facebookexperimental/triton
May 2026 – Jun 2026
2 Months active
Languages Used
C++MLIRPython
Technical Skills
CUDACompiler designGPU ProgrammingGPU programmingMachine LearningPerformance Optimization
Janani Sriram
PROFILE
Janani Sriram
Worked on performance-focused enhancements to matrix multiplication and kernel scheduling in the facebookexperimental/triton and pytorch/pytorch repositories. Developed features such as budget-aware warp allocation, memory usage optimizations for addmm broadcasts, and TMA descriptor support for fused pointwise epilogues, enabling more efficient GPU utilization and broader configuration support. Implemented multi-CTA MMA support with per-MMA cross-CTA barriers and dependency checks to improve GEMM throughput and correctness. Leveraged CUDA, Python, and MLIR to optimize kernel performance, ensure robust scheduling, and maintain code quality. The work emphasized scalable GPU programming and contributed to more efficient deep learning operations across both repositories.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
4
Lines of code
1,738
Activity Months2
Your Network
1654 peopleSame Organization
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agelunMember
Arnav AghavMember
Pooja AgarwalMember
Work History
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026: Delivered performance-focused enhancements to GEMM in facebookexperimental/triton by enabling multi-CTA MMAs with per-MMA cross-CTA barriers and adding a dependency check to prevent unsupported multi-MMA configurations. These changes improve potential GEMM throughput for 2-CTA scenarios while safeguarding correctness by rejecting problematic data dependencies. The work strengthens Triton kernel scheduling and contributes to more robust, scalable matrix multiplication on multi-CTA architectures.
1 Commits • 1 Features
Jun 1, 2026June 2026: Delivered performance-focused enhancements to GEMM in facebookexperimental/triton by enabling multi-CTA MMAs with per-MMA cross-CTA barriers and adding a dependency check to prevent unsupported multi-MMA configurations. These changes improve potential GEMM throughput for 2-CTA scenarios while safeguarding correctness by rejecting problematic data dependencies. The work strengthens Triton kernel scheduling and contributes to more robust, scalable matrix multiplication on multi-CTA architectures.
June 2026
May 2026
6 Commits • 3 Features
May 1, 2026May 2026 performance-focused monthly summary across facebookexperimental/triton and pytorch/pytorch. Focused on business value and technical achievements in kernel scheduling, memory efficiency, and Inductor/Triton integration. Highlights include budget-aware warp allocation and specialization, memory-usage optimizations enabling more configurations, and end-to-end TMA descriptor support for fused pointwise epilogues in Triton kernels.
May 2026
6 Commits • 3 Features
May 1, 2026May 2026 performance-focused monthly summary across facebookexperimental/triton and pytorch/pytorch. Focused on business value and technical achievements in kernel scheduling, memory efficiency, and Inductor/Triton integration. Highlights include budget-aware warp allocation and specialization, memory-usage optimizations enabling more configurations, and end-to-end TMA descriptor support for fused pointwise epilogues in Triton kernels.
Activity
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Quality Metrics
Correctness91.4%
Maintainability80.0%
Architecture88.6%
Performance85.6%
AI Usage34.4%
Skills & Technologies
Programming Languages
C++MLIRPython
Technical Skills
CUDACompiler designDeep LearningGPU ProgrammingGPU programmingMLIRMachine LearningMatrix MultiplicationPerformance OptimizationPerformance optimizationPyTorchPython DevelopmentTritonUnit Testing
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
pytorch/pytorch
May 2026 – May 2026
1 Month active
Languages Used
Python
Technical Skills
CUDADeep LearningGPU ProgrammingMachine LearningPerformance OptimizationPyTorch