rapidsai/cugraph
Jul 2025 – Sep 2025
2 Months active
Languages Used
C++CUDA
Technical Skills
Algorithm AnalysisC++ DevelopmentCUDA ProgrammingGraph AlgorithmsPerformance OptimizationCUDA C++
Howard Huang
PROFILE
Howard Huang
Worked on the rapidsai/cugraph repository to enhance Betweenness Centrality computation for large-scale graphs. Addressed correctness by fixing delta value updates, ensuring accurate centrality scores across multiple data types, and introduced comprehensive testing for robustness. Improved performance through frontier-based boundary detection using binary search and pre-allocated buffers. Later, implemented concurrent multi-source BFS and backward pass, enabling parallel Betweenness Centrality calculations for multiple sources and optimizing GPU utilization and memory usage. Refactored centrality logic for better maintainability and extensibility. Leveraged C++, CUDA, and advanced algorithm analysis to deliver scalable, high-throughput analytics for graph processing in GPU-accelerated environments.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
889
Activity Months2
Your Network
31 peopleShared Repositories
31
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for rapidsai/cugraph focused on performance-oriented concurrency in centrality computation. Implemented multi-source BFS and backward pass for parallel Betweenness Centrality across multiple sources, refactored centrality logic, and achieved memory and GPU utilization improvements. This release centers on delivering scalable analytics for large graphs with improved throughput and maintainability.
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for rapidsai/cugraph focused on performance-oriented concurrency in centrality computation. Implemented multi-source BFS and backward pass for parallel Betweenness Centrality across multiple sources, refactored centrality logic, and achieved memory and GPU utilization improvements. This release centers on delivering scalable analytics for large graphs with improved throughput and maintainability.
September 2025
July 2025
1 Commits
Jul 1, 2025July 2025 monthly summary for rapidsai/cugraph focusing on Betweenness Centrality calculation improvements. Highlighted business value through correctness and performance improvements across large graphs, with robust testing and data-type coverage.
July 2025
1 Commits
Jul 1, 2025July 2025 monthly summary for rapidsai/cugraph focusing on Betweenness Centrality calculation improvements. Highlighted business value through correctness and performance improvements across large graphs, with robust testing and data-type coverage.
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CUDA
Technical Skills
Algorithm AnalysisC++ DevelopmentCUDA C++CUDA ProgrammingGPU ComputingGraph AlgorithmsParallel AlgorithmsPerformance Optimization
Repositories Contributed To
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