000,1.0
721 +3145728,192,pat1-3_q2,1-3,2,2,exact,64,1.415339490849839e-12,-6.003827948322289e-13,1000,1.0
722 +3145728,192,pat1-3_q3,1-3,2,3,exact,64,2.179409592904541e-13,-2.166025169942446e-15,1000,1.0
723 +3145728,192,pat1_q0,1,1,0,exact,64,9.282661736530915,9.282122876317922,1000,1.0
724 +3145728,192,pat1_q1,1,1,1,exact,64,0.048593746845213795,-0.048582430135088624,1000,1.0
725 +3145728,192,pat1_q1_Hbits12,1,1,1,quantizedH,12,0.048612570549359135,-0.04859722693445523,1000,
726 +3145728,192,pat1_q1_Hbits16,1,1,1,quantizedH,16,0.0485939840510133,-0.04858261853180622,1000,
727 +3145728,192,pat1_q1_Hbits2,1,1,1,quantizedH,2,0.04966681728529663,0.04855084470130452,1000,
728 +3145728,192,pat1_q1_Hbits24,1,1,1,quantizedH,24,0.048593752486901086,-0.04858243594964417,1000,
729 +3145728,192,pat1_q1_Hbits3,1,1,1,quantizedH,3,0.04966681728529663,0.04855084470130452,1000,
730 +3145728,192,pat1_q1_Hbits4,1,1,1,quantizedH,4,0.04966681728529663,0.04855084470130452,1000,
731 +3145728,192,pat1_q1_Hbits52,1,1,1,quantizedH,52,0.04859374684521324,-0.048582430135088034,1000,
732 +3145728,192,pat1_q1_Hbits6,1,1,1,quantizedH,6,0.09759310002993347,-0.09611716191073334,1000,
733 +3145728,192,pat1_q1_Hbits8,1,1,1,quantizedH,8,0.05154106100661322,-0.05033956426509147,1000,
734 +3145728,192,pat1_q1_bothbits12,1,1,1,quantizedBoth,12,0.048660790951213305,-0.0486358407030099,1000,
735 +3145728,192,pat1_q1_bothbits16,1,1,1,quantizedBoth,16,0.04859071142594817,-0.048579327916911275,1000,
736 +3145728,192,pat1_q1_bothbits2,1,1,1,quantizedBoth,2,1.11467835194511,-1.110181969095341,1000,
737 +3145728,192,pat1_q1_bothbits24,1,1,1,quantizedBoth,24,0.048593754229468746,-0.04858243780256568,1000,
738 +3145728,192,pat1_q1_bothbits3,1,1,1,quantizedBoth,3,0.6295826016071246,0.6201367876659674,1000,
739 +3145728,192,pat1_q1_bothbits4,1,1,1,quantizedBoth,4,0.2662613186775708,-0.178338634623285,1000,
740 +3145728,192,pat1_q1_bothbits52,1,1,1,quantizedBoth,52,0.04859374684521362,-0.04858243013508842,1000,
741 +3145728,192,pat1_q1_bothbits6,1,1,1,quantizedBoth,6,0.11867184852592823,-0.09721061197585022,1000,
742 +3145728,192,pat1_q1_bothbits8,1,1,1,quantizedBoth,8,0.05313248060997463,-0.04934657750096034,1000,
743 +3145728,192,pat1_q1_gbits12,1,1,1,quantizedg,12,0.048641373718142394,-0.04862104072479992,1000,
744 +3145728,192,pat1_q1_gbits16,1,1,1,quantizedg,16,0.0485904721356963,-0.048579139509542735,1000,
745 +3145728,192,pat1_q1_gbits2,1,1,1,quantizedg,2,1.2235796096108131,-1.219507461141106,1000,
746 +3145728,192,pat1_q1_gbits24,1,1,1,quantizedg,24,0.04859374858780391,-0.04858243198801505,1000,
747 +3145728,192,pat1_q1_gbits3,1,1,1,quantizedg,3,0.5400315486473573,0.5290145903471385,1000,
748 +3145728,192,pat1_q1_gbits4,1,1,1,quantizedg,4,0.3418306721740992,-0.2778475999495206,1000,
749 +3145728,192,pat1_q1_gbits52,1,1,1,quantizedg,52,0.048593746845214184,-0.04858243013508901,1000,
750 +3145728,192,pat1_q1_gbits6,1,1,1,quantizedg,6,0.08204445189407167,-0.04966816783903547,1000,
751 +3145728,192,pat1_q1_gbits8,1,1,1,quantizedg,8,0.05020469669574787,-0.0475890461303959,1000,
752 +3145728,192,pat1_q1_single,1,1,1,single,64,0.04859374684521358,-0.04858243013508842,1000,
753 +3145728,192,pat1_q1_stale,1,1,1,stale,64,0.09617946684281771,0.09615736112702893,1000,
754 +3145728,192,pat1_q2,1,1,2,exact,64,1.3326055548374507e-06,-1.3313594512182315e-06,1000,1.0
755 +3145728,192,pat1_q2_single,1,1,2,single,64,1.3326055550518142e-06,-1.331359451415143e-06,1000,
756 +3145728,192,pat1_q2_stale,1,1,2,stale,64,0.000503569466747063,0.0005033078006849528,1000,
757 +3145728,192,pat1_q3,1,1,3,exact,64,2.1758484637398277e-13,9.845568954283843e-16,1000,1.0
