deepmodeling/abacus-develop
Nov 2024 – Nov 2024
1 Month active
Languages Used
C++Python
Technical Skills
C++Linear AlgebraNumerical MethodsPybind11PythonScientific Computing
a1henu
PROFILE
A1henu
Chenhsu Bai developed and integrated a Conjugate Gradient (CG) solver into the pyabacus.hsolver module within the deepmodeling/abacus-develop repository. He implemented the CG algorithm in C++ and exposed it to Python using Pybind11, ensuring seamless integration with the existing solver framework. His work included updating example scripts and expanding the test suite to validate the new solver path and maintain regression safety. By enabling CG as a solver option, Chenhsu Bai enhanced the scalability and performance of the module for large linear systems, demonstrating depth in scientific computing, numerical methods, and cross-language development between C++ and Python.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
406
Activity Months1
Your Network
25 peopleWork History
November 2024
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Delivered Conjugate Gradient (CG) solver integration in the pyabacus.hsolver module for the deepmodeling/abacus-develop repository. Implemented CG in C++ with Python bindings, integrated it into the existing solver framework, and updated examples and tests to support and exercise the CG path. No major bugs fixed this period. This work broadens solver capabilities for larger systems, enabling faster convergence and more robust performance in production workflows.
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Delivered Conjugate Gradient (CG) solver integration in the pyabacus.hsolver module for the deepmodeling/abacus-develop repository. Implemented CG in C++ with Python bindings, integrated it into the existing solver framework, and updated examples and tests to support and exercise the CG path. No major bugs fixed this period. This work broadens solver capabilities for larger systems, enabling faster convergence and more robust performance in production workflows.
November 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++Linear AlgebraNumerical MethodsPybind11PythonScientific Computing
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