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performancePerformance or resource usagePerformance or resource usage
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Lines 1391 to 1402 in 4b27972
for (Py_ssize_t i=0; i<PyList_GET_SIZE(gcstate->callbacks); i++) { | |
PyObject *r, *cb = PyList_GET_ITEM(gcstate->callbacks, i); | |
Py_INCREF(cb); /* make sure cb doesn't go away */ | |
r = PyObject_CallFunction(cb, "sO", phase, info); | |
if (r == NULL) { | |
PyErr_WriteUnraisable(cb); | |
} | |
else { | |
Py_DECREF(r); | |
} | |
Py_DECREF(cb); | |
} |
The phase object can be created earlier with one-time creation and it can be replaced with vectorcall either.
From my microbenchmark, there is 4-5% performance improvement by doing this.
microbenchmark
import pyperf
import gc
def benchamark_collection(loops):
def callback_foo(phase, info):
pass
for _ in range(100):
gc.callbacks.append(callback_foo)
total_time = 0
for _ in range(loops):
t0 = pyperf.perf_counter()
collected = gc.collect()
total_time += pyperf.perf_counter() - t0
return total_time
if __name__ == "__main__":
runner = pyperf.Runner()
runner.metadata["description"] = "GC callback benchmark"
runner.bench_time_func("create_gc_cycles", benchamark_collection)
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performancePerformance or resource usagePerformance or resource usage