jax-ml/jax
Mar 2025 – Mar 2025
1 Month active
Languages Used
C++Python
Technical Skills
C++CUDAGPU ProgrammingMLIRPython
Amir Samani
PROFILE
Amir Samani
Over a two-month period, Abhishek Samani developed high-performance GPU features across the flashinfer-ai/flashinfer and jax-ml/jax repositories. He built a distributed persistent batched dense GEMM kernel for NVIDIA Blackwell using CUTE DSL and CUDA, enabling efficient matrix multiplication with Tensor Memory Access and all-reduce epilogues for scalable distributed workloads. In jax-ml/jax and ROCm/jax, he implemented element-wise reduction operations in asynchronous shared-to-global memory copy paths, updating the lowering, API, and test coverage to ensure correctness across floating-point types. His work demonstrated deep expertise in GPU programming, low-level optimization, and distributed systems, addressing complex performance and scalability challenges.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
2,818
Activity Months2
Your Network
1814 people
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 highlights: Delivered a distributed persistent batched dense GEMM kernel for NVIDIA Blackwell in the flashinfer-ai/flashinfer repository using CUTE DSL. The kernel supports Tensor Memory Access (TMA), Blackwell tcgen05.mma for matrix multiply-accumulate, and an all-reduce epilogue with multimem instructions for scalable distributed workloads. It includes persistent tile scheduling and warp specialization to optimize resource utilization across GPUs. Commit: c8d849ee02380c5180f787b217a98785ea684513 ([cute_dsl] add gemm + all reduce (two_shot) (#1695)).
1 Commits • 1 Features
Sep 1, 2025September 2025 highlights: Delivered a distributed persistent batched dense GEMM kernel for NVIDIA Blackwell in the flashinfer-ai/flashinfer repository using CUTE DSL. The kernel supports Tensor Memory Access (TMA), Blackwell tcgen05.mma for matrix multiply-accumulate, and an all-reduce epilogue with multimem instructions for scalable distributed workloads. It includes persistent tile scheduling and warp specialization to optimize resource utilization across GPUs. Commit: c8d849ee02380c5180f787b217a98785ea684513 ([cute_dsl] add gemm + all reduce (two_shot) (#1695)).
September 2025
March 2025
2 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary focusing on key accomplishments in jax-ml/jax and ROCm/jax. The month centered on enabling and validating reduction operations in the asynchronous copy path from shared memory (SMEM) to global memory (GMEM), with a strong emphasis on test coverage and API/lowering alignment.
March 2025
2 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary focusing on key accomplishments in jax-ml/jax and ROCm/jax. The month centered on enabling and validating reduction operations in the asynchronous copy path from shared memory (SMEM) to global memory (GMEM), with a strong emphasis on test coverage and API/lowering alignment.
Activity
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Quality Metrics
Correctness93.4%
Maintainability80.0%
Architecture90.0%
Performance86.6%
AI Usage26.6%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++CUDACUTE DSLDistributed SystemsGPU ProgrammingHigh-Performance ComputingJAXLow-Level OptimizationMLIRMatrix MultiplicationOptimizationPython
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
ROCm/jax
Mar 2025 – Mar 2025
1 Month active
Languages Used
C++Python
Technical Skills
CUDAGPU ProgrammingJAXLow-Level OptimizationMLIR
flashinfer-ai/flashinfer
Sep 2025 – Sep 2025
1 Month active
Languages Used
C++Python
Technical Skills
CUDACUTE DSLDistributed SystemsGPU ProgrammingHigh-Performance ComputingMatrix Multiplication