
Yash Agarwal contributed to the ROCm/composable_kernel and ROCm/aiter repositories, focusing on high-performance GPU kernel development and optimization for machine learning workloads. He engineered modular GEMM and pooling kernels with support for non-contiguous memory layouts, introduced a flexible post-GEMM processing framework, and enhanced kernel configurability for Mixture-of-Experts models. Using C++, CUDA, and Python, Yash refactored core components for maintainability, implemented robust testing and documentation, and addressed critical bugs affecting tuning workflows. His work improved throughput, reliability, and deployment flexibility, demonstrating depth in low-level programming, template metaprogramming, and performance tuning for production-scale data processing and linear algebra operations.
In March 2026, ROCm/aiter delivered targeted performance improvements for MoE workloads and expanded configurability for CKTile GEMM MOE, focusing on reliability, throughput, and deployment flexibility. Key work included kernel-level MoE optimizations with robust inter_dim handling, enabling asm instances for idim=192 and defaults that favor 1-stage ASM kernels, plus configurable CKTile MOE tuning with blockPerCu and kernel_name-based dispatch, complemented by CLI controls and tuners for customer-defined configurations. These changes increase GPU utilization for large MoE models, reduce runtime variability, and provide measurable business value through faster inference and easier deployment across hardware generations.
In March 2026, ROCm/aiter delivered targeted performance improvements for MoE workloads and expanded configurability for CKTile GEMM MOE, focusing on reliability, throughput, and deployment flexibility. Key work included kernel-level MoE optimizations with robust inter_dim handling, enabling asm instances for idim=192 and defaults that favor 1-stage ASM kernels, plus configurable CKTile MOE tuning with blockPerCu and kernel_name-based dispatch, complemented by CLI controls and tuners for customer-defined configurations. These changes increase GPU utilization for large MoE models, reduce runtime variability, and provide measurable business value through faster inference and easier deployment across hardware generations.
February 2026 monthly summary for ROCm/aiter focusing on stability and reliability improvements to the tuning workflow. No new features were released this month; the primary deliverable was a critical bug fix that ensures the tuning process for the fmoe model executes correctly by correcting the tuning script file path. The work reduces runtime failures and debugging time for tuning campaigns, enabling more predictable CI/CD and faster iteration.
February 2026 monthly summary for ROCm/aiter focusing on stability and reliability improvements to the tuning workflow. No new features were released this month; the primary deliverable was a critical bug fix that ensures the tuning process for the fmoe model executes correctly by correcting the tuning script file path. The work reduces runtime failures and debugging time for tuning campaigns, enabling more predictable CI/CD and faster iteration.
Month: 2025-12 — Delivered high-throughput GEMM improvements and a modular post-GEMM processing framework in ROCm/composable_kernel. The work focused on performance, correctness, and flexibility to handle real-world data layouts, driving measurable business value for production workloads.
Month: 2025-12 — Delivered high-throughput GEMM improvements and a modular post-GEMM processing framework in ROCm/composable_kernel. The work focused on performance, correctness, and flexibility to handle real-world data layouts, driving measurable business value for production workloads.
November 2025 (Repo: ROCm/composable_kernel): Key features delivered around pooling kernel usage and documentation improvements, plus performance-oriented refinements to the pooling example. No major bugs fixed this month; maintenance focused on documentation quality and example optimization to accelerate onboarding and experimentation. Impact includes clearer understanding of 2D/3D pooling kernel transformations via README and a Mermaid diagram, and improved example performance via tile size tuning, warmup/repeat iterations, and optimized block/thread configuration. Technologies/skills demonstrated include C++/HIP kernel knowledge, performance tuning, and clear technical documentation.
November 2025 (Repo: ROCm/composable_kernel): Key features delivered around pooling kernel usage and documentation improvements, plus performance-oriented refinements to the pooling example. No major bugs fixed this month; maintenance focused on documentation quality and example optimization to accelerate onboarding and experimentation. Impact includes clearer understanding of 2D/3D pooling kernel transformations via README and a Mermaid diagram, and improved example performance via tile size tuning, warmup/repeat iterations, and optimized block/thread configuration. Technologies/skills demonstrated include C++/HIP kernel knowledge, performance tuning, and clear technical documentation.
October 2025 monthly recap for ROCm/composable_kernel focused on delivering end-to-end enhancements to pooling and reductions, with emphasis on business value and reliability. Key changes include pooling forward operation for CK_TILE with 2D/3D kernels, indexing support for max/absmax pooling, corresponding tests and documentation, and a refactor of descriptor transformations to enable future indexing. Additionally, identity values for Max and AbsMax reductions were corrected to ensure mathematically correct results, improving overall correctness and downstream trust in results.
October 2025 monthly recap for ROCm/composable_kernel focused on delivering end-to-end enhancements to pooling and reductions, with emphasis on business value and reliability. Key changes include pooling forward operation for CK_TILE with 2D/3D kernels, indexing support for max/absmax pooling, corresponding tests and documentation, and a refactor of descriptor transformations to enable future indexing. Additionally, identity values for Max and AbsMax reductions were corrected to ensure mathematically correct results, improving overall correctness and downstream trust in results.
August 2025 monthly summary focusing on key accomplishments in StreamHPC/rocm-libraries. Delivered two major features with stabilizing fixes and improved reuse and performance, enhancing downstream adoption and GPU efficiency.
August 2025 monthly summary focusing on key accomplishments in StreamHPC/rocm-libraries. Delivered two major features with stabilizing fixes and improved reuse and performance, enhancing downstream adoption and GPU efficiency.
Concise monthly summary for 2025-07 focusing on key features delivered, major bugs fixed, overall impact and accomplishments, and technologies demonstrated. Includes business value and technical detail with explicit deliverables and references.
Concise monthly summary for 2025-07 focusing on key features delivered, major bugs fixed, overall impact and accomplishments, and technologies demonstrated. Includes business value and technical detail with explicit deliverables and references.

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