#!/usr/bin/python3 # Plot GNU C Library string microbenchmark output. # Copyright (C) 2019-2022 Free Software Foundation, Inc. # This file is part of the GNU C Library. # # The GNU C Library is free software; you can redistribute it and/or # modify it under the terms of the GNU Lesser General Public # License as published by the Free Software Foundation; either # version 2.1 of the License, or (at your option) any later version. # # The GNU C Library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with the GNU C Library; if not, see # <https://www.gnu.org/licenses/>. """Plot string microbenchmark results. Given a benchmark results file in JSON format and a benchmark schema file, plot the benchmark timings in one of the available representations. Separate figure is generated and saved to a file for each 'results' array found in the benchmark results file. Output filenames and plot titles are derived from the metadata found in the benchmark results file. """ import argparse from collections import defaultdict import json import matplotlib as mpl import numpy as np import os import sys try: import jsonschema as validator except ImportError: print("Could not find jsonschema module.") raise # Use pre-selected markers for plotting lines to improve readability markers = [".", "x", "^", "+", "*", "v", "1", ">", "s"] # Benchmark variants for which the x-axis scale should be logarithmic log_variants = {"powers of 2"} def gmean(numbers): """Compute geometric mean. Args: numbers: 2-D list of numbers Return: numpy array with geometric means of numbers along each column """ a = np.array(numbers, dtype=np.complex) means = a.prod(0) ** (1.0 / len(a)) return np.real(means) def relativeDifference(x, x_reference): """Compute per-element relative difference between each row of a matrix and an array of reference values. Args: x: numpy matrix of shape (n, m) x_reference: numpy array of size m Return: relative difference between rows of x and x_reference (in %) """ abs_diff = np.subtract(x, x_reference) return np.divide(np.multiply(abs_diff, 100.0), x_reference) def plotTime(timings, routine, bench_variant, title, outpath): """Plot absolute timing values. Args: timings: timings to plot routine: benchmarked string routine name bench_variant: top-level benchmark variant name title: figure title (generated so far) outpath: output file path (generated so far) Return: y: y-axis values to plot title_final: final figure title outpath_final: file output file path """ y = timings plt.figure() if not args.values: plt.axes().yaxis.set_major_formatter(plt.NullFormatter()) plt.ylabel("timing") title_final = "%s %s benchmark timings\n%s" % \ (routine, bench_variant, title) outpath_final = os.path.join(args.outdir, "%s_%s_%s%s" % \ (routine, args.plot, bench_variant, outpath)) return y, title_final, outpath_final def plotRelative(timings, all_timings, routine, ifuncs, bench_variant, title, outpath): """Plot timing values relative to a chosen ifunc Args: timings: timings to plot all_timings: all collected timings routine: benchmarked string routine name ifuncs: names of ifuncs tested bench_variant: top-level benchmark variant name title: figure title (generated so far) outpath: output file path (generated so far) Return: y: y-axis values to plot title_final: final figure title outpath_final: file output file path """ # Choose the baseline ifunc if args.baseline: baseline = args.baseline.replace("__", "") else: baseline = ifuncs[0] baseline_index = ifuncs.index(baseline) # Compare timings against the baseline y = relativeDifference(timings, all_timings[baseline_index]) plt.figure() plt.axhspan(-args.threshold, args.threshold, color="lightgray", alpha=0.3) plt.axhline(0, color="k", linestyle="--", linewidth=0.4) plt.ylabel("relative timing (in %)") title_final = "Timing comparison against %s\nfor %s benchmark, %s" % \ (baseline, bench_variant, title) outpath_final = os.path.join(args.outdir, "%s_%s_%s%s" % \ (baseline, args.plot, bench_variant, outpath)) return y, title_final, outpath_final def plotMax(timings, routine, bench_variant, title, outpath): """Plot results as percentage of the maximum ifunc performance. The optimal ifunc is computed on a per-parameter-value basis. Performance is computed as 1/timing. Args: timings: timings to plot routine: benchmarked string routine name bench_variant: top-level benchmark variant name title: figure title (generated so far) outpath: output file path (generated so far) Return: y: y-axis values to plot title_final: final figure title outpath_final: file output file path """ perf = np.reciprocal(timings) max_perf = np.max(perf, axis=0) y = np.add(100.0, relativeDifference(perf, max_perf)) plt.figure() plt.axhline(100.0, color="k", linestyle="--", linewidth=0.4) plt.ylabel("1/timing relative to max (in %)") title_final = "Performance comparison against max for %s\n%s " \ "benchmark, %s" % (routine, bench_variant, title) outpath_final = os.path.join(args.outdir, "%s_%s_%s%s" % \ (routine, args.plot, bench_variant, outpath)) return y, title_final, outpath_final def plotThroughput(timings, params, routine, bench_variant, title, outpath): """Plot throughput. Throughput is computed as the varied parameter value over timing. Args: timings: timings to plot params: varied parameter values routine: benchmarked string routine name bench_variant: top-level benchmark variant name title: figure title (generated so far) outpath: output file path (generated so far) Return: y: y-axis values to plot title_final: final figure title outpath_final: file output file path """ y = np.divide(params, timings) plt.figure() if not args.values: plt.axes().yaxis.set_major_formatter(plt.NullFormatter()) plt.ylabel("%s / timing" % args.key) title_final = "%s %s benchmark throughput results\n%s" % \ (routine, bench_variant, title) outpath_final = os.path.join(args.outdir, "%s_%s_%s%s" % \ (routine, args.plot, bench_variant, outpath)) return y, title_final, outpath_final def finishPlot(x, y, title, outpath, x_scale, plotted_ifuncs): """Finish generating current Figure. Args: x: x-axis values y: y-axis values title: figure title outpath: output file path x_scale: x-axis scale plotted_ifuncs: names of ifuncs to plot """ plt.xlabel(args.key) plt.xscale(x_scale) plt.title(title) plt.grid(color="k", linestyle=args.grid, linewidth=0.5, alpha=0.5) for i in range(len(plotted_ifuncs)): plt.plot(x, y[i], marker=markers[i % len(markers)], label=plotted_ifuncs[i]) plt.legend(loc="best", fontsize="small") plt.savefig("%s_%s.