Add scaled-up feature extraction pipeline (#704) - #704
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Summary: Pull Request resolved: facebookresearch#704 Add mock feature extraction pipeline to FeedSim with large-scale code generation for I-cache and frontend pressure. 27 genuinely diverse code patterns (derived from studying 696 production feature extractors) generate ~700 variants × 1000 copies = ~700K unique functions at install time. Key components: - 6 hand-written extractors based on production leaf function profiling - 27 pattern-specific code generators (P01-P27) producing genuinely different instruction sequences (different branch topologies, loop nesting, data access patterns, code sizes from 10 to 2300 lines) - Flat shuffled dispatch: all copy function pointers shuffled into one vector, iterated sequentially per request for maximum I-cache pressure - DLRM medium/large model generation on-server during install - Configurable via --num_stories, --extractors_per_story, --feature_complexity Results on T1_BGM (Bergamo, 176 cores): 500K calls/req: L1 I-Cache MPKI 21.34 (prod target 21), IPC 0.69 (prod 0.6-0.8) 100K calls/req: Frontend Bound 23.5%, IPC 1.22, QPS 242 Results on T11_GRC_ARM (Grace, 72 cores): 100K calls/req: IPC 0.52, L1 I-Cache MPKI 15.91 Medium DLRM + 100K calls: IPC 1.03 (prod target 1.05) Reviewed By: YifanYuan3, charles-typ Differential Revision: D97022149
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Summary: Pull Request resolved: facebookresearch#704 Add mock feature extraction pipeline to FeedSim with large-scale code generation for I-cache and frontend pressure. 27 genuinely diverse code patterns (derived from studying 696 production feature extractors) generate ~700 variants × 1000 copies = ~700K unique functions at install time. Key components: - 6 hand-written extractors based on production leaf function profiling - 27 pattern-specific code generators (P01-P27) producing genuinely different instruction sequences (different branch topologies, loop nesting, data access patterns, code sizes from 10 to 2300 lines) - Flat shuffled dispatch: all copy function pointers shuffled into one vector, iterated sequentially per request for maximum I-cache pressure - DLRM medium/large model generation on-server during install - Configurable via --num_stories, --extractors_per_story, --feature_complexity Results on T1_BGM (Bergamo, 176 cores): 500K calls/req: L1 I-Cache MPKI 21.34 (prod target 21), IPC 0.69 (prod 0.6-0.8) 100K calls/req: Frontend Bound 23.5%, IPC 1.22, QPS 242 Results on T11_GRC_ARM (Grace, 72 cores): 100K calls/req: IPC 0.52, L1 I-Cache MPKI 15.91 Medium DLRM + 100K calls: IPC 1.03 (prod target 1.05) Reviewed By: YifanYuan3, charles-typ Differential Revision: D97022149
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Summary: Pull Request resolved: #704 Add mock feature extraction pipeline to FeedSim with large-scale code generation for I-cache and frontend pressure. 27 genuinely diverse code patterns (derived from studying 696 production feature extractors) generate ~700 variants × 1000 copies = ~700K unique functions at install time. Key components: - 6 hand-written extractors based on production leaf function profiling - 27 pattern-specific code generators (P01-P27) producing genuinely different instruction sequences (different branch topologies, loop nesting, data access patterns, code sizes from 10 to 2300 lines) - Flat shuffled dispatch: all copy function pointers shuffled into one vector, iterated sequentially per request for maximum I-cache pressure - DLRM medium/large model generation on-server during install - Configurable via --num_stories, --extractors_per_story, --feature_complexity Results on T1_BGM (Bergamo, 176 cores): 500K calls/req: L1 I-Cache MPKI 21.34 (prod target 21), IPC 0.69 (prod 0.6-0.8) 100K calls/req: Frontend Bound 23.5%, IPC 1.22, QPS 242 Results on T11_GRC_ARM (Grace, 72 cores): 100K calls/req: IPC 0.52, L1 I-Cache MPKI 15.91 Medium DLRM + 100K calls: IPC 1.03 (prod target 1.05) Reviewed By: YifanYuan3, charles-typ Differential Revision: D97022149 fbshipit-source-id: 528f46e151be2f6a90478fac327fb4e0a202eff2
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Summary:
Add mock feature extraction pipeline to FeedSim with large-scale code
generation for I-cache and frontend pressure. 27 genuinely diverse code
patterns (derived from studying 696 production feature extractors) generate
~700 variants × 1000 copies = ~700K unique functions at install time.
Key components:
different instruction sequences (different branch topologies, loop
nesting, data access patterns, code sizes from 10 to 2300 lines)
vector, iterated sequentially per request for maximum I-cache pressure
Results on T1_BGM (Bergamo, 176 cores):
500K calls/req: L1 I-Cache MPKI 21.34 (prod target 21), IPC 0.69 (prod 0.6-0.8)
100K calls/req: Frontend Bound 23.5%, IPC 1.22, QPS 242
Results on T11_GRC_ARM (Grace, 72 cores):
100K calls/req: IPC 0.52, L1 I-Cache MPKI 15.91
Medium DLRM + 100K calls: IPC 1.03 (prod target 1.05)
Reviewed By: YifanYuan3, charles-typ
Differential Revision: D97022149