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 and rapidsai/cuvs repositories, focusing on scalable graph analytics and build system reliability. Improved Betweenness Centrality calculations by fixing delta value updates and introducing frontier-based boundary detection, leveraging C++ and CUDA for robust performance across large graphs. Developed a concurrent multi-source backward pass, enabling parallel computation of centrality metrics and optimizing GPU utilization. Refactored centrality logic for maintainability and extensibility. In rapidsai/cuvs, enforced C++20 standards in CMake to ensure compatibility with CUDA examples and modern API headers, resolving build issues and standardizing development environments. Emphasized algorithm analysis, performance optimization, and GPU computing throughout.
Overall Statistics
Feature vs Bugs
33%Features
Repository Contributions
3Total
Bugs
2
Commits
3
Features
1
Lines of code
894
Activity Months3
Your Network
70 peopleWork History
June 2026
1 Commits
Jun 1, 2026June 2026 monthly summary for rapidsai/cuvs focused on build-system hardening and CUDA/CAGRA Dataset API compatibility. Key work unified C++20 requirements for CUDA examples and stabilized builds, enabling downstream features that depend on modern C++ features and the CAGRA API headers.
1 Commits
Jun 1, 2026June 2026 monthly summary for rapidsai/cuvs focused on build-system hardening and CUDA/CAGRA Dataset API compatibility. Key work unified C++20 requirements for CUDA examples and stabilized builds, enabling downstream features that depend on modern C++ features and the CAGRA API headers.
June 2026
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.
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.
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.
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
Activity
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Quality Metrics
Correctness93.4%
Maintainability86.6%
Architecture93.4%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CUDA
Technical Skills
Algorithm AnalysisC++ DevelopmentC++ developmentCMake configurationCUDA C++CUDA ProgrammingCUDA programmingGPU ComputingGraph AlgorithmsParallel AlgorithmsPerformance Optimization
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
rapidsai/cuvs
Jun 2026 – Jun 2026
1 Month active
Languages Used
C++CUDA
Technical Skills
C++ developmentCMake configurationCUDA programming