pip install gilknocker
When you thought the GIL was available, and you find yourself suspecting it might be spending time with another.
You probably want py-spy, however if you're looking for a quick-and-dirty way to slip in a GIL contention metric within a specific chunk of code, this might help you.
Unfortunately, there doesn't appear to be any explicit C-API for checking how busy the GIL is. PyGILState_Check won't really work, that's limited to the current thread. PyInterpreterState is an opaque struct, and the PyRuntimeState and other goodies are private in CPython.
So, in ~200 lines of Rusty code, I've conjured up a basic metric that seems
to align with what is reported by py-spy when running the same test case.
This works by spawning a thread which, at regular intervals, re-acquires the GIL and checks
how long it took for the GIL to answer.
Note, the polling_interval_micros, sampling_interval_micros, and sleeping_interval_micros
are configurable.
-
polling_interval_micros- How frequently to re-acquire the GIL and measure how long it took to acquire. The more frequent, the more likely the contention metric will represent accurate GIL contention. A good value for this is 1-1000.
-
sampling_interval_micros- How long to run the polling routine. If this is 1ms, then for 1ms it will try to re-acquire the GIL
at
polling_interval_microsfrequency. Defaults to 10xpolling_interval_micros
- How long to run the polling routine. If this is 1ms, then for 1ms it will try to re-acquire the GIL
at
-
sleeping_interval_micros- How long to sleep between sampling routines. Defaults to 100x
polling_interval_micros
- How long to sleep between sampling routines. Defaults to 100x
Look at the tests
from gilknocker import KnockKnock
# These two are equivalent.
knocker = KnockKnock(1_000)
knocker = KnockKnock(
polling_interval_micros=1_000,
sampling_interval_micros=10_000,
sleeping_interval_micros=100_000
)
knocker.start()
... smart code here ...
knocker.contention_metric # float between 0-1 indicating roughly how busy the GIL was.
knocker.reset_contention_metric() # reset timers and meteric calculation
... some more smart code ...
knocker.stop()
knocker.stop() # Idempodent stopping behavior
knocker.contention_metric # will stay the same after `stop()` is called.
knocker.is_running # If you're ever in doubtShort answer, it depends, but probably not much. As stated above, the more frequent the polling and sampling interval, the more likely non-GIL bound programs will be affected, since there is more room for contention. In GIL heavy programs, the monitoring thread will spend most of its time simply waiting for a lock. This is demonstrated in the benchmarks testing.
In general, it appears that polling_interval_micros=1_000 is a good tradeoff in terms of accurate
GIL contention metric and the resulting sampling_interval_micros=10_000 (defaults to 10x polling interval)
is high enough to relax performance impact a bit when combined with sleeping_interval_micros=100_000
(defaults to 100x polling interval); but feel free to play with these to conform to your needs.
