pymc-devs/pytensor
Nov 2024 – Nov 2024
1 Month active
Languages Used
NumPyPython
Technical Skills
Code OptimizationGraph RewritingLinear AlgebraSymbolic ComputationTensorFlow
Tanish
PROFILE
Tanish
Worked on the pymc-devs/pytensor repository to optimize determinant computation performance by introducing a specialized rewrite path leveraging SLogDet. Refactored the slogdet function to emit a naive expression, enabling targeted graph rewrites for improved efficiency and maintainability in determinant-related operations. This approach allows future optimizations to be applied more flexibly, aligning with long-term codebase goals. The work focused on code optimization and graph rewriting, utilizing Python and NumPy to enhance symbolic computation workflows. By restructuring determinant calculations, the changes reduced compute time and provided a foundation for further performance improvements in linear algebra operations within the library.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
251
Activity Months1
Your Network
50 peopleWork History
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024: Implemented determinant computation performance optimization in pytensor via SLogDet specialization. Refactored slogdet to emit a naive expression suitable for later optimization through targeted rewrites, enabling a dedicated determinant path that leverages SLogDet. This work reduces compute time for determinant-related operations and improves flexibility for future performance rewrites, aligning with long-term maintainability goals.
1 Commits • 1 Features
Nov 1, 2024November 2024: Implemented determinant computation performance optimization in pytensor via SLogDet specialization. Refactored slogdet to emit a naive expression suitable for later optimization through targeted rewrites, enabling a dedicated determinant path that leverages SLogDet. This work reduces compute time for determinant-related operations and improves flexibility for future performance rewrites, aligning with long-term maintainability goals.
November 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
NumPyPython
Technical Skills
Code OptimizationGraph RewritingLinear AlgebraSymbolic ComputationTensorFlow
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