758 +3145728,192,pat1_q3_single,1,1,3,single,64,2.1820766258333744e-13,1.1814682745140614e-15,1000,
759 +3145728,192,pat1_q3_stale,1,1,3,stale,64,2.610114628767646e-06,2.607706495263206e-06,1000,
modified results/processed/ablation_logistic_summary.csv +127 −127
@@ -1,128 +1,128 @@
1 1 N,m,variant,pattern,r,q,hessian,bits,rms,mean0,n,valid
2 −196608,96,pat1-2-3-4_q0,1-2-3-4,4,0,exact,64,125240.08710588618,1914.8598490179713,80,0.408319091796875
3 −196608,96,pat1-2-3-4_q1,1-2-3-4,4,1,exact,64,291911131368.97,-5789626052.8850975,80,0.408319091796875
4 −196608,96,pat1-2-3-4_q2,1-2-3-4,4,2,exact,64,3.691151744357216e+23,1.564518386754187e+22,80,0.408319091796875
5 −196608,96,pat1-2-3-4_q3,1-2-3-4,4,3,exact,64,1.8297680699053677e+89,-1.9251986487417423e+87,78,0.40807166466346156
6 −196608,96,pat1-2-3_q0,1-2-3,3,0,exact,64,8352.835207761933,1943.5855850959601,80,0.89671630859375
7 −196608,96,pat1-2-3_q1,1-2-3,3,1,exact,64,1017725.9771380429,-141151.5564783585,80,0.89671630859375
8 −196608,96,pat1-2-3_q2,1-2-3,3,2,exact,64,49851844589554.65,2020403362448.2437,80,0.89671630859375
9 −196608,96,pat1-2-3_q3,1-2-3,3,3,exact,64,3.798060453606584e+106,-3.7305242137975383e+104,73,0.8966984160958904
10 −196608,96,pat1-2-4-8_q0,1-2-4-8,4,0,exact,64,86205.81540136944,3778.8138207168668,80,0.0
11 −196608,96,pat1-2-4-8_q1,1-2-4-8,4,1,exact,64,52501188845.473625,992053572.3870904,80,0.0
12 −196608,96,pat1-2-4-8_q2,1-2-4-8,4,2,exact,64,1.958777996045666e+24,-3.914378959827043e+22,80,0.0
13 −196608,96,pat1-2-4-8_q3,1-2-4-8,4,3,exact,64,1.958777996045666e+24,-3.914378959827043e+22,80,0.0
14 −196608,96,pat1-2-4_q0,1-2-4,3,0,exact,64,43194.48553642932,1953.1608304552876,80,0.41552734375
15 −196608,96,pat1-2-4_q1,1-2-4,3,1,exact,64,6036956301.141765,113670514.11113386,80,0.41552734375
16 −196608,96,pat1-2-4_q1_Hbits12,1-2-4,3,1,quantizedH,12,909528509.6735398,-17580754.35449255,80,
17 −196608,96,pat1-2-4_q1_Hbits16,1-2-4,3,1,quantizedH,16,7523579765.857587,141758083.41620055,80,
18 −196608,96,pat1-2-4_q1_Hbits2,1-2-4,3,1,quantizedH,2,12225778.9475807,-228464.2880995456,80,
19 −196608,96,pat1-2-4_q1_Hbits24,1-2-4,3,1,quantizedH,24,6026767040.398128,113478005.18708882,80,
20 −196608,96,pat1-2-4_q1_Hbits3,1-2-4,3,1,quantizedH,3,4695056.998611163,-94047.24159755919,80,
21 −196608,96,pat1-2-4_q1_Hbits4,1-2-4,3,1,quantizedH,4,67513775.8199335,2078913.4046347898,80,
22 −196608,96,pat1-2-4_q1_Hbits52,1-2-4,3,1,quantizedH,52,6036956301.217164,113670514.11255841,80,
23 −196608,96,pat1-2-4_q1_Hbits6,1-2-4,3,1,quantizedH,6,16287543.433179554,-182031.5259587727,80,
24 −196608,96,pat1-2-4_q1_Hbits8,1-2-4,3,1,quantizedH,8,32037323.510255203,-577772.2071813174,80,
25 −196608,96,pat1-2-4_q1_bothbits12,1-2-4,3,1,quantizedBoth,12,909518384.5097548,-17580579.270076398,80,
26 −196608,96,pat1-2-4_q1_bothbits16,1-2-4,3,1,quantizedBoth,16,7523549373.392959,141757509.16513097,80,
27 −196608,96,pat1-2-4_q1_bothbits2,1-2-4,3,1,quantizedBoth,2,12038039.948792217,-232615.99979077076,80,
28 −196608,96,pat1-2-4_q1_bothbits24,1-2-4,3,1,quantizedBoth,24,6026767059.282383,113478005.54899819,80,
29 −196608,96,pat1-2-4_q1_bothbits3,1-2-4,3,1,quantizedBoth,3,4693205.741264144,-92830.26103392875,80,
30 −196608,96,pat1-2-4_q1_bothbits4,1-2-4,3,1,quantizedBoth,4,67934130.85161178,2090258.717442663,80,
31 −196608,96,pat1-2-4_q1_bothbits52,1-2-4,3,1,quantizedBoth,52,6036956301.217164,113670514.11255841,80,
32 −196608,96,pat1-2-4_q1_bothbits6,1-2-4,3,1,quantizedBoth,6,16336936.748826984,-180928.61770167953,80,
33 −196608,96,pat1-2-4_q1_bothbits8,1-2-4,3,1,quantizedBoth,8,32018537.005989276,-577210.9242879681,80,
