pytorch/FBGEMM
Jun 2025 – Mar 2026
2 Months active
Languages Used
C++Python
Technical Skills
Deep LearningGPU ComputingMachine LearningPerformance OptimizationTritonGPU Programming
Jian Jiao
PROFILE
Jian Jiao
Worked on the pytorch/FBGEMM repository to deliver two advanced GPU performance features over two months. Developed a Triton-based optimization that skips input scaling in the FP8 row-wise kernel, reducing memory overhead and improving efficiency for deep learning workloads. Later, implemented In-Kernel Broadcast Optimization for Linear Compression Embedding, introducing a three-stage pathway culminating in a warp-specialized kernel that fuses user and candidate GEMMs into a single launch. This approach enabled producer-consumer pipelining and cross-CTA synchronization, laying the foundation for higher GPU throughput. The work demonstrated expertise in C++, Python, GPU programming, kernel-level development, and test-driven engineering practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
1,628
Activity Months2
Your Network
2993 peopleSame Organization
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Aliaksei AndreyeuMember
Arjun ChaturvediMember
Aaron PollackMember
Aaryaman SagarMember
Aashay GaikwadMember
Ajanthan AsogamoorthyMember
Shared Repositories
195
Work History
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 (pytorch/FBGEMM) performance-focused month centered on delivering In-Kernel Broadcast Optimization (IKBO) for Linear Compression Embedding (LCE). Implemented a three-stage IKBO pathway culminating in a warp-specialized kernel that fuses user and candidate GEMMs into a single launch, enabling producer-consumer pipelining and cross-CTA synchronization in fbgemm_gpu/experimental. No major bugs fixed this period; all work focused on feature delivery and stability improvements around the new IKBO stack. This work lays the groundwork for substantial GPU throughput gains on LCE workloads, streamlining embeddings processing and enabling faster training/inference loops. Technologies demonstrated include C++/CUDA kernel design, TLX-fusion kernel development, Triton-based fusion, PyTorch integration, and cross-team code reviews. Commits: 6faac32ebef9cc66e2d9400cdb5bcb4923eb032b. PR references: #5521 (merged/resolved) and cross-link to #2493.
1 Commits • 1 Features
Mar 1, 2026March 2026 (pytorch/FBGEMM) performance-focused month centered on delivering In-Kernel Broadcast Optimization (IKBO) for Linear Compression Embedding (LCE). Implemented a three-stage IKBO pathway culminating in a warp-specialized kernel that fuses user and candidate GEMMs into a single launch, enabling producer-consumer pipelining and cross-CTA synchronization in fbgemm_gpu/experimental. No major bugs fixed this period; all work focused on feature delivery and stability improvements around the new IKBO stack. This work lays the groundwork for substantial GPU throughput gains on LCE workloads, streamlining embeddings processing and enabling faster training/inference loops. Technologies demonstrated include C++/CUDA kernel design, TLX-fusion kernel development, Triton-based fusion, PyTorch integration, and cross-team code reviews. Commits: 6faac32ebef9cc66e2d9400cdb5bcb4923eb032b. PR references: #5521 (merged/resolved) and cross-link to #2493.
March 2026
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025: Delivered a performance optimization in the FBGEMM FP8 path by skipping input scaling in the Triton row-wise kernel. The change reduces overhead in memory-bound scenarios, includes kernel logic changes and new tests, and is tracked by commit 6152f341f9a1da35b3286a30471ae8234c771a58 (Support skip scaling for input tensor for Triton rowwise FP8 kernel (#4362)). No major bugs fixed documented this month. Overall impact: improved FP8 performance in critical workloads, better memory efficiency, and strengthened test coverage with clear traceability. Technologies/skills demonstrated: Triton kernel optimization, FP8 workflow, kernel-level development, test-driven development, PR-based collaboration and code review.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025: Delivered a performance optimization in the FBGEMM FP8 path by skipping input scaling in the Triton row-wise kernel. The change reduces overhead in memory-bound scenarios, includes kernel logic changes and new tests, and is tracked by commit 6152f341f9a1da35b3286a30471ae8234c771a58 (Support skip scaling for input tensor for Triton rowwise FP8 kernel (#4362)). No major bugs fixed documented this month. Overall impact: improved FP8 performance in critical workloads, better memory efficiency, and strengthened test coverage with clear traceability. Technologies/skills demonstrated: Triton kernel optimization, FP8 workflow, kernel-level development, test-driven development, PR-based collaboration and code review.
Activity
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Quality Metrics
Correctness95.0%
Maintainability80.0%
Architecture90.0%
Performance95.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Deep LearningGPU ComputingGPU ProgrammingMachine LearningPerformance OptimizationTriton
Repositories Contributed To
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