pytorch/FBGEMM
Apr 2025 – May 2025
2 Months active
Languages Used
C++Python
Technical Skills
CPU OptimizationMulti-processingPerformance BenchmarkingPython ScriptingShell ScriptingC++
Wei Su
PROFILE
Wei Su
Worked on the pytorch/FBGEMM repository to enhance CPU micro-benchmarking and kernel parameterization for machine learning workloads. Developed multi-processing support for CPU TBE micro-benchmarks, enabling parallel execution across worker processes and introducing command-line controls for experiment configuration and performance data collection. Expanded the autovec TBE kernel parameterization by increasing supported block sizes and input bit rates, refactoring macro definitions for maintainability, and improving default behaviors in kernel output settings. Leveraged C++, Python, and shell scripting to optimize performance benchmarking and low-level kernel code, focusing on performance portability, correctness, and code quality across diverse and stress-tested computational workloads.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
1,094
Activity Months2
Your Network
2993 peopleSame Organization
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Peter RongMember
Zain RizviMember
Aahan AggarwalMember
Aliaksei AndreyeuMember
Arjun ChaturvediMember
Aaron PollackMember
Aaryaman SagarMember
Aashay GaikwadMember
Ajanthan AsogamoorthyMember
Shared Repositories
195
Work History
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for pytorch/FBGEMM: Focused on feature delivery and codebase refinements to improve performance portability and correctness across varied workloads, with emphasis on autovec TBE kernel parameterization. No major bugs fixed this period; the work prioritized expanding capability and improving defaults, accompanied by code quality improvements.
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for pytorch/FBGEMM: Focused on feature delivery and codebase refinements to improve performance portability and correctness across varied workloads, with emphasis on autovec TBE kernel parameterization. No major bugs fixed this period; the work prioritized expanding capability and improving defaults, accompanied by code quality improvements.
May 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for pytorch/FBGEMM. Implemented CPU TBE Micro-benchmarks Parallel Processing by enabling multi-processing across worker processes, with CLI options to control the number of copies, sweep experiments, and pre/post-execution scripts for performance data collection. Updated benchmark functions to support parallel execution and enhanced stress-testing across varying workloads. Committed changes: c76b03d8fc518acab868cb1a898991588ca7f8c7 - Enable multi-processing in CPU TBE micro-benchmarks (#3753).
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for pytorch/FBGEMM. Implemented CPU TBE Micro-benchmarks Parallel Processing by enabling multi-processing across worker processes, with CLI options to control the number of copies, sweep experiments, and pre/post-execution scripts for performance data collection. Updated benchmark functions to support parallel execution and enhanced stress-testing across varying workloads. Committed changes: c76b03d8fc518acab868cb1a898991588ca7f8c7 - Enable multi-processing in CPU TBE micro-benchmarks (#3753).
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture86.6%
Performance93.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++CPU OptimizationLow-Level OptimizationLow-Level ProgrammingMachine Learning KernelsMulti-processingPerformance BenchmarkingPerformance OptimizationPython ScriptingShell Scripting
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