34 −196608,96,pat1-2-4_q1_gbits12,1-2-4,3,1,quantizedg,12,6036889082.723597,113669228.63594265,80,
35 −196608,96,pat1-2-4_q1_gbits16,1-2-4,3,1,quantizedg,16,6036931914.132578,113670053.32529461,80,
36 −196608,96,pat1-2-4_q1_gbits2,1-2-4,3,1,quantizedg,2,5757955409.819916,108389343.68540367,80,
37 −196608,96,pat1-2-4_q1_gbits24,1-2-4,3,1,quantizedg,24,6036956320.057941,113670514.47364643,80,
38 −196608,96,pat1-2-4_q1_gbits3,1-2-4,3,1,quantizedg,3,5916605283.318034,111405603.6886544,80,
39 −196608,96,pat1-2-4_q1_gbits4,1-2-4,3,1,quantizedg,4,5919391895.104463,111451762.94129908,80,
40 −196608,96,pat1-2-4_q1_gbits52,1-2-4,3,1,quantizedg,52,6036956301.141765,113670514.11113386,80,
41 −196608,96,pat1-2-4_q1_gbits6,1-2-4,3,1,quantizedg,6,6058528963.2575865,114078889.38041821,80,
42 −196608,96,pat1-2-4_q1_gbits8,1-2-4,3,1,quantizedg,8,6031550273.977768,113568515.26433332,80,
43 −196608,96,pat1-2-4_q1_single,1-2-4,3,1,single,64,3.476056473100561e+26,-1.9774634416387576e+25,80,
44 −196608,96,pat1-2-4_q1_stale,1-2-4,3,1,stale,64,42966.88668106915,1802.8318289512713,80,
45 −196608,96,pat1-2-4_q2,1-2-4,3,2,exact,64,4.28813558891203e+16,57914381206315.06,80,0.41552734375
46 −196608,96,pat1-2-4_q2_single,1-2-4,3,2,single,64,5.797522364764603e+97,1.5130677161665653e+96,79,
47 −196608,96,pat1-2-4_q2_stale,1-2-4,3,2,stale,64,42747.68610835505,1651.686500110065,80,
48 −196608,96,pat1-2-4_q3,1-2-4,3,3,exact,64,4.315190340508941e+16,58646603186564.41,79,0.41539754746835444
49 −196608,96,pat1-2-4_q3_single,1-2-4,3,3,single,64,5.834567613036883e+97,1.5324660202199827e+96,78,
50 −196608,96,pat1-2-4_q3_stale,1-2-4,3,3,stale,64,42537.0104171786,1499.7337889661947,80,
51 −196608,96,pat1-2_q0,1-2,2,0,exact,64,426.9408183023899,-65.8397485320049,80,0.998675537109375
52 −196608,96,pat1-2_q1,1-2,2,1,exact,64,2316.2327040863083,171.48689505752077,80,0.998675537109375
53 −196608,96,pat1-2_q1_Hbits12,1-2,2,1,quantizedH,12,2317.290499084197,171.57398633866123,80,
54 −196608,96,pat1-2_q1_Hbits16,1-2,2,1,quantizedH,16,2316.103199145409,171.47690357456582,80,
55 −196608,96,pat1-2_q1_Hbits2,1-2,2,1,quantizedH,2,2721.153244054171,181.63898384774785,80,
56 −196608,96,pat1-2_q1_Hbits24,1-2,2,1,quantizedH,24,2316.2327276136243,171.4868978913657,80,
57 −196608,96,pat1-2_q1_Hbits3,1-2,2,1,quantizedH,3,2622.3517865936565,192.8048166643757,80,
58 −196608,96,pat1-2_q1_Hbits4,1-2,2,1,quantizedH,4,2278.290667474224,170.06263895683983,80,
59 −196608,96,pat1-2_q1_Hbits52,1-2,2,1,quantizedH,52,2316.2327040863083,171.48689505752077,80,
60 −196608,96,pat1-2_q1_Hbits6,1-2,2,1,quantizedH,6,2394.3841977487596,177.2683550088,80,
61 −196608,96,pat1-2_q1_Hbits8,1-2,2,1,quantizedH,8,2305.6217279509715,170.6633829764235,80,
62 −196608,96,pat1-2_q1_bothbits12,1-2,2,1,quantizedBoth,12,2317.114432592448,171.56086671978414,80,
63 −196608,96,pat1-2_q1_bothbits16,1-2,2,1,quantizedBoth,16,2316.098999501922,171.47645214951058,80,
64 −196608,96,pat1-2_q1_bothbits2,1-2,2,1,quantizedBoth,2,2852.512591632746,189.64679682941338,80,
65 −196608,96,pat1-2_q1_bothbits24,1-2,2,1,quantizedBoth,24,2316.232697147572,171.4868958265939,80,
66 −196608,96,pat1-2_q1_bothbits3,1-2,2,1,quantizedBoth,3,2537.8580180894674,186.4494495930497,80,
67 −196608,96,pat1-2_q1_bothbits4,1-2,2,1,quantizedBoth,4,2321.3525941359567,173.7440127905897,80,
68 −196608,96,pat1-2_q1_bothbits52,1-2,2,1,quantizedBoth,52,2316.2327040863083,171.48689505752077,80,
69 −196608,96,pat1-2_q1_bothbits6,1-2,2,1,quantizedBoth,6,2383.707803554313,176.47201307093954,80,
70 −196608,96,pat1-2_q1_bothbits8,1-2,2,1,quantizedBoth,8,2306.3202226197063,170.71943221243401,80,
