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Amir Samani

PROFILE

Amir Samani

Over three months, Arjun Samani developed high-performance GPU features across jax-ml/jax, flashinfer-ai/flashinfer, and jeejeelee/vllm. He enabled element-wise reduction operations for asynchronous shared-to-global memory transfers in jax-ml/jax, updating the lowering logic and API while expanding test coverage for floating-point types using C++ and CUDA. In flashinfer-ai/flashinfer, he delivered a distributed persistent batched dense GEMM kernel for NVIDIA Blackwell GPUs with CUTE DSL, optimizing matrix multiplication and all-reduce operations for distributed systems. For jeejeelee/vllm, he unified CUDA stream usage in NCCL graph capture and replay, improving determinism and throughput in PyTorch-based GPU workflows.

Overall Statistics

Feature vs Bugs

100%Features

Repository Contributions

4Total
Bugs
0
Commits
4
Features
4
Lines of code
2,868
Activity Months3

Work History

December 2025

1 Commits • 1 Features

Dec 1, 2025

Month: 2025-12 — Focused on improving NCCL graph performance and consistency in the jeejeelee/vllm repository. Implemented a unified CUDA stream for graph capture and replay, enabling more deterministic NCCL graph operations and reducing stream-switch overhead. This change lays groundwork for higher throughput in GPU-accelerated workloads and simplifies performance tuning.

September 2025

1 Commits • 1 Features

Sep 1, 2025

September 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)).

March 2025

2 Commits • 2 Features

Mar 1, 2025

March 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

Correctness95.0%
Maintainability80.0%
Architecture92.6%
Performance90.0%
AI Usage25.0%

Skills & Technologies

Programming Languages

C++Python

Technical Skills

C++CUDACUTE DSLDistributed SystemsGPU ProgrammingHigh-Performance ComputingJAXLow-Level OptimizationMLIRMatrix MultiplicationOptimizationParallel ComputingPerformance OptimizationPyTorchPython

Repositories Contributed To

4 repos

Overview of all repositories you've contributed to across your timeline

jax-ml/jax

Mar 2025 Mar 2025
1 Month active

Languages Used

C++Python

Technical Skills

C++CUDAGPU ProgrammingMLIRPython

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

jeejeelee/vllm

Dec 2025 Dec 2025
1 Month active

Languages Used

Python

Technical Skills

CUDAParallel ComputingPerformance OptimizationPyTorch