NVIDIA/cuda-python
Dec 2024 – Dec 2024
1 Month active
Languages Used
C++Python
Technical Skills
C++ DevelopmentCUDAGPU ProgrammingParallel ComputingPythonPython Development
Amod Wani
PROFILE
Amod Wani
Amod Wagh delivered a multi-GPU CUDA vector operations example for the NVIDIA/cuda-python repository, focusing on vector addition and subtraction across two GPUs. Using C++, CUDA, and Python, Amod implemented robust cross-GPU memory management and thorough result validation to demonstrate efficient parallel utilization. The work included refining kernel definitions, optimizing memory allocation, and enhancing code readability through improved docstrings and code cleanup. By providing a clear, maintainable example, Amod enabled scalable, high-performance GPU workloads and facilitated easier onboarding for downstream teams. This contribution laid the groundwork for broader multi-GPU demonstrations and improved the usability of cuda-python’s example suite.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
178
Activity Months1
Your Network
1381 people
Work History
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 performance summary: Delivered a Multi-GPU CUDA Vector Operations Example for NVIDIA/cuda-python that demonstrates vector addition and subtraction across two GPUs with careful memory management and result validation. Enhanced readability and usability through code cleanup, improved docstrings, refined kernel definitions, and optimized memory allocation. This work strengthens support for scalable, high-performance GPU workloads and lays groundwork for broader multi-GPU demonstrations.
2 Commits • 1 Features
Dec 1, 2024December 2024 performance summary: Delivered a Multi-GPU CUDA Vector Operations Example for NVIDIA/cuda-python that demonstrates vector addition and subtraction across two GPUs with careful memory management and result validation. Enhanced readability and usability through code cleanup, improved docstrings, refined kernel definitions, and optimized memory allocation. This work strengthens support for scalable, high-performance GPU workloads and lays groundwork for broader multi-GPU demonstrations.
December 2024
Activity
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Quality Metrics
Correctness100.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++ DevelopmentCUDAGPU ProgrammingParallel ComputingPythonPython Development
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