diff options
| -rw-r--r-- | src/cz/crcs/ectester/standalone/ECTesterStandalone.java | 2 | ||||
| -rwxr-xr-x | util/plot_gen.py | 75 |
2 files changed, 50 insertions, 27 deletions
diff --git a/src/cz/crcs/ectester/standalone/ECTesterStandalone.java b/src/cz/crcs/ectester/standalone/ECTesterStandalone.java index e75274d..d740579 100644 --- a/src/cz/crcs/ectester/standalone/ECTesterStandalone.java +++ b/src/cz/crcs/ectester/standalone/ECTesterStandalone.java @@ -502,7 +502,7 @@ public class ECTesterStandalone { System.out.println("index;time[nano];pubW;privS"); int amount = Integer.parseInt(cli.getOptionValue("generate.amount", "1")); - for (int i = 0; i < amount; ++i) { + for (int i = 0; i < amount || amount == 0; ++i) { long elapsed = -System.nanoTime(); KeyPair kp = kpg.genKeyPair(); elapsed += System.nanoTime(); diff --git a/util/plot_gen.py b/util/plot_gen.py index f6cd8e4..c07fc91 100755 --- a/util/plot_gen.py +++ b/util/plot_gen.py @@ -24,14 +24,21 @@ def hw(i): i &= i - 1 return res +def moving_average(a, n) : + ret = np.cumsum(a, dtype=float) + ret[n:] = ret[n:] - ret[:-n] + return ret[n - 1:] / n + if __name__ == "__main__": parser = argparse.ArgumentParser(description="Plot results of ECTester key generation timing.") parser.add_argument("-o", "--output", dest="output", type=argparse.FileType("wb"), help="Write image to [file], do not display.", metavar="file") parser.add_argument("--priv", dest="priv", action="store_true", help="Show private key MSB heatmap plot.") parser.add_argument("--hist", dest="hist", action="store_true", help="Show keygen time histogram.") parser.add_argument("--export-hist", dest="export_hist", action="store_true", help="Show export time histogram.") + parser.add_argument("--avg", dest="avg", action="store_true", help="Show moving average of keygen time.") parser.add_argument("--hw-hist", dest="hw_hist", action="store_true", help="Show Hamming weight heatmap (private key Hamming weight and keygen time).") - parser.add_argument("--skip-first", dest="skip_first", action="store_true", help="Skip first entry, as it's usually a large outlier.") + parser.add_argument("--log", dest="log", action="store_true", help="Use logarithmic scale.") + parser.add_argument("--skip-first", dest="skip_first", nargs="?", const=1, type=int, help="Skip first entry, as it's usually a large outlier.") parser.add_argument("-t", "--title", dest="title", type=str, nargs="?", default="", help="What title to give the figure.") parser.add_argument("file", type=str, help="The file to plot(csv).") @@ -44,14 +51,16 @@ if __name__ == "__main__": print("Bad data?") exit(1) - plots = [opts.priv, opts.hist, opts.export_hist, opts.hw_hist] + plots = [opts.priv, opts.hist, opts.export_hist, opts.avg, opts.hw_hist] n_plots = sum(plots) if n_plots == 0: + plots = [True for _ in range(5)] if len(header_names) == 4: - n_plots = 3 - else: n_plots = 4 - plots = [True for _ in range(n_plots)] + plots[2] = False + else: + n_plots = 5 + if plots[2] and len(header_names) != 5: n_plots = n_plots - 1 @@ -67,7 +76,7 @@ if __name__ == "__main__": data = np.genfromtxt(opts.file, delimiter=";", skip_header=1, converters={3: hx, 4: hx}, dtype=np.dtype([("index", "u4"), ("gen_time", "u4"), ("export_time", "u4"), ("pub", "O"), ("priv", "O")])) if opts.skip_first: - data = data[1:] + data = data[opts.skip_first:] gen_time_data = data["gen_time"] export_time_data = None @@ -80,21 +89,21 @@ if __name__ == "__main__": gen_unit = "ms" if header_names[1].endswith("[nano]"): gen_unit = r"$\mu s$" - gen_time_data = list(map(lambda x: x[1]//1000, gen_time_data)) + gen_time_data = list(map(lambda x: x//1000, gen_time_data)) export_unit = "ms" if len(header_names) == 5 and header_names[2].endswith("[nano]"): export_unit = r"$\mu s$" - export_time_data = list(map(lambda x: x[1]//1000, export_time_data)) + export_time_data = list(map(lambda x: x//1000, export_time_data)) plt.style.use("ggplot") fig = plt.figure() layout_kwargs = {} if opts.title is None: fig.suptitle(opts.file) - #layout_kwargs["rect"] = [0, 0.02, 1, 0.98] + layout_kwargs["rect"] = [0, 0.02, 1, 0.98] elif opts.title: fig.suptitle(opts.title) - #layout_kwargs["rect"] = [0, 0.02, 1, 0.98] + layout_kwargs["rect"] = [0, 0.02, 1, 0.98] fig.tight_layout(**layout_kwargs) max_gen_time = max(gen_time_data) @@ -104,29 +113,31 @@ if __name__ == "__main__": cmap = deepcopy(plt.cm.plasma) cmap.set_bad("black") + norm = colors.Normalize() + if opts.log: + norm = colors.LogNorm() + plot_i = 1 if plots[0]: axe_private = fig.add_subplot(n_plots, 1, plot_i) priv_msb = np.array(list(map(lambda x: x >> (bit_size - 8), priv_data)), dtype=np.dtype("u1")) - heatmap, xedges, yedges = np.histogram2d(priv_msb, gen_time_data, bins=[128, max_gen_time - min_gen_time]) + heatmap, xedges, yedges = np.histogram2d(priv_msb, gen_time_data, bins=[256, max_gen_time - min_gen_time]) extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]] - axe_private.imshow(heatmap.T, extent=extent, aspect="auto", cmap=cmap, origin="low", interpolation="nearest", norm=colors.LogNorm()) + axe_private.imshow(heatmap.T, extent=extent, aspect="auto", cmap=cmap, origin="low", interpolation="nearest", norm=norm) axe_private.set_xlabel("private key MSB value\n(big endian)") - axe_private.set_ylabel("time ({})".format(gen_unit)) + axe_private.set_ylabel("keygen time ({})".format(gen_unit)) plot_i += 1 if plots[1]: axe_hist = fig.add_subplot(n_plots, 1, plot_i) - time_max = max(gen_time_data) - time_min = min(gen_time_data) time_avg = np.average(gen_time_data) time_median = np.median(gen_time_data) - axe_hist.hist(gen_time_data, bins=int((time_max - time_min)/1.2), log=True) + axe_hist.hist(gen_time_data, bins=max_gen_time - min_gen_time, log=opts.log) axe_hist.axvline(x=time_avg, alpha=0.7, linestyle="dotted", color="blue", label="avg = {}".format(time_avg)) axe_hist.axvline(x=time_median, alpha=0.7, linestyle="dotted", color="green", label="median = {}".format(time_median)) - axe_hist.set_ylabel("count\n(log)") + axe_hist.set_ylabel("count" + ("\n(log)" if opts.log else "")) axe_hist.set_xlabel("keygen time ({})".format(gen_unit)) - axe_hist.xaxis.set_major_locator(ticker.MultipleLocator()) + axe_hist.xaxis.set_major_locator(ticker.MaxNLocator()) axe_hist.legend(loc="best") plot_i += 1 @@ -136,31 +147,43 @@ if __name__ == "__main__": time_min = min(export_time_data) time_avg = np.average(export_time_data) time_median = np.median(export_time_data) - axe_hist.hist(export_time_data, bins=int((time_max - time_min)/1.2), log=True) + axe_hist.hist(export_time_data, bins=time_max - time_min, log=opts.log) axe_hist.axvline(x=time_avg, alpha=0.7, linestyle="dotted", color="blue", label="avg = {}".format(time_avg)) axe_hist.axvline(x=time_median, alpha=0.7, linestyle="dotted", color="green", label="median = {}".format(time_median)) - axe_hist.set_ylabel("count\n(log)") + axe_hist.set_ylabel("count" + ("\n(log)" if opts.log else "")) axe_hist.set_xlabel("export time ({})".format(export_unit)) - axe_hist.xaxis.set_major_locator(ticker.MultipleLocator()) + axe_hist.xaxis.set_major_locator(ticker.MaxNLocator()) axe_hist.legend(loc="best") plot_i += 1 if plots[3]: + axe_avg = fig.add_subplot(n_plots, 1, plot_i) + #if len(header_names) == 5: + # axe_other = axe_avg.twinx() + # axe_other.plot(moving_average(export_time_data, 100), color="green", alpha=0.6, label="export, window = 100") + # axe_other.plot(moving_average(export_time_data, 1000), color="yellow", alpha=0.6, label="export, window = 1000") + # axe_other.legend(loc="lower right") + axe_avg.plot(moving_average(gen_time_data, 100), label="window = 100") + axe_avg.plot(moving_average(gen_time_data, 1000), label="window = 1000") + axe_avg.set_ylabel("keygen time ({})".format(gen_unit)) + axe_avg.set_xlabel("index") + axe_avg.legend(loc="best") + plot_i += 1 + + if plots[4]: axe_priv_hist = fig.add_subplot(n_plots, 1, plot_i) priv_hw = np.array(list(map(hw, priv_data)), dtype=np.dtype("u2")) h, xe, ye = np.histogram2d(priv_hw, gen_time_data, bins=[max(priv_hw) - min(priv_hw), max_gen_time - min_gen_time]) - im = axe_priv_hist.imshow(h.T, origin="low", cmap=cmap, aspect="auto", extent=[xe[0], xe[-1], ye[0], ye[-1]], norm=colors.LogNorm()) + im = axe_priv_hist.imshow(h.T, origin="low", cmap=cmap, aspect="auto", extent=[xe[0], xe[-1], ye[0], ye[-1]], norm=norm) axe_priv_hist.axvline(x=bit_size//2, alpha=0.7, linestyle="dotted", color="white", label=str(bit_size//2) + " bits") axe_priv_hist.set_xlabel("private key Hamming weight") - axe_priv_hist.set_ylabel("time ({})".format(gen_unit)) + axe_priv_hist.set_ylabel("keygen time ({})".format(gen_unit)) axe_priv_hist.legend(loc="best") fig.colorbar(im, ax=axe_priv_hist) - if plot_i > 2: - fig.text(0.01, 0.02, "Data size: {}".format(len(gen_time_data)), size="small") + fig.text(0.01, 0.02, "Data size: {}".format(len(gen_time_data)), size="small") if opts.output is None: - plt.tight_layout() plt.show() else: fig.set_size_inches(12, 10) |