ROCm/composable_kernel
Dec 2025 – Jan 2026
2 Months active
Languages Used
C++
Technical Skills
Algorithm OptimizationC++CUDAGPU ProgrammingQuantizationDeep learning
Erwin Terpstra
PROFILE
Erwin Terpstra
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
3
Lines of code
15,422
Activity Months2
Your Network
92 people
Work History
January 2026
5 Commits • 2 Features
Jan 1, 2026Concise monthly summary for 2026-01 focusing on key accomplishments for ROCm/composable_kernel. Overview: - Month: 2026-01 - Core focus: advancing RDNA4 GEMM capabilities, expanding data-type support, and strengthening testing and reliability to enable higher throughput and broader hardware coverage. Key achievements (top 3-5): - RDNA4 GEMM: delivered grouped and batched GEMMs with FastGELU, tile loop optimizations, bias permutation, ReLU support, FP8 checks, and expanded testing coverage. - FP4 (a4w4) support in GEMM AB quantization: added FP4 decoding, CPU verification, and tests; integrated into block-scale GEMM workflow. - Batched GEMM enhancements: implemented batched gemm add and relu paths; refined device kernels (gridwise WMMA), parameter handling, and validation across architectures; improved profiler and test stability. - Quality fixes and reliability: resolved FP8 enablement issues on RDNA3, aligned template parameters, and expanded test scenarios to catch edge cases earlier. - Impact: increased throughput and capability on RDNA4, broader data-type support (FP4/FP8), and stronger validation pipelines accelerating hardware-targeted optimizations. Context: - Repository: ROCm/composable_kernel - Focused on delivering business value through performance improvements, expanded hardware support, and robust testing to shorten time-to-market for new GPU generations.
5 Commits • 2 Features
Jan 1, 2026Concise monthly summary for 2026-01 focusing on key accomplishments for ROCm/composable_kernel. Overview: - Month: 2026-01 - Core focus: advancing RDNA4 GEMM capabilities, expanding data-type support, and strengthening testing and reliability to enable higher throughput and broader hardware coverage. Key achievements (top 3-5): - RDNA4 GEMM: delivered grouped and batched GEMMs with FastGELU, tile loop optimizations, bias permutation, ReLU support, FP8 checks, and expanded testing coverage. - FP4 (a4w4) support in GEMM AB quantization: added FP4 decoding, CPU verification, and tests; integrated into block-scale GEMM workflow. - Batched GEMM enhancements: implemented batched gemm add and relu paths; refined device kernels (gridwise WMMA), parameter handling, and validation across architectures; improved profiler and test stability. - Quality fixes and reliability: resolved FP8 enablement issues on RDNA3, aligned template parameters, and expanded test scenarios to catch edge cases earlier. - Impact: increased throughput and capability on RDNA4, broader data-type support (FP4/FP8), and stronger validation pipelines accelerating hardware-targeted optimizations. Context: - Repository: ROCm/composable_kernel - Focused on delivering business value through performance improvements, expanded hardware support, and robust testing to shorten time-to-market for new GPU generations.
January 2026
December 2025
2 Commits • 1 Features
Dec 1, 2025Month: 2025-12 performance-focused monthly summary for ROCm/composable_kernel. Key features delivered, major bugs fixed, overall impact, and technologies demonstrated. This month centered on advancing quantized grouped GEMM (CK) capabilities, improving testing discipline, and tuning performance for quantized workloads on AMD GPUs.
December 2025
2 Commits • 1 Features
Dec 1, 2025Month: 2025-12 performance-focused monthly summary for ROCm/composable_kernel. Key features delivered, major bugs fixed, overall impact, and technologies demonstrated. This month centered on advancing quantized grouped GEMM (CK) capabilities, improving testing discipline, and tuning performance for quantized workloads on AMD GPUs.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage48.6%
Skills & Technologies
Programming Languages
C++
Technical Skills
Algorithm OptimizationC++CUDADeep learningGPU ProgrammingGPU programmingMatrix MultiplicationMatrix multiplicationMatrix operationsPerformance OptimizationPerformance optimizationQuantizationTensor operationsTesting
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