71 −196608,96,pat1-2_q1_gbits12,1-2,2,1,quantizedg,12,2316.0567364011013,171.47378250620704,80,
72 −196608,96,pat1-2_q1_gbits16,1-2,2,1,quantizedg,16,2316.22850408998,171.48644360779457,80,
73 −196608,96,pat1-2_q1_gbits2,1-2,2,1,quantizedg,2,2443.158785239364,180.37506792442647,80,
74 −196608,96,pat1-2_q1_gbits24,1-2,2,1,quantizedg,24,2316.2326736202544,171.4868929927489,80,
75 −196608,96,pat1-2_q1_gbits3,1-2,2,1,quantizedg,3,2241.4665729757244,165.84370072017967,80,
76 −196608,96,pat1-2_q1_gbits4,1-2,2,1,quantizedg,4,2359.819168215639,175.16943489807335,80,
77 −196608,96,pat1-2_q1_gbits52,1-2,2,1,quantizedg,52,2316.2327040863083,171.4868950575207,80,
78 −196608,96,pat1-2_q1_gbits6,1-2,2,1,quantizedg,6,2305.943623118098,170.71744118538783,80,
79 −196608,96,pat1-2_q1_gbits8,1-2,2,1,quantizedg,8,2316.9345314782126,171.5432056704916,80,
80 −196608,96,pat1-2_q1_single,1-2,2,1,single,64,2485.332072728558,189.7261464674608,80,
81 −196608,96,pat1-2_q1_stale,1-2,2,1,stale,64,139.16667610593788,9.734125823129798,80,
82 −196608,96,pat1-2_q2,1-2,2,2,exact,64,934676.5379541665,-63523.27020113837,80,0.998675537109375
83 −196608,96,pat1-2_q2_single,1-2,2,2,single,64,2987160.1062184074,-208603.85182587392,80,
84 −196608,96,pat1-2_q2_stale,1-2,2,2,stale,64,33.34920453155626,2.7318734125181896,80,
85 −196608,96,pat1-2_q3,1-2,2,3,exact,64,160698855721734.75,10900040830704.254,80,0.998675537109375
86 −196608,96,pat1-2_q3_single,1-2,2,3,single,64,5.176133218460869e+38,1.070998949504629e+37,80,
87 −196608,96,pat1-2_q3_stale,1-2,2,3,stale,64,3.1597776652878307,0.09113422596709794,80,
88 −196608,96,pat1-3_q0,1-3,2,0,exact,64,4391.04528078168,-1070.552415345987,80,0.89676513671875
89 −196608,96,pat1-3_q1,1-3,2,1,exact,64,331430.0923413302,43591.08464337448,80,0.89676513671875
90 −196608,96,pat1-3_q2,1-3,2,2,exact,64,573260386466.869,-27449656834.023796,80,0.89676513671875
91 −196608,96,pat1-3_q3,1-3,2,3,exact,64,7.084372835967767e+109,-9.454090869597e+107,78,0.8967159955929487
92 −196608,96,pat1_q0,1,1,0,exact,64,57.95003118967403,14.953703419157199,80,1.0
93 −196608,96,pat1_q1,1,1,1,exact,64,3.417184351705471,-0.9004801142474772,80,1.0
94 −196608,96,pat1_q1_Hbits12,1,1,1,quantizedH,12,3.417195854256544,-0.9006198676390087,80,
95 −196608,96,pat1_q1_Hbits16,1,1,1,quantizedH,16,3.4171671960654444,-0.9004685489029874,80,
96 −196608,96,pat1_q1_Hbits2,1,1,1,quantizedH,2,3.383919616717082,-1.6412481969142785,80,
97 −196608,96,pat1_q1_Hbits24,1,1,1,quantizedH,24,3.417184273833559,-0.9004800397790194,80,
98 −196608,96,pat1_q1_Hbits3,1,1,1,quantizedH,3,3.531911922274173,-1.6138475284661085,80,
99 −196608,96,pat1_q1_Hbits4,1,1,1,quantizedH,4,3.428919126719672,-0.40314915644826216,80,
100 −196608,96,pat1_q1_Hbits52,1,1,1,quantizedH,52,3.4171843517054716,-0.9004801142474779,80,
101 −196608,96,pat1_q1_Hbits6,1,1,1,quantizedH,6,3.5040026232066586,-0.9403727295604793,80,
102 −196608,96,pat1_q1_Hbits8,1,1,1,quantizedH,8,3.418991698845034,-0.9013893469061325,80,
103 −196608,96,pat1_q1_bothbits12,1,1,1,quantizedBoth,12,3.4176673648014138,-0.900635912454959,80,
104 −196608,96,pat1_q1_bothbits16,1,1,1,quantizedBoth,16,3.4171752631634504,-0.9004714384185071,80,
105 −196608,96,pat1_q1_bothbits2,1,1,1,quantizedBoth,2,4.755456559895578,-0.6365749013522344,80,
106 −196608,96,pat1_q1_bothbits24,1,1,1,quantizedBoth,24,3.41718429328873,-0.9004800559147755,80,
107 −196608,96,pat1_q1_bothbits3,1,1,1,quantizedBoth,3,4.303046106208353,-1.5830152992462336,80,
108 −196608,96,pat1_q1_bothbits4,1,1,1,quantizedBoth,4,3.6669395671048677,-0.44324425252453176,80,
109 −196608,96,pat1_q1_bothbits52,1,1,1,quantizedBoth,52,3.4171843517054725,-0.9004801142474783,80,
