Analyze of PyPy warmup in performance benchmarks ¶

Contents

Goal ¶

The purpose of this analysis if to choose the number of warmups and samples for performance benchmarks for PyPy 5.7 on the speed-python server.

small enough to reduce total benchmark runtime
get a mean-stdev of the sample close enough to mean-stdev of all values
if performance has a cycle (usually with a spike at the start or end): compute 5 cycles to use the average of a cycle

General notes ¶

The analysis is based on a single data set: pypy2_571_warmups.json.gz.

PyPy2 5.7.1 (revision 1aa2d8e03cdf): 64-bit, static binary
perf 1.2 (dev), performance 0.5.5 (dev)
performance hacked to run exactly 10 worker processes, each computes 250 values with 0 warmup
Date: 2017-04-13 22:19 - 2017-04-14 09:01 (10h45)
67 benchmarks

After computing these data, the following 4 microbenchmarks have been removed from performance benchmark suite and so will be ignored here.

call_method
call_method_slots
call_method_unknown
call_simple

speed-python server:

cpu: 2 HP DL380 G7 Intel® Xeon® X5680 (3.33GHz/6-core/130W/12MB) FIO Processor Kit
memory: 4x 4GB (1x4GB) Dual Rank x4 PC3-10600 (DDR3-1333) Registered CAS-9 Memory Kit
OS: Ubuntu 16.04.1 LTS
Kernel: GNU/Linux 4.4.0-47-generic

Script used to get the number of loops computed by perf:

import perf
suite = perf.BenchmarkSuite.load('pypy.json.gz')
for bench in suite:
    loops = [run._get_loops() for run in bench.get_runs() if run.values]
    if len(set(loops)) != 1:
        raise Exception(bench)
    loops = loops[0]
    print("%s: loops=%s" % (bench.get_name(), loops))

Must read paper: Virtual Machine Warmup Blows Hot and Cold (december 2016).

Glossary ¶

Loops: number of outer loop iteration, number of times the benchmark function is called to compute one “value”. perf calibrates the number of iterations to get a loop taking at least 100 ms.

Value: Average wall-clock time elapsed to call the number loops times. Pseudo-code:

t0 = perf_counter()
for _ in range(loops):
    func()
dt = perf_counter()
value = float(dt) / loops

Warmups: number of ignored values at the beginning of a benchmark. The number is chosen to ignore first values were are usually much higher than the mean of the “steady state”
Run: execution of one worker processes which computed values. In this analysis, each worker process computed exactly 250 values.

Example: a result with 10 runs, 8 values, 0 warmups and 250 values means that the benchmark function was called 10x8x250 = 20,000 times in total, and 8x250 = 2,000 times per process, but the result only contains 10x250 = 2,500 values in total (or 250 values per process) since values are average time per loop iteration.

2to3: loops=1, warmups=0, values=30 ¶

many spikes up to +16% of the mean
no cycle
moving average(50) increases from 1.816 sec to 1.824 sec

Overall:

Moving average (50 values) of runs:

chameleon: loops=32, warmups=6, values=10 ¶

Overall:

Short cycle of 2 values (skip first 6 values, limit to 30 values):

Large cycle of 42 values (Moving average 1 value, skip first 6 values):

Mean:

loops=32, warmups=6, values=10: 7.05 ms +- 0.23 ms
loops=32, warmups=6, values=42: 7.02 ms +- 0.20 ms
LIMIT: loops=32, warmups=6: 7.01 ms +- 0.20 ms

chaos: loops=16, warmups=20, values=25 ¶

Overall:

Cycle of 5 values (average of runs, skip 20, limit to 50):

Glitch at values 35..38.

Mean:

loops=16, warmups=20, values=25: 6.50 ms +- 0.22 ms
LIMIT: warmups=20: 6.48 ms +- 0.22 ms

crypto_pyaes: loops=16, warmups=46, values=45 ¶

Overall:

Cycle of 9 values (average of runs, skip 46, limit to 45):

Mean:

loops=16, warmups=46, values=45: 9.81 ms +- 0.24 ms
LIMIT: loops=16, warmups=46: 9.81 ms +- 0.24 ms

deltablue: loops=256, warmups=14, values=55 ¶

Overall:

Cycle of 11 values (average of runs, skip 14, limit to 55):

Mean:

loops=256, warmups=14, values=55: 441 us +- 20 us
LIMIT: loops=256, warmups=14: 440 us +- 19 us

django_template: loops=4, warmups=12, values=36 ¶

Overall:

Cycle of 7.3 values (average of runs, skip 12, limit to 36):

Mean:

loops=4, warmups=12, values=36: 36.9 ms +- 1.6 ms
loops=4, warmups=12, values=73: 36.9 ms +- 1.6 ms
LIMIT: loops=4, warmups=12: 36.9 ms +- 1.6 ms

dulwich_log: loops=2, warmups=21, values=18 ¶

Overall:

Cycle of 3.6 values (average of runs, skip 21, limit to 18):

Mean:

loops=2, warmups=21, values=18: 98.6 ms +- 4.7 ms
LIMIT: loops=2, warmups=21: 96.8 ms +- 4.4 ms

fannkuch: loops=1, warmups=59, values=40 ¶

Overall:

Moving average of 25 values (skip 59), very small absolute variation (see the Y scale):

Long cycle of 40 values. Not easy to see using moving average, spikes depend on the width of the moving window.

Mean:

loops=1, warmups=59, values=40: 171 ms +- 1 ms
LIMIT: loops=1, warmups=59: 171 ms +- 1 ms

float: loops=4, warmups=25, values=40 ¶

Use the suboptimal step 2 to reduce total benchmark runtime, even if the step 3 is a little bit faster.

