ZJUSCT/HPC101
Jul 2025 – Aug 2025
2 Months active
Languages Used
CC++CUDAMakefileMarkdownPython
Technical Skills
BenchmarkingC/C++ OptimizationCUDA ProgrammingDeep Learning OptimizationDocumentationHigh-Performance Computing (HPC)
Xingyu Yang
PROFILE
Xingyu Yang
Xuyang developed and optimized the Winograd convolution algorithm for the ZJUSCT/HPC101 repository, delivering both CPU and GPU implementations with comprehensive documentation. Over two months, Xuyang focused on high-performance computing and deep learning optimization, using C, C++, and CUDA to improve algorithm efficiency and reliability. The work included implementing NaN-aware correctness checks to prevent invalid outputs from passing verification, enhancing the robustness of numerical analysis in HPC workloads. By ensuring cross-path consistency between CPU and GPU variants and providing clear commit-level documentation, Xuyang improved traceability and reduced the risk of silent failures, demonstrating depth in performance optimization and debugging.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
1
Lines of code
1,682
Activity Months2
Your Network
47 people
Work History
August 2025
2 Commits
Aug 1, 2025Concise monthly summary for 2025-08 highlighting key deliverables, fixes, impact, and skills demonstrated for ZJUSCT/HPC101. Summary of key outcomes: - NaN-aware correctness verification for the Winograd algorithm and its CPU variant, preventing NaN outputs from being treated as valid results and increasing reliability of numerical checks. - Cross-path consistency improvements between the Winograd algorithm and the Winograd CPU version, reducing divergence in verification paths. - Clear traceability of changes with commit-level documentation for major fixes. Impact: - Enhanced reliability of numeric verification in HPC workloads, reducing risk of silent failures and improving confidence in results used for performance optimization and validation. - Faster issue resolution due to explicit NaN handling and consistent behavior across execution paths. Technologies/skills demonstrated: - Debugging, numerical verification, NaN handling, cross-component validation (GPU/CPU paths), and commit-driven traceability. Key achievements: - Implemented NaN-aware correctness check for Winograd verification, preventing NaN outputs from passing verification (applied to both Winograd algorithm and Winograd CPU version). - Ensured cross-path consistency between GPU algorithm and CPU variant, reducing divergence in outcomes. - Documented fixes with explicit commits for traceability.
2 Commits
Aug 1, 2025Concise monthly summary for 2025-08 highlighting key deliverables, fixes, impact, and skills demonstrated for ZJUSCT/HPC101. Summary of key outcomes: - NaN-aware correctness verification for the Winograd algorithm and its CPU variant, preventing NaN outputs from being treated as valid results and increasing reliability of numerical checks. - Cross-path consistency improvements between the Winograd algorithm and the Winograd CPU version, reducing divergence in verification paths. - Clear traceability of changes with commit-level documentation for major fixes. Impact: - Enhanced reliability of numeric verification in HPC workloads, reducing risk of silent failures and improving confidence in results used for performance optimization and validation. - Faster issue resolution due to explicit NaN handling and consistent behavior across execution paths. Technologies/skills demonstrated: - Debugging, numerical verification, NaN handling, cross-component validation (GPU/CPU paths), and commit-driven traceability. Key achievements: - Implemented NaN-aware correctness check for Winograd verification, preventing NaN outputs from passing verification (applied to both Winograd algorithm and Winograd CPU version). - Ensured cross-path consistency between GPU algorithm and CPU variant, reducing divergence in outcomes. - Documented fixes with explicit commits for traceability.
August 2025
July 2025
1 Commits • 1 Features
Jul 1, 2025Delivered Final HPC 101 Project: Winograd Convolution optimization and HPCG Benchmark, with full documentation and CPU/GPU implementations. Stable, reproducible release; no major bugs reported.
July 2025
1 Commits • 1 Features
Jul 1, 2025Delivered Final HPC 101 Project: Winograd Convolution optimization and HPCG Benchmark, with full documentation and CPU/GPU implementations. Stable, reproducible release; no major bugs reported.
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture83.4%
Performance80.0%
AI Usage26.6%
Skills & Technologies
Programming Languages
CC++CUDAMakefileMarkdownPython
Technical Skills
Algorithm ImplementationAlgorithm OptimizationBenchmarkingBug FixingC/C++ OptimizationCUDA ProgrammingDeep Learning OptimizationDocumentationGPU ComputingHigh-Performance Computing (HPC)Numerical AnalysisParallel ProgrammingPerformance Optimization
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