ScalingIntelligence/KernelBench
Oct 2024 – Jun 2025
7 Months active
Languages Used
PythonSQLC++MarkdownShellCUDA
Technical Skills
Backend DevelopmentBatch ProcessingBenchmarkingCPU ParallelismCUDACaching
Simon Guo
PROFILE
Simon Guo
Simon Guo developed and maintained the KernelBench repository, delivering a robust benchmarking and evaluation suite for AI-generated CUDA kernels and LLM workflows. Over seven months, Simon engineered distributed multi-GPU evaluation pipelines, integrated multi-backend LLM support, and implemented hardware-aware prompt engineering to optimize kernel generation and performance analysis. His work included refactoring for modularity, enhancing error handling, and establishing reproducible benchmarking baselines using Python, CUDA, and PyTorch. Simon also improved documentation and project organization, enabling clearer onboarding and usage. The depth of his contributions advanced KernelBench’s reliability, scalability, and extensibility, supporting both research experimentation and production-grade performance evaluation.
Overall Statistics
Feature vs Bugs
91%Features
Repository Contributions
76Total
Bugs
3
Commits
76
Features
32
Lines of code
10,577
Activity Months7
Your Network
113 people
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025: KernelBench Documentation Refresh and Caesar Framework Section delivered, improving onboarding and framework clarity. Reworked README for better title clarity, navigation, and added a dedicated Caesar multi-turn framework section; refreshed roadmap and known usage with new entries. All changes captured in commit 21fbe5a642898cd60b8f60c7aefb43d475e11f33 (Update README.md).
1 Commits • 1 Features
Jun 1, 2025June 2025: KernelBench Documentation Refresh and Caesar Framework Section delivered, improving onboarding and framework clarity. Reworked README for better title clarity, navigation, and added a dedicated Caesar multi-turn framework section; refreshed roadmap and known usage with new entries. All changes captured in commit 21fbe5a642898cd60b8f60c7aefb43d475e11f33 (Update README.md).
June 2025
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for ScalingIntelligence/KernelBench focused on feature delivery and benchmarking readiness. Delivered B200 profiling data artifacts to support performance analysis and benchmarking, and added B200-specific torch.compile configurations to enable and optimize hardware acceleration. No explicit bug fixes were reported in this scope, but profiling and config enhancements remove prior gaps and improve system stability for measurements.
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for ScalingIntelligence/KernelBench focused on feature delivery and benchmarking readiness. Delivered B200 profiling data artifacts to support performance analysis and benchmarking, and added B200-specific torch.compile configurations to enable and optimize hardware acceleration. No explicit bug fixes were reported in this scope, but profiling and config enhancements remove prior gaps and improve system stability for measurements.
February 2025
8 Commits • 5 Features
Feb 1, 2025February 2025: Focused on expanding KernelBench's inference ecosystem, improving modularity, and accelerating evaluation. Delivered multi-backend support (Fireworks, Claude) via Archon orchestration, enhanced benchmarking capabilities, and enriched documentation, translating technical work into measurable business value such as broader model compatibility, faster experimentation cycles, and clearer usage guidance.
8 Commits • 5 Features
Feb 1, 2025February 2025: Focused on expanding KernelBench's inference ecosystem, improving modularity, and accelerating evaluation. Delivered multi-backend support (Fireworks, Claude) via Archon orchestration, enhanced benchmarking capabilities, and enriched documentation, translating technical work into measurable business value such as broader model compatibility, faster experimentation cycles, and clearer usage guidance.
February 2025
January 2025
11 Commits • 5 Features
Jan 1, 2025January 2025 monthly summary for ScalingIntelligence/KernelBench focused on delivering business-value features for model-guided CUDA kernel generation, strengthening debugging reliability, and enabling hardware-aware performance optimization. The work advances kernel quality, reduces debugging time, and provides data-driven baselines to drive GPU investments and configurations across hardware.
January 2025
11 Commits • 5 Features
Jan 1, 2025January 2025 monthly summary for ScalingIntelligence/KernelBench focused on delivering business-value features for model-guided CUDA kernel generation, strengthening debugging reliability, and enabling hardware-aware performance optimization. The work advances kernel quality, reduces debugging time, and provides data-driven baselines to drive GPU investments and configurations across hardware.
December 2024
17 Commits • 3 Features
Dec 1, 2024December 2024: KernelBench delivered a production-ready performance benchmarking and evaluation suite, establishing a baseline for timings, inspection, and model prompts. A unified framework for batch and single-sample code generation and evaluation with dataset integration (including HuggingFace) was implemented, enabling end-to-end benchmarking of code-generation pipelines. Documentation and project organization were improved to support release readiness and clarity.
