NVIDIA/CUDALibrarySamples
Feb 2025 – Aug 2025
4 Months active
Languages Used
C++CUDA
Technical Skills
Build Systems (CMake)C++CUDA ProgrammingGPU ComputingLinear AlgebraPerformance Optimization
Pawel Grabowski
PROFILE
Pawel Grabowski
Over four months, Piotr Grabowski developed and maintained advanced GPU computing examples in the NVIDIA/CUDALibrarySamples repository, focusing on linear algebra, compression, and numerical methods. He implemented new GEMM and DGEMM emulation workflows using C++ and CUDA, leveraging template metaprogramming and performance benchmarking to support mixed-precision and cross-architecture scenarios. His work included reorganizing and updating sample sets for cuBLASDx, cuSolverDx, cuRANDDx, and MathDx, ensuring compatibility with evolving CUDA toolkits and improving code maintainability. By integrating build system enhancements and detailed error analysis, Piotr enabled researchers and developers to evaluate and optimize GPU-accelerated algorithms more effectively.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
7
Lines of code
15,477
Activity Months4
Your Network
1388 people
Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for NVIDIA/CUDALibrarySamples: Delivered an update to the MathDx samples to align with library version 25.06.1 and ensure CUDA compatibility. This work included updating example descriptions, build configurations, and CUDA version compatibility checks to support newer CUDA toolkits. The change set is captured in commit 8fbe63692db027588e73efbe83cd4e60bb170064. Major bugs fixed: none reported this month for this repo. Overall impact: improved compatibility with newer CUDA toolkits, reduced risk for downstream projects, and improved maintainability of MathDx samples. Technologies/skills demonstrated: CUDA toolkit compatibility, library versioning, build configuration management, sample maintenance, and change tracing.
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for NVIDIA/CUDALibrarySamples: Delivered an update to the MathDx samples to align with library version 25.06.1 and ensure CUDA compatibility. This work included updating example descriptions, build configurations, and CUDA version compatibility checks to support newer CUDA toolkits. The change set is captured in commit 8fbe63692db027588e73efbe83cd4e60bb170064. Major bugs fixed: none reported this month for this repo. Overall impact: improved compatibility with newer CUDA toolkits, reduced risk for downstream projects, and improved maintainability of MathDx samples. Technologies/skills demonstrated: CUDA toolkit compatibility, library versioning, build configuration management, sample maintenance, and change tracing.
August 2025
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 — NVIDIA/CUDALibrarySamples: Focused feature delivery and evaluation for the Ozaki scheme-based DGEMM emulation example. Delivered an end-to-end approach that demonstrates emulating FP64 DGEMM using lower-precision slices, including decomposition of FP64 matrices into int8 slices, slice-based GEMM, and reconstruction with high accuracy. The work includes preprocessing, slicing, and fused matrix-multiplication kernels, accompanied by performance and error analysis against native cuBLAS. No major bugs fixed this month; emphasis was on delivering a complete, evaluable example and its artifacts to enable benchmarking and future optimizations. This enhances the repository as a reference for mixed-precision DGEMM exploration and cuBLASDx-based experimentation, driving research and potential performance insights on supported GPUs.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 — NVIDIA/CUDALibrarySamples: Focused feature delivery and evaluation for the Ozaki scheme-based DGEMM emulation example. Delivered an end-to-end approach that demonstrates emulating FP64 DGEMM using lower-precision slices, including decomposition of FP64 matrices into int8 slices, slice-based GEMM, and reconstruction with high accuracy. The work includes preprocessing, slicing, and fused matrix-multiplication kernels, accompanied by performance and error analysis against native cuBLAS. No major bugs fixed this month; emphasis was on delivering a complete, evaluable example and its artifacts to enable benchmarking and future optimizations. This enhances the repository as a reference for mixed-precision DGEMM exploration and cuBLASDx-based experimentation, driving research and potential performance insights on supported GPUs.
May 2025
4 Commits • 4 Features
May 1, 2025May 2025 monthly summary for NVIDIA/CUDALibrarySamples focused on delivering API-aligned example sets across CUDA libraries and improving discoverability, performance measurement scaffolding, and cross-architecture compatibility.
4 Commits • 4 Features
May 1, 2025May 2025 monthly summary for NVIDIA/CUDALibrarySamples focused on delivering API-aligned example sets across CUDA libraries and improving discoverability, performance measurement scaffolding, and cross-architecture compatibility.
May 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for NVIDIA/CUDALibrarySamples focused on delivering a major feature update to CuBLASDx samples (version 0.3.1). The changes introduce new GEMM examples and refinements to existing ones, enabling support for decoupled precision, custom layouts, and comprehensive performance benchmarks across varied precisions and architectures. The update includes improved error checking and integration of new CUDA features to optimize performance. All work tracked under the commit 4d4be5d3361d76f83deae848a6a607711832ccfb (Update cuBLASDx samples to 0.3.1).
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for NVIDIA/CUDALibrarySamples focused on delivering a major feature update to CuBLASDx samples (version 0.3.1). The changes introduce new GEMM examples and refinements to existing ones, enabling support for decoupled precision, custom layouts, and comprehensive performance benchmarks across varied precisions and architectures. The update includes improved error checking and integration of new CUDA features to optimize performance. All work tracked under the commit 4d4be5d3361d76f83deae848a6a607711832ccfb (Update cuBLASDx samples to 0.3.1).
Activity
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Quality Metrics
Correctness91.4%
Maintainability87.2%
Architecture90.0%
Performance83.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CUDA
Technical Skills
Algorithm ImplementationBuild System ConfigurationBuild SystemsBuild Systems (CMake)C++CMakeCUDACUDA DevelopmentCUDA ProgrammingCode OrganizationCode RefactoringCompression AlgorithmsExample DevelopmentGPU ComputingLibrary Integration
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