fix: use parallelized `numba` functions if possible
#155Comparing
ig/parallel_kernels
(f5f9bb6
) withmain
(099cd51
)-33%
- Regression
- 6
- Untouched
- 226
Different runtime environments detected
Some benchmarks with significant performance changes were compared across different runtime environments, which may affect the accuracy of the results. Learn more about regression causes.Benchmarks
232 total
test_stats_benchmark[scipy.sparse.csc_array-2d-ax0-float64-is_constant]
tests/test_stats.py-33%
1.7 ms2.5 ms
test_stats_benchmark[scipy.sparse.csr_array-2d-ax1-float64-is_constant]
tests/test_stats.py-32%
1.7 ms2.5 ms
test_stats_benchmark[scipy.sparse.csc_array-2d-ax0-float32-is_constant]
tests/test_stats.py-29%
1.7 ms2.4 ms
test_stats_benchmark[scipy.sparse.csr_array-2d-ax1-int32-is_constant]
tests/test_stats.py-29%
1.7 ms2.4 ms
test_stats_benchmark[scipy.sparse.csc_array-2d-ax0-int32-is_constant]
tests/test_stats.py-28%
1.7 ms2.4 ms
test_stats_benchmark[scipy.sparse.csr_array-2d-ax1-float32-is_constant]
tests/test_stats.py-26%
1.7 ms2.3 ms
test_stats_benchmark[scipy.sparse.csc_array-2d-ax0-int32-mean_var]
tests/test_stats.py+2%
57.5 ms56.6 ms
test_stats_benchmark[scipy.sparse.csc_array-2d-ax0-float32-mean_var]
tests/test_stats.py+2%
60.3 ms59.4 ms
test_stats_benchmark[scipy.sparse.csr_array-2d-ax1-float32-mean_var]
tests/test_stats.py+2%
60.3 ms59.4 ms
test_stats_benchmark[scipy.sparse.csr_array-2d-ax1-int32-mean_var]
tests/test_stats.py+2%
57.5 ms56.6 ms
test_stats_benchmark[scipy.sparse.csr_array-2d-ax1-float64-mean_var]
tests/test_stats.py+1%
75.3 ms74.3 ms
test_stats_benchmark[scipy.sparse.csr_array-2d-ax0-int32-mean_var]
tests/test_stats.py+1%
187.3 ms184.7 ms
test_stats_benchmark[scipy.sparse.csc_array-2d-ax0-float64-mean_var]
tests/test_stats.py+1%
75.3 ms74.5 ms
test_stats_benchmark[numpy.ndarray-1d-all-int32-mean]
tests/test_stats.py0%
36.3 ms36.2 ms
test_stats_benchmark[scipy.sparse.csr_array-2d-ax0-int32-min]
tests/test_stats.py0%
277.4 ms276.8 ms
test_stats_benchmark[scipy.sparse.csr_array-2d-ax0-int32-max]
tests/test_stats.py0%
276.7 ms276.2 ms
test_stats_benchmark[scipy.sparse.csr_array-2d-ax0-float64-min]
tests/test_stats.py0%
339.3 ms338.7 ms
test_stats_benchmark[scipy.sparse.csc_array-2d-ax1-int32-mean_var]
tests/test_stats.py0%
187.2 ms187 ms
test_stats_benchmark[scipy.sparse.csr_array-2d-ax0-float32-max]
tests/test_stats.py0%
280 ms279.8 ms
test_stats_benchmark[scipy.sparse.csr_array-2d-ax1-float32-max]
tests/test_stats.py0%
67 ms67 ms
test_stats_benchmark[numpy.ndarray-2d-ax0-int32-is_constant]
tests/test_stats.py0%
70 ms69.9 ms
test_stats_benchmark[scipy.sparse.csc_array-2d-ax1-float32-max]
tests/test_stats.py0%
279.7 ms279.5 ms
test_stats_benchmark[scipy.sparse.csr_array-2d-ax1-float32-mean]
tests/test_stats.py0%
32.3 ms32.3 ms
test_stats_benchmark[scipy.sparse.csr_array-1d-all-int32-mean]
tests/test_stats.py0%
36.2 ms36.2 ms
test_stats_benchmark[scipy.sparse.csc_array-2d-ax0-int32-min]
tests/test_stats.py0%
63.8 ms63.8 ms
Commits
Click on a commit to change the comparison rangeBase
main
099cd51
-32.5%
fix: use parallelized `numba` functions if possible
e91911a
10 days ago
by ilan-gold-0.16%
Merge branch 'main' into ig/parallel_kernels
f5f9bb6
9 days ago
by flying-sheep