
Worked on the amrvac/AGILE-experimental repository, delivering core enhancements to a Fortran-based finite volume solver for high-performance scientific computing. Focused on GPU acceleration and parallel reliability, the work included refactoring limiter data access, optimizing host-device memory synchronization with CUDA and OpenACC, and improving MPI robustness for distributed simulations. Codebase maintenance involved removing dead code, refining build system configuration, and updating compiler flags to support reproducible builds. Additional efforts improved test efficiency and simulation fidelity by adjusting grid sizes and enabling high-resolution configurations. These contributions strengthened numerical correctness, scalability, and maintainability, establishing a solid foundation for future scientific simulation development.
February 2025 monthly summary for amrvac/AGILE-experimental: Delivered MPI robustness and ghost cell handling improvements along with a high-resolution test configuration. Resulting in stronger parallel reliability, reduced data transfer overhead, and enhanced validation capability.
February 2025 monthly summary for amrvac/AGILE-experimental: Delivered MPI robustness and ghost cell handling improvements along with a high-resolution test configuration. Resulting in stronger parallel reliability, reduced data transfer overhead, and enhanced validation capability.
January 2025 — amrvac/AGILE-experimental: Focused on codebase maintenance and build-system refinements for the Fortran project. Delivered non-functional improvements to readability, dependency hygiene, and build performance, establishing a stronger foundation for future feature work. No user-facing changes this month; the changes improve reliability, reproducibility, and developer productivity.
January 2025 — amrvac/AGILE-experimental: Focused on codebase maintenance and build-system refinements for the Fortran project. Delivered non-functional improvements to readability, dependency hygiene, and build performance, establishing a stronger foundation for future feature work. No user-facing changes this month; the changes improve reliability, reproducibility, and developer productivity.
November 2024 Monthly Summary for amrvac/AGILE-experimental focused on API usability, GPU data management, test efficiency, and code hygiene. Key features delivered include exposing reconstruct_LR_gpu as a public API in mod_finite_volume_all (API surface change only; no functional changes); OpenACC data management improvements for bg and ps with validation prints for data integrity; OpenACC data region updates to synchronize bg on the GPU at every time step to maintain CPU-GPU consistency; test suite optimization by reducing grid sizes to speed up tests and lower resource usage; and a typo fix in the finite volume module affecting parallel directives.
November 2024 Monthly Summary for amrvac/AGILE-experimental focused on API usability, GPU data management, test efficiency, and code hygiene. Key features delivered include exposing reconstruct_LR_gpu as a public API in mod_finite_volume_all (API surface change only; no functional changes); OpenACC data management improvements for bg and ps with validation prints for data integrity; OpenACC data region updates to synchronize bg on the GPU at every time step to maintain CPU-GPU consistency; test suite optimization by reducing grid sizes to speed up tests and lower resource usage; and a typo fix in the finite volume module affecting parallel directives.
For 2024-10, focused on delivering a performance and correctness-focused enhancement to the Finite Volume solver in amrvac/AGILE-experimental, including a core refactor and GPU-oriented optimizations. The work improves numerical accuracy, enables more scalable GPU execution, and lays groundwork for future performance gains.
For 2024-10, focused on delivering a performance and correctness-focused enhancement to the Finite Volume solver in amrvac/AGILE-experimental, including a core refactor and GPU-oriented optimizations. The work improves numerical accuracy, enables more scalable GPU execution, and lays groundwork for future performance gains.

Overview of all repositories you've contributed to across your timeline