ClementiGroup/mlcg
Nov 2024 – Feb 2025
2 Months active
Languages Used
C++CUDAPython
Technical Skills
C++CUDA ProgrammingGraph AlgorithmsPerformance OptimizationPyTorchPython
Paul Mifsud
PROFILE
Paul Mifsud
Paul Mifsud developed GPU-accelerated graph proximity features for the ClementiGroup/mlcg repository, focusing on scalable radius graph and distance calculations. He implemented optimized CUDA kernels with Python wrappers, enabling fast, large-scale graph computations and improving data processing throughput. In a subsequent refactor, Paul unified the radius calculation logic to support PyTorch autograd, allowing backpropagation through radius computations in SchNet training loops. He removed legacy CUDA code, updated the file structure, and expanded test coverage to ensure training compatibility. His work demonstrated depth in CUDA programming, Python, and performance optimization, resulting in more maintainable, extensible, and efficient graph neural network pipelines.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
1,253
Activity Months2
Your Network
55 people
Work History
February 2025
3 Commits • 1 Features
Feb 1, 2025February 2025 – ClementiGroup/mlcg: SchNet Radius Calculation Refactor with Autograd Integration. Delivered a backward-compatible radius computation path enabling backpropagation through radius in training loops, and consolidated the radius logic into a single maintainable code path. Removed the legacy CUDA-based radius implementation and refactored CUDA kernel calls with updated file structure. Updated tests to cover backward functionality and training-loop compatibility, establishing a solid foundation for end-to-end SchNet training. Overall, this work improves training stability, maintainability, and prepares groundwork for future kernel optimizations.
3 Commits • 1 Features
Feb 1, 2025February 2025 – ClementiGroup/mlcg: SchNet Radius Calculation Refactor with Autograd Integration. Delivered a backward-compatible radius computation path enabling backpropagation through radius in training loops, and consolidated the radius logic into a single maintainable code path. Removed the legacy CUDA-based radius implementation and refactored CUDA kernel calls with updated file structure. Updated tests to cover backward functionality and training-loop compatibility, establishing a solid foundation for end-to-end SchNet training. Overall, this work improves training stability, maintainability, and prepares groundwork for future kernel optimizations.
February 2025
November 2024
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Focused on delivering GPU-accelerated graph capabilities for ClementiGroup/mlcg. Implemented CUDA-accelerated radius graph and distance calculations with new kernels and Python wrappers to enable fast, scalable proximity computations. No major bugs reported this month; feature delivery was stable. Overall impact: faster proximity analyses, ability to handle larger graphs with higher throughput, contributing to faster data processing and model pipelines. Technologies demonstrated: CUDA kernel development, GPU acceleration, Python bindings/wrappers, GPU-accelerated graph algorithms, performance optimization.
November 2024
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Focused on delivering GPU-accelerated graph capabilities for ClementiGroup/mlcg. Implemented CUDA-accelerated radius graph and distance calculations with new kernels and Python wrappers to enable fast, scalable proximity computations. No major bugs reported this month; feature delivery was stable. Overall impact: faster proximity analyses, ability to handle larger graphs with higher throughput, contributing to faster data processing and model pipelines. Technologies demonstrated: CUDA kernel development, GPU acceleration, Python bindings/wrappers, GPU-accelerated graph algorithms, performance optimization.
Activity
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Quality Metrics
Correctness82.6%
Maintainability80.0%
Architecture72.4%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CUDAPython
Technical Skills
AutogradBuild systemsC++CUDA ProgrammingCUDA programmingDebuggingGraph AlgorithmsGraph Neural NetworksMachine LearningPerformance OptimizationPyTorchPythonTesting
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