Author: Remi Meier <remi.me...@gmail.com>
Branch: multithread-runner
Changeset: r362:b4ffba47bd6a
Date: 2016-05-28 13:31 +0200
http://bitbucket.org/pypy/benchmarks/changeset/b4ffba47bd6a/
Log: make get_results.py output a latex table for the paper
diff --git a/multithread/get_results.py b/multithread/get_results.py
--- a/multithread/get_results.py
+++ b/multithread/get_results.py
@@ -3,17 +3,22 @@
import os
import sys
import json
+import numpy as np
+def collect_warmiters(all_res, run_key):
+ # group timings by thread (multiple vmstarts)
+ res_over_run = all_res[run_key]
+ grouped = {}
+ for bench_key, timings in res_over_run.items():
+ for t in timings:
+ per_bench = grouped.setdefault(bench_key, {})
+ per_bench.setdefault(t['threads'], []).extend(t['warmiters'])
+ return grouped
-def main(argv):
- results_file = argv[0]
- assert os.path.exists(results_file)
-
- with open(results_file, 'r') as f:
- results = json.loads(f.read())
-
+def retrieve_data(results):
print "RUNS:", len(results)
+ all_res = {}
for run_key, run in results.iteritems():
res_over_run = {}
@@ -38,19 +43,117 @@
print "############# THERE ARE FAILURES! #############"
#print "fail reasons:", failures
#import pdb;pdb.set_trace()
+ # print ""
+ # print "RESULTS OF RUN:"
+ all_res[run_key] = res_over_run
+ # grouped = collect_warmiters(all_res, run_key)
+ # for b, per_bench in grouped.items():
+ # print "BENCH", b
+ # for ts in sorted(per_bench.keys()):
+ # if per_bench[ts]:
+ # print "TS:", ts, "times:", per_bench[ts]
print ""
- print "RESULTS OF RUN:"
- for bench_key, timings in res_over_run.items():
- print "BENCH", bench_key
- # group timings by thread (multiple vmstarts)
- threads = results[run_key]['config']['defaults']['threads']
- grouped = [[] for _ in range(max(threads))]
- for t in timings:
- grouped[t['threads'] - 1].extend(t['warmiters'])
+ print ""
+ return all_res
+
+
+def print_csv(all_res, run_key):
+ import numpy as np
+
+ grouped = collect_warmiters(all_res, run_key)
+ cols = len(grouped) * 2 + 1 # "threads" + avg, stddev for each benchmark
+ rows = len(grouped.values()[0]) + 1 # name + threads
+ table = [["" for _ in range(cols)] for _ in range(rows)] # t[row][col]
+
+ table[0][0] = "Threads"
+ for bench_num, (b, per_bench) in enumerate(grouped.items()):
+ print "BENCH", b
+ table[0][1 + bench_num * 2] = b
+ ts_index = 0
+ for ts in sorted(per_bench.keys()):
+ if per_bench[ts]:
+ row = 1 + ts_index
+ if table[row][0] == "":
+ table[row][0] = ts
+ else:
+ assert table[row][0] == ts
+
+ print "TS:", ts, "times:", per_bench[ts]
+ col = 1 + bench_num * 2
+ table[row][col] = np.mean(per_bench[ts])
+ table[row][col+1] = np.std(per_bench[ts])
+ ts_index += 1
+
+ # print table:
+ for r in range(rows):
+ line = ",\t".join(map(str, table[r]))
+ print line
+
+def print_latex_table(all_res, gil_run_key, stm_run_key):
+ print ""
+ print r"\footnotesize"
+ print r"\begin{tabularx}{\textwidth}{l|rrrr|rrrr|r}"
+ #print r"\hline"
+ print r"\textbf{Python VM} & \multicolumn{4}{c|}{\textbf{PyPy-GIL}} &
\multicolumn{4}{c}{\textbf{PyPy-STM}} & \multicolumn{1}{|p{1cm}}{\textbf{Max.
speedup}} \\ \hline"
+ print r"\textbf{Threads} & \multicolumn{1}{c}{\textbf{1}} &
\multicolumn{1}{c}{\textbf{2}} & \multicolumn{1}{c}{\textbf{4}} &
\multicolumn{1}{c|}{\textbf{8}} & \multicolumn{1}{c}{\textbf{1}} &
\multicolumn{1}{c}{\textbf{2}} & \multicolumn{1}{c}{\textbf{4}} &
\multicolumn{1}{c}{\textbf{8}} & \multicolumn{1}{|c}{*} \\ \hline"
+
+ gil_grouped = collect_warmiters(all_res, gil_run_key)
+ stm_grouped = collect_warmiters(all_res, stm_run_key)
+
+ assert stm_grouped.keys() == gil_grouped.keys()
+ for bench_key in stm_grouped.keys():
+ elems = []
+ gil_bench = gil_grouped[bench_key]
+ stm_bench = stm_grouped[bench_key]
+ threads = [1, 2, 4, 8]
+ min_gil, min_stm = 9999999., 9999999.
+ for vm_bench in [gil_bench, stm_bench]:
for ts in threads:
- print "TS:", ts, "times:", grouped[ts - 1]
- print ""
- print ""
+ mean, std = np.mean(vm_bench[ts]), np.std(vm_bench[ts])
+ if vm_bench is gil_bench:
+ if mean < min_gil:
+ min_gil = mean
+ else:
+ if mean < min_stm:
+ min_stm = mean
+ elems.append((mean, std))
+
+ cells = []
+ min_mean = min(min_gil, min_stm)
+ for e in elems:
+ if e[0] == min_mean:
+ s = r"$\mathbf{%.2f}$ \scriptsize $\pm %.1f$ \footnotesize" % e
+ else:
+ s = r"$%.2f$ \scriptsize $\pm %.1f$ \footnotesize" % e
+ cells.append(s)
+ #
+ speedup = min_stm / min_gil
+ cells.append(r"$%.1f\times$" % speedup)
+ print r"%s & " % bench_key + " & ".join(cells) + r" \\"
+ #print r"\hline"
+ print r"\end{tabularx}"
+ print r"\normalsize"
+ print ""
+
+def main(argv):
+ results_file = argv[0]
+ assert os.path.exists(results_file)
+
+ with open(results_file, 'r') as f:
+ results = json.loads(f.read())
+
+ all_res = retrieve_data(results)
+ while True:
+ print "select gil and stm run (e.g., 0,1):"
+ runs = all_res.keys()
+ choices = ["%s: %s (%s)" % (i, r, results[r]['python'])
+ for i, r in enumerate(runs)]
+ print "\n".join(choices)
+ choice = raw_input()
+
+ gil_run_key, stm_run_key = [runs[int(c)] for c in choice.split(',')]
+ #print_csv(all_res, stm_run_key)
+ print_latex_table(all_res, gil_run_key, stm_run_key)
if __name__ == '__main__':
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