Overall:

Step 2, after warmup (average of runs, skip 25, limit 40):

Step 3: cycle of 16 values (average of runs, skip 119, limit 80):

Mean:

Step 2: loops=4, warmups=25, values=40: 41.0 ms +- 0.4 ms
Step 3: loops=4, warmups=119: 39.9 ms +- 1.6 ms

genshi_text: loops=8, BUG!¶

BUG! 19 ms at value 0 => 92 ms at value 250, steady slowdown!

Overall:

genshi_xml: loops=2, BUG!¶

BUG! 70 ms at value 0 => 200 ms at value 250, steady slowdown!

Overall:

go: loops=2, warmups=87, values=80 ¶

Overall:

Cycle of 5 values (average of runs, skip 87, limit 80):

Step 2 after warmup (skip 87):

Contiguous optimization (moving average of 50 values, skip 87), but only minor optimization (look at the Y scale):

Mean:

loops=2, warmups=87, values=32: 87.4 ms +- 4.9 ms
loops=2, warmups=87, values=80: 87.3 ms +- 5.0 ms
LIMIT: loops=2, warmups=87: 87.2 ms +- 5.0 ms

hexiom: loops=64, warmups=36, values=50 ¶

Only compute 2 cycles instead of 5 to limit the benchmark total runtime, since the cycle of long (25 values).

Overall:

Cycle of 25 values (average of runs, skip 36 , limit 127):

Mean:

loops=64, warmups=36, values=50: 2.32 ms +- 0.04 ms
LIMIT: loops=64, warmups=36: 2.33 ms +- 0.05 ms

hg_startup: loops=1, warmups=4, values=10 ¶

Overall:

Step 2 (skip 4, limit 10):

Mean:

loops=1, warmups=4, values=10: 243 ms +- 1 ms
LIMIT: loops=1, warmups=4: 243 ms +- 1 ms

html5lib: loops=2, warmups=50, values=50 ¶

On 250 values, it seems like PyPy optimizes the code multiple times. Tere are at least 3 steps:

Warmup: 0..56
Step 1: 57..158
Step 2: 159..249

Use the step 1 which is suboptimal to reduce the benchmark total runtime.

Overall:

Moving average of 25 values:

Cycle (runs average, skip 57, limit 50):

Mean:

loops=2, warmups=50, values=50: 63.8 ms +- 3.6 ms
LIMIT: loops=2, warmups=50: 63.3 ms +- 3.5 ms

json_dumps: loops=16, warmups=38, values=25 ¶

Overall:

Cycle of 5 values (runs average, skip 38, limit 50):

Mean:

Sample (5 cycles): loops=16, warmups=38, values=25: 7.38 ms +- 0.15 ms
Sample (10 cycles): loops=16, warmups=38, values=50: 7.39 ms +- 0.16 ms
LIMIT: loops=16, warmups=38: 7.39 ms +- 0.16 ms

json_loads: loops=256, warmups=27, values=50 ¶

Overall, +16% spike at value 90, +10% spike at value 190:

Step 2 (skip 27, limit 50) with a pike at value 51:

Mean:

Sample: loops=256, warmups=27, values=50: 28.4 us +- 0.2 us
Limit: loops=256, warmups=27: 28.4 us +- 0.4 us

TODO ¶

logging_format: loops=2048
logging_silent: loops=134217728
logging_simple: loops=4096
mako: loops=8
meteor_contest: loops=2
nbody: loops=4
nqueens: loops=4
pathlib: loops=8
pickle: loops=64
pickle_dict: loops=64
pickle_list: loops=256
pickle_pure_python: loops=128
pidigits: loops=1
pyflate: loops=1
python_startup: loops=8
python_startup_no_site: loops=8
raytrace: loops=8
regex_compile: loops=2
regex_dna: loops=1
regex_effbot: loops=4
regex_v8: loops=1
richards: loops=64
scimark_fft: loops=16
scimark_lu: loops=64
scimark_monte_carlo: loops=32
scimark_sor: loops=128
scimark_sparse_mat_mult: loops=1024
spambayes: loops=4
spectral_norm: loops=16
sqlalchemy_declarative: loops=1
sqlalchemy_imperative: loops=8
sqlite_synth: loops=32768
sympy_expand: loops=1
sympy_integrate: loops=4
sympy_sum: loops=2
sympy_str: loops=1
telco: loops=8
tornado_http: loops=2
unpack_sequence: loops=2048
unpickle: loops=128
unpickle_list: loops=1024
unpickle_pure_python: loops=128
xml_etree_parse: loops=1
xml_etree_iterparse: loops=1
xml_etree_generate: loops=2
xml_etree_process: loops=2

Analyze of PyPy warmup in performance benchmarks ¶

Goal ¶

General notes ¶

Glossary ¶

2to3: loops=1, warmups=0, values=30 ¶

chameleon: loops=32, warmups=6, values=10 ¶

chaos: loops=16, warmups=20, values=25 ¶

crypto_pyaes: loops=16, warmups=46, values=45 ¶

deltablue: loops=256, warmups=14, values=55 ¶

django_template: loops=4, warmups=12, values=36 ¶

dulwich_log: loops=2, warmups=21, values=18 ¶

fannkuch: loops=1, warmups=59, values=40 ¶

float: loops=4, warmups=25, values=40 ¶

genshi_text: loops=8, BUG!¶

genshi_xml: loops=2, BUG!¶

go: loops=2, warmups=87, values=80 ¶

hexiom: loops=64, warmups=36, values=50 ¶

hg_startup: loops=1, warmups=4, values=10 ¶

html5lib: loops=2, warmups=50, values=50 ¶

json_dumps: loops=16, warmups=38, values=25 ¶

json_loads: loops=256, warmups=27, values=50 ¶

TODO ¶

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