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
Over a two-month period, Wei Su contributed to the pytorch/FBGEMM repository by developing features focused on CPU benchmarking and kernel parameterization. He enabled multi-processing in CPU TBE micro-benchmarks, introducing parallel execution across worker processes and adding command-line controls for experiment configuration and performance data collection. Using Python scripting and shell scripting, he enhanced the benchmarking framework to support stress-testing under diverse workloads. In C++, he expanded autovec TBE kernel parameter specialization, refactored macros for maintainability, and improved default behaviors for output bit rates. The work demonstrated depth in low-level optimization and performance engineering, addressing both scalability and code quality.
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
2401 people
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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