
Worked on the stfc/PSyclone and spack/spack-packages repositories, focusing on GPU offloading, OpenMP parallelism, and build system reliability. Delivered GPU acceleration for core physics modules by implementing conditional OpenMP DECLARE TARGET directives in Fortran, enabling scalable performance for HPC workflows. Expanded parallelization strategies with OpenMP Teams Loop support and stabilized GPU transformation workflows through refined exclusion criteria and improved test coverage. Addressed build and packaging issues in spack-spack-packages by updating dependency management and configuration handling, ensuring compatibility with evolving Python packaging standards. Demonstrated expertise in Fortran, Python, and configuration management, consistently improving reliability and performance across complex scientific computing environments.
January 2026: Focused on stabilizing the NEMO offloading pipeline within stfc/PSyclone by implementing a targeted exclusion for trabbc.f90, preventing unintended offloading behavior and errors. This work reduces risk in HPC workflows and enhances reliability of the NEMO integration.
January 2026: Focused on stabilizing the NEMO offloading pipeline within stfc/PSyclone by implementing a targeted exclusion for trabbc.f90, preventing unintended offloading behavior and errors. This work reduces risk in HPC workflows and enhances reliability of the NEMO integration.
October 2025: Delivered stability and compatibility improvements for the spack-spack-packages repository, focusing on build reliability and packaging interoperability. Consolidated fixes around psyclone integration and dependency/config handling, and ensured packaging compatibility with newer py-packaging requirements for py-setuptools-scm 7.1+. These changes reduce build failures and improve reproducibility across CI and developer environments.
October 2025: Delivered stability and compatibility improvements for the spack-spack-packages repository, focusing on build reliability and packaging interoperability. Consolidated fixes around psyclone integration and dependency/config handling, and ensured packaging compatibility with newer py-packaging requirements for py-setuptools-scm 7.1+. These changes reduce build failures and improve reproducibility across CI and developer environments.
PSyclone delivered expanded OpenMP parallelism options and improved transformation reliability in January 2025. Key work includes introducing OpenMP Teams Loop support to broaden parallelization strategies, stabilizing the OpenMP GPU transformation workflow by refining exclusion criteria and reclassifying problematic files, and refreshing the example/test suites to reflect current capabilities. These changes enhance performance opportunities, reduce transformation errors, and improve testing coverage, delivering tangible business value for GPU offload and scalable HPC workflows.
PSyclone delivered expanded OpenMP parallelism options and improved transformation reliability in January 2025. Key work includes introducing OpenMP Teams Loop support to broaden parallelization strategies, stabilizing the OpenMP GPU transformation workflow by refining exclusion criteria and reclassifying problematic files, and refreshing the example/test suites to reflect current capabilities. These changes enhance performance opportunities, reduce transformation errors, and improve testing coverage, delivering tangible business value for GPU offload and scalable HPC workflows.
December 2024: Delivered GPU offloading support for core physics modules in PSyclone, enabling conditional GPU acceleration through OpenMP. Implemented DECLARE TARGET directives in sbc_phy and solfrac_mod with file-name/loop-based activation and a force option to override problematic code blocks. This work lays the groundwork for accelerator-enabled deployments, improving performance and scalability for GPU-accelerated workflows.
December 2024: Delivered GPU offloading support for core physics modules in PSyclone, enabling conditional GPU acceleration through OpenMP. Implemented DECLARE TARGET directives in sbc_phy and solfrac_mod with file-name/loop-based activation and a force option to override problematic code blocks. This work lays the groundwork for accelerator-enabled deployments, improving performance and scalability for GPU-accelerated workflows.

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