deepmodeling/abacus-develop
Nov 2024 – Nov 2024
1 Month active
Languages Used
CC++
Technical Skills
Algorithm ImplementationC++Error HandlingFortran InterfaceNumerical IntegrationNumerical Methods
jinzx10
PROFILE
Jinzx10
During November 2024, Jinxin Zhang contributed to the deepmodeling/abacus-develop repository by enhancing the robustness and accuracy of parallel scientific computations. Zhang implemented a MaxMin-based grid batching workflow in C++ to optimize parallel grid evaluation, refactored the BLAS connector to support dsyrk_ operations, and introduced batching logic that partitions grid points by spatial proximity. To ensure numerical reliability, Zhang added unit tests validating Lebedev grid quadrature accuracy across various grid sizes and spherical harmonic orders. Additionally, improved error messaging for MPI distribution issues streamlined debugging and user guidance. The work demonstrated depth in algorithm implementation, error handling, and scientific computing.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
2
Lines of code
596
Activity Months1
Your Network
25 peopleWork History
November 2024
3 Commits • 2 Features
Nov 1, 2024Monthly summary for 2024-11 for deepmodeling/abacus-develop focused on robustness of parallel computations, performance-oriented batching, and numerical accuracy validation. Key outcomes include improved debugging for MPI distribution errors, introduction of a MaxMin-based grid batching workflow, BLAS connector refactor (dsyrk_) to support grid batching, and added unit tests ensuring Lebedev grid quadrature accuracy across varying grid sizes and spherical harmonic orders. These changes collectively enhance reliability in high-performance runs, improve grid evaluation throughput, and strengthen accuracy verification.
3 Commits • 2 Features
Nov 1, 2024Monthly summary for 2024-11 for deepmodeling/abacus-develop focused on robustness of parallel computations, performance-oriented batching, and numerical accuracy validation. Key outcomes include improved debugging for MPI distribution errors, introduction of a MaxMin-based grid batching workflow, BLAS connector refactor (dsyrk_) to support grid batching, and added unit tests ensuring Lebedev grid quadrature accuracy across varying grid sizes and spherical harmonic orders. These changes collectively enhance reliability in high-performance runs, improve grid evaluation throughput, and strengthen accuracy verification.
November 2024
Activity
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Quality Metrics
Correctness86.6%
Maintainability80.0%
Architecture73.4%
Performance73.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
CC++
Technical Skills
Algorithm ImplementationC++Error HandlingFortran InterfaceNumerical IntegrationNumerical MethodsParallel ComputingScientific ComputingSoftware DevelopmentSoftware TestingUnit Testing
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