ScalingIntelligence/KernelBench
Jan 2025 – Feb 2025
2 Months active
Languages Used
CSSHTMLJavaScriptPythonShellJinja
Technical Skills
CSSCUDACloud ComputingData AnalysisDevOpsFrontend Development
William Hu
PROFILE
William Hu
Will Hu developed and enhanced GPU benchmarking workflows for the ScalingIntelligence/KernelBench repository over a two-month period. He built a modal-based benchmarking system with UI scaffolding, automated evaluation scripts, and baseline timing data, enabling standardized and reproducible performance analysis across multiple GPU models. Using Python, CUDA, and JavaScript, Will refactored modal support for maintainability and introduced data collection tooling to streamline benchmarking. He also delivered a kernel evaluation tool with caching strategies and CUDA error handling, integrated advanced scoring metrics, and added unit tests to ensure reliability. His work provided a robust foundation for objective GPU performance evaluation and optimization.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
6
Lines of code
29,970
Activity Months2
Your Network
113 people
Work History
February 2025
7 Commits • 2 Features
Feb 1, 2025February 2025 (2025-02) – KernelBench performance benchmarking and analysis enhancements. Delivered core tooling and scoring improvements to support reproducible kernel evaluation and clearer performance insights.
7 Commits • 2 Features
Feb 1, 2025February 2025 (2025-02) – KernelBench performance benchmarking and analysis enhancements. Delivered core tooling and scoring improvements to support reproducible kernel evaluation and clearer performance insights.
February 2025
January 2025
4 Commits • 4 Features
Jan 1, 2025January 2025 performance summary for ScalingIntelligence/KernelBench: Delivered a modal-based GPU benchmark workflow with UI scaffolding, automated evaluation script, and baseline timing data. Established baseline GPU performance metrics across select GPUs. Refactored modal support to improve maintainability. Added modal baselines and data collection tooling to enable reproducible benchmarking. This work standardizes GPU benchmarking, reduces manual setup time, and supports data-driven decisions for product optimization.
January 2025
4 Commits • 4 Features
Jan 1, 2025January 2025 performance summary for ScalingIntelligence/KernelBench: Delivered a modal-based GPU benchmark workflow with UI scaffolding, automated evaluation script, and baseline timing data. Established baseline GPU performance metrics across select GPUs. Refactored modal support to improve maintainability. Added modal baselines and data collection tooling to enable reproducible benchmarking. This work standardizes GPU benchmarking, reduces manual setup time, and supports data-driven decisions for product optimization.
Activity
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Quality Metrics
Correctness82.6%
Maintainability81.8%
Architecture78.2%
Performance84.6%
AI Usage23.6%
Skills & Technologies
Programming Languages
CSSHTMLJavaScriptJinjaPythonShell
Technical Skills
Algorithm DevelopmentBuild SystemsCSSCUDACaching StrategiesCloud ComputingCode EvaluationConfiguration ManagementData AnalysisDevOpsFrontend DevelopmentGPU ComputingHTMLJavaScriptModal
Repositories Contributed To
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