%s" % (outpath, x_scale, args.extension), format=args.extension, dpi=args.resolution) if args.display: plt.show() plt.close() def plotRecursive(json_iter, routine, ifuncs, bench_variant, title, outpath, x_scale): """Plot benchmark timings. Args: json_iter: reference to json object routine: benchmarked string routine name ifuncs: names of ifuncs tested bench_variant: top-level benchmark variant name title: figure's title (generated so far) outpath: output file path (generated so far) x_scale: x-axis scale """ # RECURSIVE CASE: 'variants' array found if "variants" in json_iter: # Continue recursive search for 'results' array. Record the # benchmark variant (configuration) in order to customize # the title, filename and X-axis scale for the generated figure. for variant in json_iter["variants"]: new_title = "%s%s, " % (title, variant["name"]) new_outpath = "%s_%s" % (outpath, variant["name"].replace(" ", "_")) new_x_scale = "log" if variant["name"] in log_variants else x_scale plotRecursive(variant, routine, ifuncs, bench_variant, new_title, new_outpath, new_x_scale) return # BASE CASE: 'results' array found domain = [] timings = defaultdict(list) # Collect timings for result in json_iter["results"]: domain.append(result[args.key]) timings[result[args.key]].append(result["timings"]) domain = np.unique(np.array(domain)) averages = [] # Compute geometric mean if there are multple timings for each # parameter value. for parameter in domain: averages.append(gmean(timings[parameter])) averages = np.array(averages).transpose() # Choose ifuncs to plot if isinstance(args.ifuncs, str): plotted_ifuncs = ifuncs else: plotted_ifuncs = [x.replace("__", "") for x in args.ifuncs] plotted_indices = [ifuncs.index(x) for x in plotted_ifuncs] plotted_vals = averages[plotted_indices,:] # Plotting logic specific to each plot type if args.plot == "time": codomain, title, outpath = plotTime(plotted_vals, routine, bench_variant, title, outpath) elif args.plot == "rel": codomain, title, outpath = plotRelative(plotted_vals, averages, routine, ifuncs, bench_variant, title, outpath) elif args.plot == "max": codomain, title, outpath = plotMax(plotted_vals, routine, bench_variant, title, outpath) elif args.plot == "thru": codomain, title, outpath = plotThroughput(plotted_vals, domain, routine, bench_variant, title, outpath) # Plotting logic shared between plot types finishPlot(domain, codomain, title, outpath, x_scale, plotted_ifuncs) def main(args): """Program Entry Point. Args: args: command line arguments (excluding program name) """ # Select non-GUI matplotlib backend if interactive display is disabled if not args.display: mpl.use("Agg") global plt import matplotlib.pyplot as plt schema = None with open(args.schema, "r") as f: schema = json.load(f) for filename in args.bench: bench = None if filename == '-': bench = json.load(sys.stdin) else: with open(filename, "r") as f: bench = json.load(f) validator.validate(bench, schema) for function in bench["functions"]: bench_variant = bench["functions"][function]["bench-variant"] ifuncs = bench["functions"][function]["ifuncs"] ifuncs = [x.replace("__", "") for x in ifuncs] plotRecursive(bench["functions"][function], function, ifuncs, bench_variant, "", "", args.logarithmic) """ main() """ if __name__ == "__main__": parser = argparse.ArgumentParser(description= "Plot string microbenchmark results", formatter_class=argparse.ArgumentDefaultsHelpFormatter) # Required parameter parser.add_argument("bench", nargs="+", help="benchmark results file(s) in json format, " \ "and/or '-' as a benchmark result file from stdin") # Optional parameters parser.add_argument("-b", "--baseline", type=str, help="baseline ifunc for 'rel' plot") parser.add_argument("-d", "--display", action="store_true", help="display figures") parser.add_argument("-e", "--extension", type=str, default="png", choices=["png", "pdf", "svg"], help="output file(s) extension") parser.add_argument("-g", "--grid", action="store_const", default="", const="-", help="show grid lines") parser.add_argument("-i", "--ifuncs", nargs="+", default="all", help="ifuncs to plot") parser.add_argument("-k", "--key", type=str, default="length", help="key to access the varied parameter") parser.add_argument("-l", "--logarithmic", action="store_const", default="linear", const="log", help="use logarithmic x-axis scale") parser.add_argument("-o", "--outdir", type=str, default=os.getcwd(), help="output directory") parser.add_argument("-p", "--plot", type=str, default="time", choices=["time", "rel", "max", "thru"], help="plot absolute timings, relative timings, " \ "performance relative to max, or throughput") parser.add_argument("-r", "--resolution", type=int, default=100, help="dpi resolution for the generated figures") parser.add_argument("-s", "--schema", type=str, default=os.path.join(os.path.dirname( os.path.realpath(__file__)), "benchout_strings.schema.json"), help="schema file to validate the results file.") parser.add_argument("-t", "--threshold", type=int, default=5, help="threshold to mark in 'rel' graph (in %%)") parser.add_argument("-v", "--values", action="store_true", help="show actual values") args = parser.parse_args() main(args)