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
Contributed to the ZJUSCT/HPC101 repository by developing and optimizing a Winograd convolution implementation with both CPU and GPU support, focusing on high-performance computing and deep learning workloads. Leveraged C++, CUDA, and Python to deliver a stable, reproducible release, complete with comprehensive documentation and benchmarking using the HPCG suite. Addressed numerical reliability by implementing NaN-aware correctness checks, ensuring that invalid outputs are properly flagged during verification. Improved consistency between CPU and GPU execution paths, reducing divergence in results and enhancing validation processes. Maintained clear traceability of changes through detailed commit documentation, demonstrating strong skills in algorithm optimization and performance tuning.
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
54 peopleWork 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
Loading activity data...
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
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline