pytorch/FBGEMM
Dec 2024 – Aug 2025
5 Months active
Languages Used
C++Python
Technical Skills
CUDAGPU ComputingLow-level ProgrammingPerformance BenchmarkingPerformance OptimizationPyTorch
Shintaro Iwasaki
PROFILE
Shintaro Iwasaki
Siwasaki contributed to the pytorch/FBGEMM repository by developing and refining benchmarking infrastructure for large-scale embedding workloads, with a focus on GPU computing and MTIA hardware integration. Over five months, Siwasaki implemented new benchmarks, enhanced performance instrumentation, and addressed device initialization issues to improve reliability and reproducibility. Using C++, CUDA, and Python, Siwasaki fixed integer overflow bugs in kernel code, introduced cache precision controls, and streamlined device management for MTIA-accelerated evaluation. The work demonstrated depth in low-level programming, code refactoring, and debugging, resulting in more accurate performance metrics and stable benchmarking workflows for both CUDA and MTIA environments.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
7Total
Bugs
3
Commits
7
Features
3
Lines of code
303
Activity Months5
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2875 peopleSame Organization
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Aahan AggarwalMember
Aliaksei AndreyeuMember
Arjun ChaturvediMember
Aaron PollackMember
Aaryaman SagarMember
Aashay GaikwadMember
Ajanthan AsogamoorthyMember
Work History
August 2025
1 Commits
Aug 1, 2025August 2025 monthly summary for pytorch/FBGEMM focusing on stability and benchmarking reliability. The major delivered change this month was a fix to the VBE Benchmark MTIA initialization by restoring the required 'device' argument, ensuring the benchmark runs correctly and deterministically. This addressed a reproducibility issue in MTIA-related benchmarks and reduced downstream debugging in CI and local development.
1 Commits
Aug 1, 2025August 2025 monthly summary for pytorch/FBGEMM focusing on stability and benchmarking reliability. The major delivered change this month was a fix to the VBE Benchmark MTIA initialization by restoring the required 'device' argument, ensuring the benchmark runs correctly and deterministically. This addressed a reproducibility issue in MTIA-related benchmarks and reduced downstream debugging in CI and local development.
August 2025
May 2025
1 Commits
May 1, 2025May 2025 monthly summary: Delivered a targeted bug fix for the VBE Benchmark MTIA path in PyTorch FBGEMM, improving device initialization reliability and benchmark stability. The fix adds device=get_device() to the SplitTableBatchedEmbeddings constructor, preventing device-related errors and enabling consistent MTIA benchmark runs. The work includes a dedicated commit and aligns with our device-management best practices, enhancing reproducibility and trust in performance measurements across MTIA devices.
May 2025
1 Commits
May 1, 2025May 2025 monthly summary: Delivered a targeted bug fix for the VBE Benchmark MTIA path in PyTorch FBGEMM, improving device initialization reliability and benchmark stability. The fix adds device=get_device() to the SplitTableBatchedEmbeddings constructor, preventing device-related errors and enabling consistent MTIA benchmark runs. The work includes a dedicated commit and aligns with our device-management best practices, enhancing reproducibility and trust in performance measurements across MTIA devices.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for pytorch/FBGEMM. Delivered MTIA support for VBE benchmarks on CUDA by configuring EmbeddingLocation to DEVICE when the compute device is CUDA, enabling MTIA-accelerated evaluation of VBE kernels. No major bugs fixed this month. Impact: improved CUDA-based benchmarking throughput and faster experimentation for VBE kernels. Skills demonstrated: CUDA programming, MTIA integration, VBE benchmarking, EmbeddingLocation configuration, code integration and review, and repository maintenance.
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for pytorch/FBGEMM. Delivered MTIA support for VBE benchmarks on CUDA by configuring EmbeddingLocation to DEVICE when the compute device is CUDA, enabling MTIA-accelerated evaluation of VBE kernels. No major bugs fixed this month. Impact: improved CUDA-based benchmarking throughput and faster experimentation for VBE kernels. Skills demonstrated: CUDA programming, MTIA integration, VBE benchmarking, EmbeddingLocation configuration, code integration and review, and repository maintenance.
April 2025
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025: TBE benchmarking enhancements and MTIA readiness in pytorch/FBGEMM. Implemented cache_precision for the device_with_spec TBE benchmark, performed a targeted cleanup of the SplitTableBatchedEmbeddingBagsCodegen constructor to improve maintainability, and updated device selection logic to surface MTIA hardware information for testing. These changes improve benchmarking fidelity, extend hardware coverage, and lay groundwork for broader MTIA validation, aligning with performance and hardware compatibility goals.
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025: TBE benchmarking enhancements and MTIA readiness in pytorch/FBGEMM. Implemented cache_precision for the device_with_spec TBE benchmark, performed a targeted cleanup of the SplitTableBatchedEmbeddingBagsCodegen constructor to improve maintainability, and updated device selection logic to surface MTIA hardware information for testing. These changes improve benchmarking fidelity, extend hardware coverage, and lay groundwork for broader MTIA validation, aligning with performance and hardware compatibility goals.
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for pytorch/FBGEMM highlighting key delivered work, bug fixes, and impact. Focused on correctness, benchmark instrumentation, and performance visibility to inform optimization decisions for large-scale embeddings workloads.
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for pytorch/FBGEMM highlighting key delivered work, bug fixes, and impact. Focused on correctness, benchmark instrumentation, and performance visibility to inform optimization decisions for large-scale embeddings workloads.
December 2024
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Quality Metrics
Correctness87.2%
Maintainability88.6%
Architecture84.2%
Performance74.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
BenchmarkingCUDACode RefactoringDebuggingGPU ComputingLow-level ProgrammingMachine Learning EngineeringPerformance BenchmarkingPerformance OptimizationPyTorchPython
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