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
Developed a multi-GPU CUDA vector operations example for the NVIDIA/cuda-python repository, demonstrating vector addition and subtraction across two GPUs with careful attention to memory management and result validation. Leveraged C++ and Python to implement robust cross-GPU memory handling, ensuring efficient parallel utilization and correctness of computations. Enhanced the codebase by refining kernel definitions, optimizing memory allocation, and improving docstrings for better readability and usability. This work provides a clear, maintainable example that supports scalable, high-performance GPU workloads, facilitating faster onboarding and adoption by downstream teams seeking to leverage parallel computing and GPU programming within the cuda-python ecosystem.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
178
Activity Months1
Your Network
1672 peopleSame Organization
@nvidia.com1629
Shared Repositories
43
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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