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Title: bpo-40255: Implement Immortal Instances - Optimizations Combined by eduardo-elizondo · Pull Request #31491 · python/cpython · GitHub

Open Graph Title: bpo-40255: Implement Immortal Instances - Optimizations Combined by eduardo-elizondo · Pull Request #31491 · python/cpython

X Title: bpo-40255: Implement Immortal Instances - Optimizations Combined by eduardo-elizondo · Pull Request #31491 · python/cpython

Description: This is an optimization on top of PR19474. It combines PR31488, PR31489, and PR31490 into a single change to measure the combined performance benefits. These results do not change too much from what was already achieved independently by these optimizations (as some of the immortalized instances start overlaping with each other). That being said, performance will keep scaling as the application scales as well. The current microbenchmarks do not measure applications that contain hundreds of imports or thousands of interned strings. Nonetheless, it is still worthwhile to consider all of these improvements in conjunction when thinking about larger scale applications. Benchmark Results Overall: 0% faster compared to the main branch and the highest number of non-stat significant benchmarks (18) pyperformance results 2to3: Mean +- std dev: [cpython_master] 432 ms +- 15 ms -> [immortal_instances_opt_combined_v3] 451 ms +- 16 ms: 1.04x slower chaos: Mean +- std dev: [cpython_master] 126 ms +- 4 ms -> [immortal_instances_opt_combined_v3] 123 ms +- 4 ms: 1.03x faster deltablue: Mean +- std dev: [cpython_master] 7.35 ms +- 0.20 ms -> [immortal_instances_opt_combined_v3] 7.74 ms +- 0.41 ms: 1.05x slower django_template: Mean +- std dev: [cpython_master] 62.2 ms +- 2.0 ms -> [immortal_instances_opt_combined_v3] 63.7 ms +- 2.4 ms: 1.02x slower fannkuch: Mean +- std dev: [cpython_master] 664 ms +- 15 ms -> [immortal_instances_opt_combined_v3] 677 ms +- 18 ms: 1.02x slower float: Mean +- std dev: [cpython_master] 128 ms +- 4 ms -> [immortal_instances_opt_combined_v3] 135 ms +- 7 ms: 1.05x slower go: Mean +- std dev: [cpython_master] 244 ms +- 10 ms -> [immortal_instances_opt_combined_v3] 228 ms +- 14 ms: 1.07x faster json_dumps: Mean +- std dev: [cpython_master] 19.2 ms +- 0.7 ms -> [immortal_instances_opt_combined_v3] 20.1 ms +- 0.8 ms: 1.04x slower logging_format: Mean +- std dev: [cpython_master] 10.4 us +- 0.3 us -> [immortal_instances_opt_combined_v3] 11.0 us +- 0.4 us: 1.06x slower logging_silent: Mean +- std dev: [cpython_master] 201 ns +- 8 ns -> [immortal_instances_opt_combined_v3] 205 ns +- 7 ns: 1.02x slower logging_simple: Mean +- std dev: [cpython_master] 9.77 us +- 0.32 us -> [immortal_instances_opt_combined_v3] 10.2 us +- 0.4 us: 1.04x slower nqueens: Mean +- std dev: [cpython_master] 159 ms +- 5 ms -> [immortal_instances_opt_combined_v3] 154 ms +- 3 ms: 1.03x faster pickle: Mean +- std dev: [cpython_master] 16.0 us +- 0.5 us -> [immortal_instances_opt_combined_v3] 16.6 us +- 0.7 us: 1.04x slower pickle_dict: Mean +- std dev: [cpython_master] 37.3 us +- 0.6 us -> [immortal_instances_opt_combined_v3] 35.6 us +- 2.1 us: 1.05x faster pidigits: Mean +- std dev: [cpython_master] 284 ms +- 15 ms -> [immortal_instances_opt_combined_v3] 273 ms +- 9 ms: 1.04x faster pyflate: Mean +- std dev: [cpython_master] 