facebookexperimental/triton
Nov 2025 – Nov 2025
1 Month active
Languages Used
C++Python
Technical Skills
Backend developmentGPU programmingMachine LearningPerformance optimization
Puyan Lotfi
PROFILE
Puyan Lotfi
During November 2025, Plotfi developed and integrated BF16x3 and BF16x6 dot-product emulation for Triton matrix multiplication on MI350 GPUs in the facebookexperimental/triton repository. By implementing a GPU-agnostic path for FP32 dot emulation using three BF16 values, Plotfi enabled TF32-like precision on devices lacking native TF32 support. This approach, written in C++ and Python, leveraged backend development and GPU programming skills to deliver 60-70% performance gains for matmul workloads with BF16x3 and 10-15% with BF16x6. The work improved device compatibility, aligned Triton with HIPBLAS-like methods, and laid the foundation for broader deployment across heterogeneous GPU environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
294
Activity Months1
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Work History
November 2025
1 Commits • 1 Features
Nov 1, 2025Month 2025-11: Delivered BF16x3/BF16x6 dot-product emulation for Triton matrix multiplication on MI350 GPUs, enabling 32-bit dot precision using three BF16 values. Implemented a GPU-agnostic BF16x3 path invoked by tl.dot with input precision 'BF16x3', to provide a TF32-like capability on devices without TF32 support. Benchmarks indicate performance gains of 60-70% on matmul workloads when using 3 BF16 dots, and 10-15% with 6 BF16 dots. The change is a cherry-pick (D86786661) of Triton PR7592 (beta) with commit eca75cb85ff14197fcbc6f223eddc9ddbcdb1566. This work improves device compatibility and performance for MI350 GPUs, aligning Triton with HIPBLAS-like approaches and enabling broader deployment."
1 Commits • 1 Features
Nov 1, 2025Month 2025-11: Delivered BF16x3/BF16x6 dot-product emulation for Triton matrix multiplication on MI350 GPUs, enabling 32-bit dot precision using three BF16 values. Implemented a GPU-agnostic BF16x3 path invoked by tl.dot with input precision 'BF16x3', to provide a TF32-like capability on devices without TF32 support. Benchmarks indicate performance gains of 60-70% on matmul workloads when using 3 BF16 dots, and 10-15% with 6 BF16 dots. The change is a cherry-pick (D86786661) of Triton PR7592 (beta) with commit eca75cb85ff14197fcbc6f223eddc9ddbcdb1566. This work improves device compatibility and performance for MI350 GPUs, aligning Triton with HIPBLAS-like approaches and enabling broader deployment."
November 2025
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance100.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Backend developmentGPU programmingMachine LearningPerformance optimization
Repositories Contributed To
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