110 −196608,96,pat1_q1_bothbits6,1,1,1,quantizedBoth,6,3.489399105733282,-0.9205048054230579,80,
111 −196608,96,pat1_q1_bothbits8,1,1,1,quantizedBoth,8,3.422677122593681,-0.9037968764468021,80,
112 −196608,96,pat1_q1_gbits12,1,1,1,quantizedg,12,3.4176564057909196,-0.900496148003249,80,
113 −196608,96,pat1_q1_gbits16,1,1,1,quantizedg,16,3.4171924196064336,-0.9004830037146784,80,
114 −196608,96,pat1_q1_gbits2,1,1,1,quantizedg,2,4.459558961196356,0.0480763052464867,80,
115 −196608,96,pat1_q1_gbits24,1,1,1,quantizedg,24,3.417184371160661,-0.9004801303832325,80,
116 −196608,96,pat1_q1_gbits3,1,1,1,quantizedg,3,4.101158482588874,-0.869126355041694,80,
117 −196608,96,pat1_q1_gbits4,1,1,1,quantizedg,4,3.687395110136895,-0.9420030822393578,80,
118 −196608,96,pat1_q1_gbits52,1,1,1,quantizedg,52,3.4171843517054725,-0.9004801142474779,80,
119 −196608,96,pat1_q1_gbits6,1,1,1,quantizedg,6,3.406094207582943,-0.8808442152127327,80,
120 −196608,96,pat1_q1_gbits8,1,1,1,quantizedg,8,3.420875982958587,-0.9028865511712338,80,
121 −196608,96,pat1_q1_single,1,1,1,single,64,17.274562982273423,-1.1185654111119803,80,
122 −196608,96,pat1_q1_stale,1,1,1,stale,64,1.301854166202213,-0.20755076519401663,80,
123 −196608,96,pat1_q2,1,1,2,exact,64,0.011268815482371377,-0.003005495351663909,80,1.0
124 −196608,96,pat1_q2_single,1,1,2,single,64,11.673753027721276,0.5541014863265371,80,
125 −196608,96,pat1_q2_stale,1,1,2,stale,64,0.10943351985967734,0.012730776372106687,80,
126 −196608,96,pat1_q3,1,1,3,exact,64,1.2447290999364743e-07,-3.310662032485816e-08,80,1.0
127 −196608,96,pat1_q3_single,1,1,3,single,64,10.562124610310807,0.124865722320413,80,
128 −196608,96,pat1_q3_stale,1,1,3,stale,64,0.00918446604901482,-0.0005940183283543361,80,
2 +196608,96,pat1-2-3-4_q0,1-2-3-4,4,0,exact,64,104987.82999501462,-64.57967566152847,240,0.4075703938802083
3 +196608,96,pat1-2-3-4_q1,1-2-3-4,4,1,exact,64,183514089715.7295,-1155637972.4892218,240,0.4075703938802083
4 +196608,96,pat1-2-3-4_q2,1-2-3-4,4,2,exact,64,4.0151599459513256e+32,-2.5984141012661525e+30,240,0.4075703938802083
5 +196608,96,pat1-2-3-4_q3,1-2-3-4,4,3,exact,64,5.1614582019313276e+140,1.3469240187338678e+138,232,0.4073759933997845
6 +196608,96,pat1-2-3_q0,1-2-3,3,0,exact,64,17214.62230599354,1987.3795392435013,240,0.897369384765625
7 +196608,96,pat1-2-3_q1,1-2-3,3,1,exact,64,904318231.3432182,-5817131.064306829,240,0.897369384765625
8 +196608,96,pat1-2-3_q2,1-2-3,3,2,exact,64,4.221705036669512e+17,-2216780644713044.0,240,0.897369384765625
9 +196608,96,pat1-2-3_q3,1-2-3,3,3,exact,64,1.5219227320174857e+137,-1.6891705312236e+135,222,0.8974213471283784
10 +196608,96,pat1-2-4-8_q0,1-2-4-8,4,0,exact,64,61173.01618248491,5189.850395144552,240,0.0
11 +196608,96,pat1-2-4-8_q1,1-2-4-8,4,1,exact,64,31383347470.438206,146912111.9162064,240,0.0
12 +196608,96,pat1-2-4-8_q2,1-2-4-8,4,2,exact,64,1.4970487417880418e+27,-8.813990777150969e+24,240,0.0
13 +196608,96,pat1-2-4-8_q3,1-2-4-8,4,3,exact,64,1.4970487417880418e+27,-8.813990777150969e+24,240,0.0
14 +196608,96,pat1-2-4_q0,1-2-4,3,0,exact,64,30715.236161112156,2671.3659442118433,240,0.41467081705729164
15 +196608,96,pat1-2-4_q1,1-2-4,3,1,exact,64,3623029971.193384,15571292.57793255,240,0.41467081705729164
16 +196608,96,pat1-2-4_q1_Hbits12,1-2-4,3,1,quantizedH,12,637105263.9947677,-14095122.616158815,240,
17 +196608,96,pat1-2-4_q1_Hbits16,1-2-4,3,1,quantizedH,16,4480857767.918501,22445065.38706069,240,
18 +196608,96,pat1-2-4_q1_Hbits2,1-2-4,3,1,quantizedH,2,7408076.616482713,-115733.1827019616,240,
19 +196608,96,pat1-2-4_q1_Hbits24,1-2-4,3,1,quantizedH,24,3617386591.130274,15505841.632080352,240,
