Diff4Earth/ige-jaxathon-2025
Mar 2025 – Mar 2025
1 Month active
Languages Used
Jupyter NotebookMarkdownPython
Technical Skills
JAXLinear AlgebraMatplotlibNumPyNumerical AnalysisNumerical Methods
Hugo Frezat
PROFILE
Hugo Frezat
Hugo Frezat developed a sparse spectral solver performance analysis framework for the Diff4Earth/ige-jaxathon-2025 repository, focusing on Poisson-like problems using spectral methods. He implemented the solution in Python and JAX, leveraging NumPy and SciPy for numerical analysis and matrix operations. The project included a reproducible pipeline for setting up problems, generating sparse matrices, and benchmarking solver performance, with optional multi-GPU scaling via jax.pmap. Hugo provided a comprehensive Jupyter notebook demonstrating the solver’s Segment class, spectral transforms, and Galerkin basis approach, enabling dense versus sparse solver comparisons. The work demonstrated depth in scientific computing and performance analysis.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
959
Activity Months1
Your Network
6 people
Work History
March 2025
3 Commits • 1 Features
Mar 1, 2025March 2025 monthly work summary for Diff4Earth/ige-jaxathon-2025 focused on delivering a new sparse spectral solver performance analysis framework in JAX, with optional multi-GPU scaling and a demonstration notebook. The work established a reproducible performance analysis pipeline for Poisson-like spectral problems, including problem setup, matrix generation, GMRES testing, and preconditioner exploration, plus an option to scale across GPUs using jax.pmap. A comprehensive Jupyter notebook demonstrates a sparse spectral solver (Segment class, transforms, derivatives, matrix operations) solving Poisson equations with a Galerkin basis and includes dense-vs-sparse solver benchmarks.
3 Commits • 1 Features
Mar 1, 2025March 2025 monthly work summary for Diff4Earth/ige-jaxathon-2025 focused on delivering a new sparse spectral solver performance analysis framework in JAX, with optional multi-GPU scaling and a demonstration notebook. The work established a reproducible performance analysis pipeline for Poisson-like spectral problems, including problem setup, matrix generation, GMRES testing, and preconditioner exploration, plus an option to scale across GPUs using jax.pmap. A comprehensive Jupyter notebook demonstrates a sparse spectral solver (Segment class, transforms, derivatives, matrix operations) solving Poisson equations with a Galerkin basis and includes dense-vs-sparse solver benchmarks.
March 2025
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture86.6%
Performance86.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPython
Technical Skills
JAXLinear AlgebraMatplotlibNumPyNumerical AnalysisNumerical MethodsPartial Differential EquationsPerformance AnalysisSciPyScientific ComputingSparse MatricesSpectral Methods
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