ScalingIntelligence/KernelBench
Oct 2024 – Jul 2025
5 Months active
Languages Used
C++CUDAPythonMarkdownJSONJupyter NotebookText
Technical Skills
Backend DevelopmentCUDA ProgrammingCode GenerationData EngineeringDebuggingDeep Learning
Anne Ouyang
PROFILE
Anne Ouyang
Anne Ouyang contributed to the ScalingIntelligence/KernelBench repository, building and refining a benchmarking suite for deep learning model evaluation and performance analysis. She developed custom CUDA kernels, optimized PyTorch model architectures, and implemented profiling tools to compare compiled and non-compiled models. Anne enhanced data pipelines by curating datasets with improved metadata and streamlined Hugging Face integration, supporting reproducible experiments and robust data management. Her work included debugging loss functions, refactoring forward passes, and expanding benchmarking coverage across hardware. Using Python, CUDA, and Jupyter Notebooks, Anne delivered maintainable code that improved benchmarking accuracy, model expressiveness, and onboarding for new contributors.
Overall Statistics
Feature vs Bugs
58%Features
Repository Contributions
62Total
Bugs
13
Commits
62
Features
18
Lines of code
25,969
Activity Months5
Your Network
12 peopleShared Repositories
12
Work History
July 2025
17 Commits • 2 Features
Jul 1, 20252025-07 KernelBench monthly summary: Focused on delivering feature enhancements, model updates, and essential maintenance to improve benchmarking accuracy, reproducibility, and usability. The month produced concrete business-value by strengthening performance characterization across hardware and simplifying user onboarding.
17 Commits • 2 Features
Jul 1, 20252025-07 KernelBench monthly summary: Focused on delivering feature enhancements, model updates, and essential maintenance to improve benchmarking accuracy, reproducibility, and usability. The month produced concrete business-value by strengthening performance characterization across hardware and simplifying user onboarding.
July 2025
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 performance summary for ScalingIntelligence/KernelBench: Delivered critical data-shape alignment for hinge loss and implemented a forward-pass optimization that replaces an unnecessary global average pooling with a transposed convolution-based path and multiple pooling steps. These changes improve loss stability and model expressiveness, while potentially enhancing training efficiency and inference readiness. The work supports more reliable experimentation and faster iteration cycles, directly contributing to data quality and throughput in model evaluation.
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 performance summary for ScalingIntelligence/KernelBench: Delivered critical data-shape alignment for hinge loss and implemented a forward-pass optimization that replaces an unnecessary global average pooling with a transposed convolution-based path and multiple pooling steps. These changes improve loss stability and model expressiveness, while potentially enhancing training efficiency and inference readiness. The work supports more reliable experimentation and faster iteration cycles, directly contributing to data quality and throughput in model evaluation.
December 2024
7 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for ScalingIntelligence/KernelBench. Delivered baseline performance profiling tooling, enhanced dataset organization, and documentation improvements to strengthen reproducibility, benchmarking capabilities, and data governance.
7 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for ScalingIntelligence/KernelBench. Delivered baseline performance profiling tooling, enhanced dataset organization, and documentation improvements to strengthen reproducibility, benchmarking capabilities, and data governance.
December 2024
November 2024
31 Commits • 10 Features
Nov 1, 2024November 2024 (ScalingIntelligence/KernelBench) delivered a robust performance tooling upgrade and benchmark expansion, with a focus on reproducible results, broader coverage, and codebase stability. Key outcomes include baseline timing tooling with JSON reporting, curated benchmark subsets, and workflow enhancements that improve measurement accuracy and throughput. The month also saw successful integration of upstream contributions and critical bug fixes, strengthening model support and data handling pipelines, ultimately enabling faster, data-driven optimization cycles and more representative performance insights.
November 2024
31 Commits • 10 Features
Nov 1, 2024November 2024 (ScalingIntelligence/KernelBench) delivered a robust performance tooling upgrade and benchmark expansion, with a focus on reproducible results, broader coverage, and codebase stability. Key outcomes include baseline timing tooling with JSON reporting, curated benchmark subsets, and workflow enhancements that improve measurement accuracy and throughput. The month also saw successful integration of upstream contributions and critical bug fixes, strengthening model support and data handling pipelines, ultimately enabling faster, data-driven optimization cycles and more representative performance insights.
October 2024
5 Commits • 3 Features
Oct 1, 2024October 2024 — KernelBench: Delivered performance and reliability enhancements across ScalingIntelligence. Key features include Mish Activation CUDA kernel performance optimization (model refactor and CUDA compilation), reference architecture fetch by level/problem_id, and a temperature sweep framework for evaluating code generation. Fixed the evaluation pipeline with Test Evaluation Workflow Stabilization, standardizing RUN_NAME/problem_id and multiprocess_eval flow. Overall impact: faster inference, more reliable benchmarks, and a scalable foundation for future experiments. Technologies demonstrated: CUDA, Python, multiprocessing, data management, and robust test workflows.
5 Commits • 3 Features
Oct 1, 2024October 2024 — KernelBench: Delivered performance and reliability enhancements across ScalingIntelligence. Key features include Mish Activation CUDA kernel performance optimization (model refactor and CUDA compilation), reference architecture fetch by level/problem_id, and a temperature sweep framework for evaluating code generation. Fixed the evaluation pipeline with Test Evaluation Workflow Stabilization, standardizing RUN_NAME/problem_id and multiprocess_eval flow. Overall impact: faster inference, more reliable benchmarks, and a scalable foundation for future experiments. Technologies demonstrated: CUDA, Python, multiprocessing, data management, and robust test workflows.
October 2024
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Quality Metrics
Correctness86.8%
Maintainability87.8%
Architecture81.8%
Performance82.0%
AI Usage22.2%
Skills & Technologies
Programming Languages
C++CUDAJSONJupyter NotebookMarkdownPythonText
Technical Skills
AI model integrationBackend DevelopmentBenchmarkingBug FixCUDACUDA ProgrammingCode CleanupCode GenerationCode ManagementCode OrganizationCode RefactoringComputer VisionConfiguration ManagementConfiguration managementData Analysis
Repositories Contributed To
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