20 +196608,96,pat1-2-4_q1_Hbits3,1-2-4,3,1,quantizedH,3,31873095.174458865,-147475.17186181236,240,
21 +196608,96,pat1-2-4_q1_Hbits4,1-2-4,3,1,quantizedH,4,40310849.00678594,865726.7160097938,240,
22 +196608,96,pat1-2-4_q1_Hbits52,1-2-4,3,1,quantizedH,52,3623029971.2369547,15571292.578268489,240,
23 +196608,96,pat1-2-4_q1_Hbits6,1-2-4,3,1,quantizedH,6,12803035.467607845,-139816.97995240026,240,
24 +196608,96,pat1-2-4_q1_Hbits8,1-2-4,3,1,quantizedH,8,32483213.82596439,251828.2702993033,240,
25 +196608,96,pat1-2-4_q1_bothbits12,1-2-4,3,1,quantizedBoth,12,637093551.3343352,-14094792.055722125,240,
26 +196608,96,pat1-2-4_q1_bothbits16,1-2-4,3,1,quantizedBoth,16,4480840929.149087,22444858.359035127,240,
27 +196608,96,pat1-2-4_q1_bothbits2,1-2-4,3,1,quantizedBoth,2,7279394.174527087,-113974.23361749777,240,
28 +196608,96,pat1-2-4_q1_bothbits24,1-2-4,3,1,quantizedBoth,24,3617386593.147599,15505842.447116503,240,
29 +196608,96,pat1-2-4_q1_bothbits3,1-2-4,3,1,quantizedBoth,3,32431890.55067585,-146773.88564066202,240,
30 +196608,96,pat1-2-4_q1_bothbits4,1-2-4,3,1,quantizedBoth,4,40534289.23195556,869808.3552548807,240,
31 +196608,96,pat1-2-4_q1_bothbits52,1-2-4,3,1,quantizedBoth,52,3623029971.2369547,15571292.578268489,240,
32 +196608,96,pat1-2-4_q1_bothbits6,1-2-4,3,1,quantizedBoth,6,12797131.845580501,-139465.8522224867,240,
33 +196608,96,pat1-2-4_q1_bothbits8,1-2-4,3,1,quantizedBoth,8,32487783.7506734,252313.25557354066,240,
34 +196608,96,pat1-2-4_q1_gbits12,1-2-4,3,1,quantizedg,12,3622983525.47059,15571611.591135055,240,
35 +196608,96,pat1-2-4_q1_gbits16,1-2-4,3,1,quantizedg,16,3623016597.380035,15571124.951251643,240,
36 +196608,96,pat1-2-4_q1_gbits2,1-2-4,3,1,quantizedg,2,3484411708.4610786,12578703.026007738,240,
37 +196608,96,pat1-2-4_q1_gbits24,1-2-4,3,1,quantizedg,24,3623029973.2439785,15571293.393129624,240,
38 +196608,96,pat1-2-4_q1_gbits3,1-2-4,3,1,quantizedg,3,3559965720.5201063,14517849.29301046,240,
39 +196608,96,pat1-2-4_q1_gbits4,1-2-4,3,1,quantizedg,4,3552999911.262167,15220684.19640828,240,
40 +196608,96,pat1-2-4_q1_gbits52,1-2-4,3,1,quantizedg,52,3623029971.193384,15571292.57793255,240,
41 +196608,96,pat1-2-4_q1_gbits6,1-2-4,3,1,quantizedg,6,3636390602.8585186,15594130.924539061,240,
42 +196608,96,pat1-2-4_q1_gbits8,1-2-4,3,1,quantizedg,8,3620160123.10198,15526420.639394639,240,
43 +196608,96,pat1-2-4_q1_single,1-2-4,3,1,single,64,2.0069021407302868e+26,-6.591539734568365e+24,240,
44 +196608,96,pat1-2-4_q1_stale,1-2-4,3,1,stale,64,30442.89727464845,2522.9940209660263,240,
45 +196608,96,pat1-2-4_q2,1-2-4,3,2,exact,64,3.1394066129468456e+16,-83680451881704.53,240,0.41467081705729164
46 +196608,96,pat1-2-4_q2_single,1-2-4,3,2,single,64,5.383772881791558e+139,-2.8605372366231365e+137,238,
47 +196608,96,pat1-2-4_q2_stale,1-2-4,3,2,stale,64,30181.8771823734,2373.7002125309346,240,
48 +196608,96,pat1-2-4_q3,1-2-4,3,3,exact,64,2.4544027848643174e+91,3.2047569130323376e+89,239,0.41462432989016734
49 +196608,96,pat1-2-4_q3_single,1-2-4,3,3,single,64,5.418028410097271e+139,-2.8970547332608787e+137,235,
50 +196608,96,pat1-2-4_q3_stale,1-2-4,3,3,stale,64,29932.45675085813,2223.491105395825,240,
51 +196608,96,pat1-2_q0,1-2,2,0,exact,64,925.9470469748504,-65.85460410715402,240,0.9986796061197917
52 +196608,96,pat1-2_q1,1-2,2,1,exact,64,268017.5220556079,2387.771599314045,240,0.9986796061197917
53 +196608,96,pat1-2_q1_Hbits12,1-2,2,1,quantizedH,12,262119.19350813277,2336.9137514006634,240,
54 +196608,96,pat1-2_q1_Hbits16,1-2,2,1,quantizedH,16,267992.16322220635,2387.5625189594216,240,
55 +196608,96,pat1-2_q1_Hbits2,1-2,2,1,quantizedH,2,76049.41615092182,-461.37658413129327,240,
