cp2k/cp2k
Nov 2024 – Feb 2026
2 Months active
Languages Used
CFortranCMake
Technical Skills
C ProgrammingCUDAFortran ProgrammingGPU ComputingMPINumerical Linear Algebra
Jiří Vyskočil
PROFILE
Jiří Vyskočil
Jiri Vyskocil developed a GPU-accelerated generalized eigenvalue solver for the cp2k/cp2k repository, targeting large-scale matrix computations. He integrated cuSOLVER’s sygvd routine, creating a new C API and a Fortran wrapper to expose the solver within the codebase. His implementation included a size-based dispatch mechanism in Fortran, automatically selecting the GPU-accelerated path for matrices of size 64x64 or larger while retaining a safe fallback for smaller cases. Leveraging C, Fortran, and CUDA, Jiri’s work improved performance and scalability for numerical linear algebra tasks, providing a robust foundation for future GPU computing enhancements in scientific simulations.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
515
Activity Months2
Your Network
48 peopleShared Repositories
48
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly performance summary focusing on the cp2k/cp2k repository. Delivered targeted enhancements to enable scalable, distributed CPU/GPU workflows by adding NCCL support to CuSolverMP and introducing version-detection logic to conditionally link to NCCL or CAL depending on CuSolverMP version. These changes improve throughput, scalability, portability, and deployment flexibility for large-scale simulations.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly performance summary focusing on the cp2k/cp2k repository. Delivered targeted enhancements to enable scalable, distributed CPU/GPU workflows by adding NCCL support to CuSolverMP and introducing version-detection logic to conditionally link to NCCL or CAL depending on CuSolverMP version. These changes improve throughput, scalability, portability, and deployment flexibility for large-scale simulations.
February 2026
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 was focused on delivering a GPU-accelerated generalized eigenvalue solver in cp2k/cp2k, enabling scalable large-matrix eigenvalue computations via cuSOLVER. The integration includes a new C API, a Fortran wrapper, and a size-based dispatch to switch to cuSOLVER for matrices 64x64 or larger, with a safe fallback for smaller matrices. This work lays the groundwork for continued GPU-accelerated linear algebra improvements and improved performance for large-scale simulations.
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 was focused on delivering a GPU-accelerated generalized eigenvalue solver in cp2k/cp2k, enabling scalable large-matrix eigenvalue computations via cuSOLVER. The integration includes a new C API, a Fortran wrapper, and a size-based dispatch to switch to cuSOLVER for matrices 64x64 or larger, with a safe fallback for smaller matrices. This work lays the groundwork for continued GPU-accelerated linear algebra improvements and improved performance for large-scale simulations.
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture90.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CCMakeFortran
Technical Skills
C ProgrammingCMakeCUDAFortranFortran ProgrammingGPU ComputingMPINumerical Linear Algebraparallel computing
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline