deepmodeling/abacus-develop
Nov 2024 – Mar 2026
15 Months active
Languages Used
CC++CUDAMarkdownMakefilePythonShellCMake
Technical Skills
refactoringtestingBLASC++C++ DevelopmentCI/CD
Taoni Bao
PROFILE
Taoni Bao
Baotao Ni contributed to the deepmodeling/abacus-develop repository by engineering robust features and stability improvements for RT-TDDFT workflows, focusing on GPU acceleration, parallel computing, and scientific reliability. He refactored core modules to support multi-GPU execution using C++ and CUDA, optimized memory management, and unified output conventions to streamline analysis and reduce user error. His work included enhancing MPI reliability, expanding test coverage, and modernizing linear algebra interfaces with BLAS and LAPACK integration. By addressing bugs and improving documentation, Baotao enabled scalable, reproducible simulations and faster research cycles, demonstrating depth in high-performance computing, code maintainability, and scientific software engineering.
Overall Statistics
Feature vs Bugs
52%Features
Repository Contributions
46Total
Bugs
16
Commits
46
Features
17
Lines of code
26,119
Activity Months15
Your Network
47 peopleWork History
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for deepmodeling/abacus-develop. Delivered reliability and scalability enhancements for RT-TDDFT GPU compute by fixing a device-allocation bug that caused incorrect H-matrix results, and by optimizing memory management to enable larger-scale GPU runs. Introduced multi-GPU support for RT-TDDFT using cuBLASMp and cuSOLVERMp, boosting performance and scalability on large GPU configurations. These changes were implemented via two focused commits and directly improve simulation throughput, accuracy, and capability for researchers.
2 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for deepmodeling/abacus-develop. Delivered reliability and scalability enhancements for RT-TDDFT GPU compute by fixing a device-allocation bug that caused incorrect H-matrix results, and by optimizing memory management to enable larger-scale GPU runs. Introduced multi-GPU support for RT-TDDFT using cuBLASMp and cuSOLVERMp, boosting performance and scalability on large GPU configurations. These changes were implemented via two focused commits and directly improve simulation throughput, accuracy, and capability for researchers.
March 2026
February 2026
1 Commits
Feb 1, 2026February 2026 monthly summary for deepmodeling/abacus-develop focused on stabilizing the RT-TDDFT workflow and improving reliability during SCF convergence. Delivered a targeted bug fix and stability enhancements that reduce runtime failures and improve the predictability of RT-TDDFT calculations.
February 2026
1 Commits
Feb 1, 2026February 2026 monthly summary for deepmodeling/abacus-develop focused on stabilizing the RT-TDDFT workflow and improving reliability during SCF convergence. Delivered a targeted bug fix and stability enhancements that reduce runtime failures and improve the predictability of RT-TDDFT calculations.
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026: Delivered a Wavefunction Initialization Refactor to improve clarity and maintainability in deepmodeling/abacus-develop. Refactor removes unnecessary initializations and simplifies the initialization path in lcao_others, reducing technical debt and easing future feature work. No major bugs fixed this month for this repository. Overall impact includes improved code quality, maintainability, and faster onboarding for new contributors. Demonstrated skills include Python refactoring, code quality practices, and disciplined version control.
1 Commits • 1 Features
Jan 1, 2026January 2026: Delivered a Wavefunction Initialization Refactor to improve clarity and maintainability in deepmodeling/abacus-develop. Refactor removes unnecessary initializations and simplifies the initialization path in lcao_others, reducing technical debt and easing future feature work. No major bugs fixed this month for this repository. Overall impact includes improved code quality, maintainability, and faster onboarding for new contributors. Demonstrated skills include Python refactoring, code quality practices, and disciplined version control.
January 2026
December 2025
4 Commits • 3 Features
Dec 1, 2025Month: 2025-12 — concise monthly summary focusing on key accomplishments and business value. In December 2025, I advanced GPU-enabled time-dependent simulations, improved cross-architecture validation, and refined output control for RT-TDDFT workflows, delivering measurable performance and reliability gains for researchers and teams relying on RT-TDDFT in production environments. Key outcomes include faster GPU-accelerated EDM calculations, robust correctness across CPU/GPU paths, and configurable data output that reduces I/O and simplifies analysis. These efforts also enhanced test coverage and documentation to support reproducibility and maintainability.
December 2025
4 Commits • 3 Features
Dec 1, 2025Month: 2025-12 — concise monthly summary focusing on key accomplishments and business value. In December 2025, I advanced GPU-enabled time-dependent simulations, improved cross-architecture validation, and refined output control for RT-TDDFT workflows, delivering measurable performance and reliability gains for researchers and teams relying on RT-TDDFT in production environments. Key outcomes include faster GPU-accelerated EDM calculations, robust correctness across CPU/GPU paths, and configurable data output that reduces I/O and simplifies analysis. These efforts also enhanced test coverage and documentation to support reproducibility and maintainability.
November 2025
6 Commits • 1 Features
Nov 1, 2025November 2025: Delivered a major RT-TDDFT ESolver refactor and EDM enhancements in deepmodeling/abacus-develop, delivering a more modular architecture, tensor-enabled EDM pathway, and robust output handling to improve stability and scalability for large HPC runs. Implemented tensor-backed EDM calculations, modernized linear-algebra interfaces, and standardized artifacts to streamline downstream analytics. Fixed correctness issues (template disambiguation for complex double precision) and MPI reliability, reinforcing accuracy and performance. These changes enable faster time-to-insight for RT-TDDFT workflows and lay the groundwork for future performance optimizations.
6 Commits • 1 Features
Nov 1, 2025November 2025: Delivered a major RT-TDDFT ESolver refactor and EDM enhancements in deepmodeling/abacus-develop, delivering a more modular architecture, tensor-enabled EDM pathway, and robust output handling to improve stability and scalability for large HPC runs. Implemented tensor-backed EDM calculations, modernized linear-algebra interfaces, and standardized artifacts to streamline downstream analytics. Fixed correctness issues (template disambiguation for complex double precision) and MPI reliability, reinforcing accuracy and performance. These changes enable faster time-to-insight for RT-TDDFT workflows and lay the groundwork for future performance optimizations.
November 2025
September 2025
2 Commits • 1 Features
Sep 1, 20252025-09 monthly summary for deepmodeling/abacus-develop: Focused on improving observability, test coverage, and measurement reliability. Delivered a new logging capability with out_alllog handling, added test coverage including a new test directory with inputs and a reference result, and updated the test suite to verify correct log filename generation when out_alllog is enabled. Fixed timer tick placement in v_xc to ensure reliable timing measurements and improved performance tracking.
September 2025
2 Commits • 1 Features
Sep 1, 20252025-09 monthly summary for deepmodeling/abacus-develop: Focused on improving observability, test coverage, and measurement reliability. Delivered a new logging capability with out_alllog handling, added test coverage including a new test directory with inputs and a reference result, and updated the test suite to verify correct log filename generation when out_alllog is enabled. Fixed timer tick placement in v_xc to ensure reliable timing measurements and improved performance tracking.
August 2025
3 Commits • 1 Features
Aug 1, 2025August 2025: Delivered three focused improvements in deepmodeling/abacus-develop that enhance parallel execution reliability, RT-TDDFT accuracy, and release readiness. Key work included aligning MPI-based log naming across ranks, refining RT-TDDFT length-gauge electric fields and ionic forces with updated docs and a non-cubic lattice fix, and bumping the release version to 3.9.0.13 to reflect a customer-ready build. These changes lower debugging time, improve result accuracy, and streamline deployment.
3 Commits • 1 Features
Aug 1, 2025August 2025: Delivered three focused improvements in deepmodeling/abacus-develop that enhance parallel execution reliability, RT-TDDFT accuracy, and release readiness. Key work included aligning MPI-based log naming across ranks, refining RT-TDDFT length-gauge electric fields and ionic forces with updated docs and a non-cubic lattice fix, and bumping the release version to 3.9.0.13 to reflect a customer-ready build. These changes lower debugging time, improve result accuracy, and streamline deployment.
August 2025
July 2025
5 Commits • 1 Features
Jul 1, 2025In July 2025, focused on delivering robust RT-TDDFT output enhancements and MPI reliability for the deepmodeling/abacus-develop repo, with clear business value in readability, performance readiness, and test stability. Key outcomes include updated outputs for RT-TDDFT, removal of outdated interfaces, GPU-friendly outputs, standardized dipole reporting, and increased MPI reliability with cross-rank consistency and ekb tensor synchronization, enabling scalable runs and more reliable CI tests.
July 2025
5 Commits • 1 Features
Jul 1, 2025In July 2025, focused on delivering robust RT-TDDFT output enhancements and MPI reliability for the deepmodeling/abacus-develop repo, with clear business value in readability, performance readiness, and test stability. Key outcomes include updated outputs for RT-TDDFT, removal of outdated interfaces, GPU-friendly outputs, standardized dipole reporting, and increased MPI reliability with cross-rank consistency and ekb tensor synchronization, enabling scalable runs and more reliable CI tests.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for deepmodeling/abacus-develop: API simplification, output unification, and documentation improvements that reduce user error and improve maintainability. Focused on removing deprecated nbands_istate input, standardizing output naming for real-space wave functions and partial charge densities, and updating docs and internal function references.
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for deepmodeling/abacus-develop: API simplification, output unification, and documentation improvements that reduce user error and improve maintainability. Focused on removing deprecated nbands_istate input, standardizing output naming for real-space wave functions and partial charge densities, and updating docs and internal function references.
June 2025
May 2025
6 Commits • 3 Features
May 1, 2025May 2025 highlights for deepmodeling/abacus-develop: four focused deliverables enhanced reliability, scalability, and scientific value. 1) RT-TDDFT GPU testing infrastructure with integrated tests and stability fixes, improving validation reliability for GPU-accelerated real-time TDDFT (commit eb20d4853edaf489320929c993efa2d295cbfe03). 2) Renamed input parameter bands_to_print to out_pchg and cleaned test configurations/docs, plus CI/CD test parameter fixes to reduce false positives (commits 6f3b743d2f95928a32f1a9d585a025af60053564; 281d2a2c7a02dfd08b430bd342b212cda5cbc583). 3) Enhanced Plane Wave outputs with cube-format real-space wavefunctions and correct partial charge/wave outputs under k-point parallelism (commits dcbeb0686638ad8d16b58c097e4050e0f99d274a; aee56915d272d35080bd3ec3f389b08f5898b45e). 4) Fixed MPI coordination for sparse matrix generation by synchronizing R-coordinates across all processes, eliminating MPI_ERR_TRUNCATE and increasing parallel robustness (commit 5daf5d9770a891af6ae70784a17a85d7afdf45). Overall impact: higher reliability of GPU-accelerated RT-TDDFT validation, cleaner CI/CD pipelines, richer data outputs for PW workflows, and more robust parallel computations. Skills demonstrated: GPU testing, MPI parallelism, cube-format data handling, multi-processor testing, and documentation/code hygiene.
May 2025
6 Commits • 3 Features
May 1, 2025May 2025 highlights for deepmodeling/abacus-develop: four focused deliverables enhanced reliability, scalability, and scientific value. 1) RT-TDDFT GPU testing infrastructure with integrated tests and stability fixes, improving validation reliability for GPU-accelerated real-time TDDFT (commit eb20d4853edaf489320929c993efa2d295cbfe03). 2) Renamed input parameter bands_to_print to out_pchg and cleaned test configurations/docs, plus CI/CD test parameter fixes to reduce false positives (commits 6f3b743d2f95928a32f1a9d585a025af60053564; 281d2a2c7a02dfd08b430bd342b212cda5cbc583). 3) Enhanced Plane Wave outputs with cube-format real-space wavefunctions and correct partial charge/wave outputs under k-point parallelism (commits dcbeb0686638ad8d16b58c097e4050e0f99d274a; aee56915d272d35080bd3ec3f389b08f5898b45e). 4) Fixed MPI coordination for sparse matrix generation by synchronizing R-coordinates across all processes, eliminating MPI_ERR_TRUNCATE and increasing parallel robustness (commit 5daf5d9770a891af6ae70784a17a85d7afdf45). Overall impact: higher reliability of GPU-accelerated RT-TDDFT validation, cleaner CI/CD pipelines, richer data outputs for PW workflows, and more robust parallel computations. Skills demonstrated: GPU testing, MPI parallelism, cube-format data handling, multi-processor testing, and documentation/code hygiene.
April 2025
1 Commits
Apr 1, 2025April 2025 monthly summary for deepmodeling/abacus-develop: Focused on stabilizing periodic boundary condition (PBC) handling and reducing configuration complexity. Delivered centralized PBC search logic and eliminated misconfiguration risk, contributing to more reliable, reproducible simulations and lower maintenance costs.
1 Commits
Apr 1, 2025April 2025 monthly summary for deepmodeling/abacus-develop: Focused on stabilizing periodic boundary condition (PBC) handling and reducing configuration complexity. Delivered centralized PBC search logic and eliminated misconfiguration risk, contributing to more reliable, reproducible simulations and lower maintenance costs.
April 2025
March 2025
6 Commits
Mar 1, 2025March 2025 monthly summary for deepmodeling/abacus-develop focused on stability, correctness, and release readiness across RT-TDDFT workflows and IO. Delivered targeted fixes to enable scalable parallel runs, hardened test infrastructure, improved documentation rendering, and strengthened data handling. Prepared the release artifact for v3.9.0.2 and expanded SOC testing coverage.