56 +196608,96,pat1-2_q1_Hbits24,1-2,2,1,quantizedH,24,268018.7526108341,2387.7820626552007,240,
57 +196608,96,pat1-2_q1_Hbits3,1-2,2,1,quantizedH,3,31099.814792457895,-148.11513699488418,240,
58 +196608,96,pat1-2_q1_Hbits4,1-2,2,1,quantizedH,4,35910.712366041356,412.0591372193192,240,
59 +196608,96,pat1-2_q1_Hbits52,1-2,2,1,quantizedH,52,268017.5220555998,2387.771599313976,240,
60 +196608,96,pat1-2_q1_Hbits6,1-2,2,1,quantizedH,6,522945.16694806115,-4377.9644955586755,240,
61 +196608,96,pat1-2_q1_Hbits8,1-2,2,1,quantizedH,8,224657.20964831673,2013.6984735195765,240,
62 +196608,96,pat1-2_q1_bothbits12,1-2,2,1,quantizedBoth,12,262096.9970858725,2336.720096372362,240,
63 +196608,96,pat1-2_q1_bothbits16,1-2,2,1,quantizedBoth,16,267992.2423864764,2387.562905941932,240,
64 +196608,96,pat1-2_q1_bothbits2,1-2,2,1,quantizedBoth,2,71187.4192640373,-422.4578014131099,240,
65 +196608,96,pat1-2_q1_bothbits24,1-2,2,1,quantizedBoth,24,268018.7497363679,2387.7820372477004,240,
66 +196608,96,pat1-2_q1_bothbits3,1-2,2,1,quantizedBoth,3,31161.38105320975,-151.05499017483996,240,
67 +196608,96,pat1-2_q1_bothbits4,1-2,2,1,quantizedBoth,4,36667.324228477286,419.82536393770516,240,
68 +196608,96,pat1-2_q1_bothbits52,1-2,2,1,quantizedBoth,52,268017.5220555998,2387.771599313976,240,
69 +196608,96,pat1-2_q1_bothbits6,1-2,2,1,quantizedBoth,6,520133.09259564546,-4354.210033713556,240,
70 +196608,96,pat1-2_q1_bothbits8,1-2,2,1,quantizedBoth,8,224737.10833238089,2014.3598976513158,240,
71 +196608,96,pat1-2_q1_gbits12,1-2,2,1,quantizedg,12,267994.8276626745,2387.5736510286424,240,
72 +196608,96,pat1-2_q1_gbits16,1-2,2,1,quantizedg,16,268017.6012297329,2387.7719863716725,240,
73 +196608,96,pat1-2_q1_gbits2,1-2,2,1,quantizedg,2,250498.39103371094,2241.0717607605097,240,
74 +196608,96,pat1-2_q1_gbits24,1-2,2,1,quantizedg,24,268017.519181155,2387.771573906658,240,
75 +196608,96,pat1-2_q1_gbits3,1-2,2,1,quantizedg,3,268143.33104690904,2386.7265035562027,240,
76 +196608,96,pat1-2_q1_gbits4,1-2,2,1,quantizedg,4,273534.49012124265,2436.0782303487995,240,
77 +196608,96,pat1-2_q1_gbits52,1-2,2,1,quantizedg,52,268017.5220556079,2387.771599314045,240,
78 +196608,96,pat1-2_q1_gbits6,1-2,2,1,quantizedg,6,266569.4990703741,2375.0808712218036,240,
79 +196608,96,pat1-2_q1_gbits8,1-2,2,1,quantizedg,8,268112.9065871851,2388.566388926783,240,
80 +196608,96,pat1-2_q1_single,1-2,2,1,single,64,592993.2975422308,7301.894274832475,240,
81 +196608,96,pat1-2_q1_stale,1-2,2,1,stale,64,761.9973143699335,7.19103271373589,240,
82 +196608,96,pat1-2_q2,1-2,2,2,exact,64,5108023454601.344,-43953494733.351616,240,0.9986796061197917
83 +196608,96,pat1-2_q2_single,1-2,2,2,single,64,3.899558941225697e+24,1.108602377682711e+23,240,
84 +196608,96,pat1-2_q2_stale,1-2,2,2,stale,64,664.0085551898372,-2.193202435478047,240,
85 +196608,96,pat1-2_q3,1-2,2,3,exact,64,92920034064013.06,3589604341291.3345,240,0.9986796061197917
86 +196608,96,pat1-2_q3_single,1-2,2,3,single,64,2.9884419070397464e+38,3.569996498348874e+36,240,
87 +196608,96,pat1-2_q3_stale,1-2,2,3,stale,64,580.59779234687,-3.131392825172847,240,
88 +196608,96,pat1-3_q0,1-3,2,0,exact,64,8657.8958551275,-1092.4716757824815,240,0.897418212890625
89 +196608,96,pat1-3_q1,1-3,2,1,exact,64,201834442.07698423,1322545.0445519863,240,0.897418212890625
90 +196608,96,pat1-3_q2,1-3,2,2,exact,64,7.731477829622522e+46,-5.111575430079434e+43,240,0.897418212890625
91 +196608,96,pat1-3_q3,1-3,2,3,exact,64,4.2204872248326707e+130,-1.1741439383209204e+128,237,0.8974217925896625
92 +196608,96,pat1_q0,1,1,0,exact,64,57.821468038325904,14.928031133358237,240,1.0
93 +196608,96,pat1_q1,1,1,1,exact,64,3.401174227178468,-0.896177248404215,240,1.0
