ROCm/composable_kernel
Dec 2025 – Jan 2026
2 Months active
Languages Used
C++
Technical Skills
CUDAConvolution AlgorithmsGPU ProgrammingPerformance OptimizationGPU programmingPerformance optimization
ApoorvaKalyani
PROFILE
Apoorvakalyani
Apoorva contributed to the ROCm/composable_kernel repository by developing advanced grouped convolution features and optimizing tensor data conversions over a two-month period. Using C++ and CUDA, Apoorva implemented a grouped convolution backward data path leveraging WMMA v3 for both 2D and 3D cases, supporting multiple data types and layouts. The work included broadening test coverage, improving build stability, and refactoring device-level code for performance and maintainability. Additionally, Apoorva optimized the i4_to_half4_scale conversion to enhance tensor operation throughput and correctness, addressing numerical robustness across hardware architectures and ensuring reliable, maintainable code through comprehensive regression testing and build system improvements.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
5,662
Activity Months2
Your Network
97 peopleWork History
January 2026
2 Commits • 2 Features
Jan 1, 2026January 2026 focused on performance and reliability for grouped convolution and integer-to-half conversion in ROCm/composable_kernel. Delivered features emphasize throughput, stability, and hardware compatibility across architectures, with substantial test coverage and maintainability improvements.
2 Commits • 2 Features
Jan 1, 2026January 2026 focused on performance and reliability for grouped convolution and integer-to-half conversion in ROCm/composable_kernel. Delivered features emphasize throughput, stability, and hardware compatibility across architectures, with substantial test coverage and maintainability improvements.
January 2026
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for ROCm/composable_kernel focusing on feature delivery, reliability, and technical impact. Key context: Implemented grouped convolution backward data path using WMMA v3 for 2D/3D, with broad data type and layout support; expanded tests and regression coverage; improved build stability and maintainability.
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for ROCm/composable_kernel focusing on feature delivery, reliability, and technical impact. Key context: Implemented grouped convolution backward data path using WMMA v3 for 2D/3D, with broad data type and layout support; expanded tests and regression coverage; improved build stability and maintainability.
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Quality Metrics
Correctness80.0%
Maintainability73.4%
Architecture80.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
CUDAConvolution AlgorithmsGPU ProgrammingGPU programmingPerformance OptimizationPerformance optimizationTesting
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