770 ms +- 28 ms -> [immortal_instances_opt_combined_v3] 746 ms +- 24 ms: 1.03x faster python_startup: Mean +- std dev: [cpython_master] 12.6 ms +- 0.4 ms -> [immortal_instances_opt_combined_v3] 11.6 ms +- 0.6 ms: 1.08x faster python_startup_no_site: Mean +- std dev: [cpython_master] 8.89 ms +- 0.39 ms -> [immortal_instances_opt_combined_v3] 8.11 ms +- 0.43 ms: 1.10x faster raytrace: Mean +- std dev: [cpython_master] 529 ms +- 16 ms -> [immortal_instances_opt_combined_v3] 544 ms +- 17 ms: 1.03x slower scimark_fft: Mean +- std dev: [cpython_master] 571 ms +- 12 ms -> [immortal_instances_opt_combined_v3] 589 ms +- 13 ms: 1.03x slower scimark_lu: Mean +- std dev: [cpython_master] 195 ms +- 6 ms -> [immortal_instances_opt_combined_v3] 205 ms +- 7 ms: 1.05x slower scimark_sor: Mean +- std dev: [cpython_master] 211 ms +- 6 ms -> [immortal_instances_opt_combined_v3] 216 ms +- 6 ms: 1.03x slower scimark_sparse_mat_mult: Mean +- std dev: [cpython_master] 8.28 ms +- 0.40 ms -> [immortal_instances_opt_combined_v3] 8.66 ms +- 0.30 ms: 1.05x slower sympy_expand: Mean +- std dev: [cpython_master] 878 ms +- 34 ms -> [immortal_instances_opt_combined_v3] 850 ms +- 27 ms: 1.03x faster sympy_integrate: Mean +- std dev: [cpython_master] 35.2 ms +- 1.0 ms -> [immortal_instances_opt_combined_v3] 36.6 ms +- 1.5 ms: 1.04x slower sympy_sum: Mean +- std dev: [cpython_master] 291 ms +- 13 ms -> [immortal_instances_opt_combined_v3] 309 ms +- 7 ms: 1.06x slower sympy_str: Mean +- std dev: [cpython_master] 514 ms +- 11 ms -> [immortal_instances_opt_combined_v3] 535 ms +- 13 ms: 1.04x slower telco: Mean +- std dev: [cpython_master] 10.4 ms +- 0.2 ms -> [immortal_instances_opt_combined_v3] 10.7 ms +- 0.5 ms: 1.03x slower unpack_sequence: Mean +- std dev: [cpython_master] 77.9 ns +- 2.3 ns -> [immortal_instances_opt_combined_v3] 70.6 ns +- 2.0 ns: 1.10x faster unpickle_list: Mean +- std dev: [cpython_master] 6.77 us +- 0.25 us -> [immortal_instances_opt_combined_v3] 6.95 us +- 0.22 us: 1.03x slower xml_etree_parse: Mean +- std dev: [cpython_master] 245 ms +- 6 ms -> [immortal_instances_opt_combined_v3] 238 ms +- 5 ms: 1.03x faster xml_etree_generate: Mean +- std dev: [cpython_master] 146 ms +- 4 ms -> [immortal_instances_opt_combined_v3] 143 ms +- 6 ms: 1.02x faster xml_etree_process: Mean +- std dev: [cpython_master] 102 ms +- 2 ms -> [immortal_instances_opt_combined_v3] 100 ms +- 3 ms: 1.02x faster Benchmark hidden because not significant (18): hexiom, html5lib, json_loads, meteor_contest, nbody, pathlib, pickle_list, pickle_pure_python, regex_compile, regex_dna, regex_effbot, regex_v8, richards, scimark_monte_carlo, spectral_norm, unpickle, unpickle_pure_python, xml_etree_iterparse Geometric mean: 1.00x faster https://bugs.python.org/issue40255

Open Graph Description: This is an optimization on top of PR19474. It combines PR31488, PR31489, and PR31490 into a single change to measure the combined performance benefits. These results do not change too much from wha...

X Description: This is an optimization on top of PR19474. It combines PR31488, PR31489, and PR31490 into a single change to measure the combined performance benefits. These results do not change too much from wha...

Opengraph URL: https://github.com/python/cpython/pull/31491

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