Below is a summary of benchmarking two different
functions, one which uses the GIL, and one which releases it. For interval=None this means
no polling was used, effectively just running the function without gilknocker. Otherwise,
the interval represents the value passed to KnockKnock(polling_interval_micros=interval)
python -m pytest -v --benchmark-only benchmarks/ --benchmark-histogram
------------------------------------------------------------------------------------ benchmark: 18 tests -------------------------------------------------------------------------------------
Name (time in s) Min Max Mean StdDev Median IQR Outliers OPS Rounds Iterations
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_bench[a_little_gil-100000] 1.5368 (2.07) 1.6596 (2.23) 1.5968 (2.14) 0.0476 (130.12) 1.5943 (2.14) 0.0719 (140.14) 2;0 0.6262 (0.47) 5 1
test_bench[a_little_gil-10000] 1.5321 (2.06) 1.5989 (2.14) 1.5618 (2.09) 0.0289 (78.95) 1.5610 (2.09) 0.0510 (99.52) 2;0 0.6403 (0.48) 5 1
test_bench[a_little_gil-1000] 1.5246 (2.05) 1.5298 (2.05) 1.5271 (2.05) 0.0019 (5.12) 1.5269 (2.05) 0.0021 (4.00) 2;0 0.6549 (0.49) 5 1
test_bench[a_little_gil-100] 1.5505 (2.09) 1.5543 (2.08) 1.5528 (2.08) 0.0014 (3.96) 1.5533 (2.08) 0.0018 (3.60) 2;0 0.6440 (0.48) 5 1
test_bench[a_little_gil-10] 1.5863 (2.13) 1.6074 (2.16) 1.5928 (2.14) 0.0088 (23.94) 1.5896 (2.13) 0.0111 (21.60) 1;0 0.6278 (0.47) 5 1
test_bench[a_little_gil-None] 1.5043 (2.02) 1.5067 (2.02) 1.5051 (2.02) 0.0011 (2.95) 1.5044 (2.02) 0.0016 (3.17) 1;0 0.6644 (0.50) 5 1
test_bench[a_lotta_gil-100000] 0.7450 (1.00) 0.7458 (1.0) 0.7455 (1.0) 0.0004 (1.0) 0.7457 (1.0) 0.0005 (1.0) 1;0 1.3413 (1.0) 5 1
test_bench[a_lotta_gil-10000] 0.7471 (1.00) 0.8104 (1.09) 0.7601 (1.02) 0.0281 (76.94) 0.7472 (1.00) 0.0168 (32.82) 1;1 1.3156 (0.98) 5 1
test_bench[a_lotta_gil-1000] 0.7436 (1.0) 0.7472 (1.00) 0.7463 (1.00) 0.0015 (4.11) 0.7470 (1.00) 0.0013 (2.54) 1;1 1.3400 (1.00) 5 1
test_bench[a_lotta_gil-100] 0.7558 (1.02) 0.7680 (1.03) 0.7640 (1.02) 0.0050 (13.56) 0.7644 (1.03) 0.0061 (11.97) 1;0 1.3089 (0.98) 5 1
test_bench[a_lotta_gil-10] 0.7542 (1.01) 0.7734 (1.04) 0.7649 (1.03) 0.0084 (23.05) 0.7669 (1.03) 0.0151 (29.45) 2;0 1.3074 (0.97) 5 1
test_bench[a_lotta_gil-None] 0.7437 (1.00) 0.8490 (1.14) 0.8006 (1.07) 0.0501 (137.15) 0.8074 (1.08) 0.0969 (189.03) 1;0 1.2490 (0.93) 5 1
test_bench[some_gil-100000] 1.4114 (1.90) 1.4131 (1.89) 1.4122 (1.89) 0.0007 (1.81) 1.4121 (1.89) 0.0010 (2.00) 2;0 0.7081 (0.53) 5 1
test_bench[some_gil-10000] 1.4115 (1.90) 1.4258 (1.91) 1.4167 (1.90) 0.0059 (16.03) 1.4141 (1.90) 0.0083 (16.19) 1;0 0.7058 (0.53) 5 1
test_bench[some_gil-1000] 1.4169 (1.91) 1.5793 (2.12) 1.4618 (1.96) 0.0690 (188.82) 1.4232 (1.91) 0.0769 (150.04) 1;0 0.6841 (0.51) 5 1
test_bench[some_gil-100] 1.4468 (1.95) 1.6261 (2.18) 1.5701 (2.11) 0.0752 (205.83) 1.5998 (2.15) 0.1004 (195.70) 1;0 0.6369 (0.47) 5 1
test_bench[some_gil-10] 1.5269 (2.05) 1.9894 (2.67) 1.7037 (2.29) 0.1895 (518.49) 1.7301 (2.32) 0.2692 (524.96) 1;0 0.5870 (0.44) 5 1
test_bench[some_gil-None] 1.4115 (1.90) 1.4267 (1.91) 1.4155 (1.90) 0.0063 (17.33) 1.4136 (1.90) 0.0053 (10.24) 1;1 0.7065 (0.53) 5 1
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