17 Commits • 3 Features
Dec 1, 2024December 2024: KernelBench delivered a production-ready performance benchmarking and evaluation suite, establishing a baseline for timings, inspection, and model prompts. A unified framework for batch and single-sample code generation and evaluation with dataset integration (including HuggingFace) was implemented, enabling end-to-end benchmarking of code-generation pipelines. Documentation and project organization were improved to support release readiness and clarity.
December 2024
November 2024
24 Commits • 14 Features
Nov 1, 2024Month: 2024-11. KernelBench delivered a suite of cross-backend LLM experimentation capabilities, strengthened performance benchmarking, and improved code quality and reproducibility. Notable progress includes multi-backend LLM support with seamless integration into query_llm, baseline timing tooling and test harness for reliable performance baselines, and a major codebase refactor with API config presets that simplify experimentation workflows. Enhancements to observability (logging and formatting) and reproducibility (problem hashing) improve maintainability and reliability. Foundational unit testing and Hugging Face scripting groundwork establish a durable path for future automation and quality guarantees.
November 2024
24 Commits • 14 Features
Nov 1, 2024Month: 2024-11. KernelBench delivered a suite of cross-backend LLM experimentation capabilities, strengthened performance benchmarking, and improved code quality and reproducibility. Notable progress includes multi-backend LLM support with seamless integration into query_llm, baseline timing tooling and test harness for reliable performance baselines, and a major codebase refactor with API config presets that simplify experimentation workflows. Enhancements to observability (logging and formatting) and reproducibility (problem hashing) improve maintainability and reliability. Foundational unit testing and Hugging Face scripting groundwork establish a durable path for future automation and quality guarantees.
October 2024
13 Commits • 3 Features
Oct 1, 2024October 2024 (2024-10) monthly summary for ScalingIntelligence/KernelBench. The month delivered a set of reliability, performance, and scalability improvements to the evaluation pipeline, driving measurable business value through faster feedback loops, reduced downtime, and improved traceability. Key outcomes include hardened runtime and metadata handling to prevent crashes and improve error reporting, integrated CUDA timing and performance statistics for data-driven optimization, a distributed multi-GPU batch evaluation framework with timeouts and enhanced reporting for end-to-end throughput, and kernel compilation isolation with caching to dramatically speed up evaluation. These changes reduce evaluation time, improve reproducibility, and enable larger-scale experiments with better resource utilization. Business value and impact: - Increased reliability reduces debugging time and production incidents. - Quantifiable performance metrics enable targeted optimizations and faster iteration cycles. - Scalable, device-targeted evaluation unlocks higher throughput across GPUs for large-scale experiments. - Build-time caching and isolated compilation cut evaluation readiness times, accelerating the feedback loop for kernel development. Technologies/skills demonstrated: - CUDA timing, PyTorch integration, and performance profiling in evaluation loops. - Distributed compute design with device-targeted evaluation, work queues, and timeouts. - Robust error handling, logging, and metadata management for production-grade experiments. - Per-kernel compilation isolation and caching to reduce build conflicts and accelerate evals.
13 Commits • 3 Features
Oct 1, 2024October 2024 (2024-10) monthly summary for ScalingIntelligence/KernelBench. The month delivered a set of reliability, performance, and scalability improvements to the evaluation pipeline, driving measurable business value through faster feedback loops, reduced downtime, and improved traceability. Key outcomes include hardened runtime and metadata handling to prevent crashes and improve error reporting, integrated CUDA timing and performance statistics for data-driven optimization, a distributed multi-GPU batch evaluation framework with timeouts and enhanced reporting for end-to-end throughput, and kernel compilation isolation with caching to dramatically speed up evaluation. These changes reduce evaluation time, improve reproducibility, and enable larger-scale experiments with better resource utilization. Business value and impact: - Increased reliability reduces debugging time and production incidents. - Quantifiable performance metrics enable targeted optimizations and faster iteration cycles. - Scalable, device-targeted evaluation unlocks higher throughput across GPUs for large-scale experiments. - Build-time caching and isolated compilation cut evaluation readiness times, accelerating the feedback loop for kernel development. Technologies/skills demonstrated: - CUDA timing, PyTorch integration, and performance profiling in evaluation loops. - Distributed compute design with device-targeted evaluation, work queues, and timeouts. - Robust error handling, logging, and metadata management for production-grade experiments. - Per-kernel compilation isolation and caching to reduce build conflicts and accelerate evals.
October 2024
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Quality Metrics
Correctness83.8%
Maintainability82.2%
Architecture81.0%
Performance72.6%
AI Usage33.4%
Skills & Technologies
Programming Languages
C++CUDAMarkdownPythonSQLShell
Technical Skills
AI DevelopmentAI Model InteractionAPI IntegrationBackend DevelopmentBatch ProcessingBenchmarkingBug FixBuild SystemsCPU ParallelismCUDACUDA DevelopmentCUDA ProgrammingCachingCode AnalysisCode Compilation
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