March 2025
6 Commits
Mar 1, 2025March 2025 monthly summary for deepmodeling/abacus-develop focused on stability, correctness, and release readiness across RT-TDDFT workflows and IO. Delivered targeted fixes to enable scalable parallel runs, hardened test infrastructure, improved documentation rendering, and strengthened data handling. Prepared the release artifact for v3.9.0.2 and expanded SOC testing coverage.
February 2025
4 Commits • 2 Features
Feb 1, 2025February 2025 (2025-02) monthly summary for deepmodeling/abacus-develop: Key items delivered across features and fixes, focusing on user guidance, build stability, and simulation accuracy. Highlights include documentation improvements for OCp/OCp_set and RT-TDDFT input usage, Libxc 7.0.0 compatibility fixes, and improvements to RT-TDDFT electric field force computation with time-accumulated field contributions and safety checks. The work enhances business value by reducing misconfiguration risks, ensuring smoother library integration, and improving the reliability and physical accuracy of RT-TDDFT simulations. Notable commits include: 6e94e9e51f70bf6bec073e66eba5ca9be67c4a1f, dbf3bd6579a1d53e595fcd08e7ad947a4c5260aa, b4d13c7d762199b9b503508c884a4c238fbbbcbd, b7745352befe4ad380bbf3d91dd11761b02096b0.
4 Commits • 2 Features
Feb 1, 2025February 2025 (2025-02) monthly summary for deepmodeling/abacus-develop: Key items delivered across features and fixes, focusing on user guidance, build stability, and simulation accuracy. Highlights include documentation improvements for OCp/OCp_set and RT-TDDFT input usage, Libxc 7.0.0 compatibility fixes, and improvements to RT-TDDFT electric field force computation with time-accumulated field contributions and safety checks. The work enhances business value by reducing misconfiguration risks, ensuring smoother library integration, and improving the reliability and physical accuracy of RT-TDDFT simulations. Notable commits include: 6e94e9e51f70bf6bec073e66eba5ca9be67c4a1f, dbf3bd6579a1d53e595fcd08e7ad947a4c5260aa, b4d13c7d762199b9b503508c884a4c238fbbbcbd, b7745352befe4ad380bbf3d91dd11761b02096b0.
February 2025
January 2025
3 Commits • 1 Features
Jan 1, 2025January 2025: Delivered GPU-accelerated RT-TDDFT path and restart reliability enhancements in deepmodeling/abacus-develop, plus tensor I/O precision fix. These changes improve performance, reliability, and reproducibility on GPU clusters while stabilizing CUDA/MPI CI builds.
January 2025
3 Commits • 1 Features
Jan 1, 2025January 2025: Delivered GPU-accelerated RT-TDDFT path and restart reliability enhancements in deepmodeling/abacus-develop, plus tensor I/O precision fix. These changes improve performance, reliability, and reproducibility on GPU clusters while stabilizing CUDA/MPI CI builds.
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for deepmodeling/abacus-develop. Focused on test-suite organization and naming convention alignment. Delivered a targeted refactor of integration tests to improve consistency and maintainability. Key action: rename integration test folders to align with get_pchg/get_wf, and update identifiers from wfc_ienvelope to wfc_get_wf and from wfc_istate to wfc_get_pchg. Commit: 83efe85b3acc745d1efd53276a733418ca7db5a6. No major bugs fixed this month; changes were limited to test structure and naming. Impact: clearer test taxonomy, reduced onboarding time, and improved CI reliability. Technologies/skills: Python testing infra, refactoring, repository hygiene, consistent naming conventions.
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for deepmodeling/abacus-develop. Focused on test-suite organization and naming convention alignment. Delivered a targeted refactor of integration tests to improve consistency and maintainability. Key action: rename integration test folders to align with get_pchg/get_wf, and update identifiers from wfc_ienvelope to wfc_get_wf and from wfc_istate to wfc_get_pchg. Commit: 83efe85b3acc745d1efd53276a733418ca7db5a6. No major bugs fixed this month; changes were limited to test structure and naming. Impact: clearer test taxonomy, reduced onboarding time, and improved CI reliability. Technologies/skills: Python testing infra, refactoring, repository hygiene, consistent naming conventions.
November 2024
Activity
Loading activity data...
Quality Metrics
Correctness91.4%
Maintainability85.2%
Architecture84.8%
Performance81.0%
AI Usage23.0%
Skills & Technologies
Programming Languages
CC++CMakeCUDAMakefileMarkdownPythonShell
Technical Skills
BLASC programmingC++C++ DevelopmentC++ developmentCI/CDCUDACode RefactoringComputational PhysicsDebuggingDocumentationDocumentation UpdateExample ImprovementFile Naming ConventionFortran
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline