ScalingIntelligence/KernelBench
Jan 2025 – Feb 2025
2 Months active
Languages Used
CSSHTMLJavaScriptPythonShellJinja
Technical Skills
CSSCUDACloud ComputingData AnalysisDevOpsFrontend Development
William Hu
PROFILE
William Hu
Over a two-month period, contributed to ScalingIntelligence/KernelBench by developing a modal-based GPU benchmarking workflow and enhancing performance analysis tooling. Built UI scaffolding and automated evaluation scripts using Python, JavaScript, and CUDA, enabling reproducible benchmarking and standardized GPU performance metrics. Refactored modal support for maintainability and introduced data collection tools to streamline benchmarking processes. Delivered a kernel evaluation tool with caching strategies and CUDA error handling, supporting objective kernel comparisons. Integrated geometric mean speed ratio and fastp scoring into analysis pipelines, and added unit tests to ensure scoring accuracy. These efforts improved reproducibility, maintainability, and clarity in performance benchmarking workflows.
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
125 peopleShared Repositories
12
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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