
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.
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.
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 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 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 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 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.

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