AI-Hypercomputer/tpu-recipes
Mar 2025 – Apr 2025
2 Months active
Languages Used
JAXMarkdownPythonTextNumPy
Technical Skills
BenchmarkingCloud Computing (GCP)Code RefactoringDependency ManagementDocumentationJAX
Chi Shuen Lee
PROFILE
Chi Shuen Lee
Over a two-month period, contributed to AI-Hypercomputer/tpu-recipes by developing and enhancing a unified TPU microbenchmark suite focused on matrix multiplication and high bandwidth memory bandwidth analysis. Leveraging Python, JAX, and NumPy, the work introduced robust performance profiling, improved measurement accuracy, and reproducible benchmarking through refined FLOPs calculations, dependency management, and multi-core reporting. Comprehensive documentation updates streamlined TPU VM onboarding and clarified setup instructions, enabling faster adoption and more reliable results. The technical approach emphasized end-to-end usability, with detailed output formats and integration of JAX/TPU profiler, supporting data-driven performance analysis and decision-making for TPU workloads in cloud environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
13Total
Bugs
0
Commits
13
Features
2
Lines of code
815
Activity Months2
Your Network
4751 peopleSame Organization
@google.com4703
Benedict OdaiMember
Craig IngramMember
KayyuriMember
Scott SuarezMember
Agent2Agent (A2A) BotMember
Andreas AbelMember
Aadi KapurMember
Aadish GoelMember
Aahil MehtaMember
Shared Repositories
48
Work History
April 2025
3 Commits • 1 Features
Apr 1, 20252025-04 monthly summary for AI-Hypercomputer/tpu-recipes focusing on delivering an enhanced microbenchmark suite and documentation to improve measurement accuracy and TPU VM onboarding. No major bugs fixed this month.
3 Commits • 1 Features
Apr 1, 20252025-04 monthly summary for AI-Hypercomputer/tpu-recipes focusing on delivering an enhanced microbenchmark suite and documentation to improve measurement accuracy and TPU VM onboarding. No major bugs fixed this month.
April 2025
March 2025
10 Commits • 1 Features
Mar 1, 2025March 2025 performance-focused delivery centered on the TPU benchmark suite in AI-Hypercomputer/tpu-recipes. Delivered a unified microbenchmark suite for Matrix Multiplication (MatMul) and High Bandwidth Memory (HBM) bandwidth with end-to-end setup instructions, usage examples, detailed output formats, and performance profiling via JAX/TPU profiler. The work also included dependency cleanup, correctness improvements for FLOPs measurement, improved multi-core reporting, and comprehensive documentation fixes to improve reproducibility and usability.
March 2025
10 Commits • 1 Features
Mar 1, 2025March 2025 performance-focused delivery centered on the TPU benchmark suite in AI-Hypercomputer/tpu-recipes. Delivered a unified microbenchmark suite for Matrix Multiplication (MatMul) and High Bandwidth Memory (HBM) bandwidth with end-to-end setup instructions, usage examples, detailed output formats, and performance profiling via JAX/TPU profiler. The work also included dependency cleanup, correctness improvements for FLOPs measurement, improved multi-core reporting, and comprehensive documentation fixes to improve reproducibility and usability.
Activity
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Quality Metrics
Correctness92.2%
Maintainability93.8%
Architecture89.2%
Performance89.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
JAXMarkdownNumPyPythonText
Technical Skills
BenchmarkingCloud Computing (GCP)Code RefactoringDependency ManagementDocumentationJAXMicrobenchmarkingNumPyNumerical ComputationPerformance AnalysisPerformance BenchmarkingPython DevelopmentPython ScriptingScriptingTPU Benchmarking
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