94 +196608,96,pat1_q1_Hbits12,1,1,1,quantizedH,12,3.4012076362589516,-0.8961980210506795,240,
95 +196608,96,pat1_q1_Hbits16,1,1,1,quantizedH,16,3.4011663159762078,-0.8961745555183789,240,
96 +196608,96,pat1_q1_Hbits2,1,1,1,quantizedH,2,3.3843031669873977,-1.6328723952404522,240,
97 +196608,96,pat1_q1_Hbits24,1,1,1,quantizedH,24,3.4011741907535042,-0.8961772138363923,240,
98 +196608,96,pat1_q1_Hbits3,1,1,1,quantizedH,3,3.5516321230406898,-1.6330896480167791,240,
99 +196608,96,pat1_q1_Hbits4,1,1,1,quantizedH,4,3.4798079823522072,-0.4079422056192334,240,
100 +196608,96,pat1_q1_Hbits52,1,1,1,quantizedH,52,3.401174227178468,-0.8961772484042149,240,
101 +196608,96,pat1_q1_Hbits6,1,1,1,quantizedH,6,3.49326646015315,-0.933271548165891,240,
102 +196608,96,pat1_q1_Hbits8,1,1,1,quantizedH,8,3.405515283556912,-0.8960583841242772,240,
103 +196608,96,pat1_q1_bothbits12,1,1,1,quantizedBoth,12,3.4015001854147675,-0.8962414010842253,240,
104 +196608,96,pat1_q1_bothbits16,1,1,1,quantizedBoth,16,3.4011574452872693,-0.8961764474871969,240,
105 +196608,96,pat1_q1_bothbits2,1,1,1,quantizedBoth,2,4.7726536837401285,-0.7061771683679964,240,
106 +196608,96,pat1_q1_bothbits24,1,1,1,quantizedBoth,24,3.4011742962575204,-0.8961772455043537,240,
107 +196608,96,pat1_q1_bothbits3,1,1,1,quantizedBoth,3,4.279497895907375,-1.5810801090004327,240,
108 +196608,96,pat1_q1_bothbits4,1,1,1,quantizedBoth,4,3.6295754445643897,-0.4231788713474064,240,
109 +196608,96,pat1_q1_bothbits52,1,1,1,quantizedBoth,52,3.4011742271784686,-0.8961772484042149,240,
110 +196608,96,pat1_q1_bothbits6,1,1,1,quantizedBoth,6,3.496769358815918,-0.9265736736724833,240,
111 +196608,96,pat1_q1_bothbits8,1,1,1,quantizedBoth,8,3.406484411406843,-0.8957855083765909,240,
112 +196608,96,pat1_q1_gbits12,1,1,1,quantizedg,12,3.401466692691324,-0.896220611717678,240,
113 +196608,96,pat1_q1_gbits16,1,1,1,quantizedg,16,3.4011653569331712,-0.8961791403753865,240,
114 +196608,96,pat1_q1_gbits2,1,1,1,quantizedg,2,4.416401883250279,-0.022490741162727697,240,
115 +196608,96,pat1_q1_gbits24,1,1,1,quantizedg,24,3.401174332682474,-0.8961772800721757,240,
116 +196608,96,pat1_q1_gbits3,1,1,1,quantizedg,3,4.086344250214245,-0.8456944533062777,240,
117 +196608,96,pat1_q1_gbits4,1,1,1,quantizedg,4,3.584184044543176,-0.912036674024486,240,
118 +196608,96,pat1_q1_gbits52,1,1,1,quantizedg,52,3.4011742271784686,-0.896177248404215,240,
119 +196608,96,pat1_q1_gbits6,1,1,1,quantizedg,6,3.405524809904378,-0.8895437604619688,240,
120 +196608,96,pat1_q1_gbits8,1,1,1,quantizedg,8,3.4021827123387527,-0.895905227049535,240,
121 +196608,96,pat1_q1_single,1,1,1,single,64,17.180386732266662,-1.7235726359264512,240,
122 +196608,96,pat1_q1_stale,1,1,1,stale,64,1.315874498276448,-0.21340676669288697,240,
123 +196608,96,pat1_q2,1,1,2,exact,64,0.011160765872970272,-0.0029753980365091037,240,1.0
124 +196608,96,pat1_q2_single,1,1,2,single,64,11.768949085853533,0.9223524568664164,240,
125 +196608,96,pat1_q2_stale,1,1,2,stale,64,0.11081372445832047,0.013296074776807885,240,
126 +196608,96,pat1_q3,1,1,3,exact,64,1.220474744667872e-07,-3.244616325268122e-08,240,1.0
127 +196608,96,pat1_q3_single,1,1,3,single,64,11.092754648644917,-0.2898556950075847,240,
128 +196608,96,pat1_q3_stale,1,1,3,stale,64,0.009311188033791109,-0.0006457738238706966,240,
modified results/processed/skew_summary.csv +8 −4
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modified results/processed/summary.csv +44 −0
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modified results/raw/ablation_exponential_N1572864_m24.csv +18342 −0
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