
Worked on the UVA-LavaLab/PIMeval-PIMbench repository, developing and refining cross-device APIs and performance modeling tools for memory-centric computing. Built a Prefix Sum API supporting bit-serial, CUDA, and CPU backends, ensuring correctness across architectures through targeted bug fixes and operation count recalibration. Modernized reduction command handling using C++ features like std::variant to simplify data-type management and improve maintainability. Enhanced documentation and configuration management, including HBM simulation updates and improved attribution guidance. Addressed cross-platform build issues for macOS by resolving type-casting bugs in string utilities. Demonstrated depth in C++, CUDA, and system programming, with a focus on maintainable, high-performance code.
May 2025 monthly summary for UVA-LavaLab/PIMeval-PIMbench: Implemented a cross-device Prefix Sum API across bit-serial, CUDA, and CPU backends, integrated into the PIM library with device-specific variants, updated operation counts, and documentation. Fixed correctness issues (off-by-one loop and bit-serial calculation) ensuring reliable results across data types and architectures. Removed obsolete AES PIM outputs to simplify the repo. Updated HBM configurations for PIMeval/TACO/AQUABOLT simulations, adding subarray-level configs and aligning rank bandwidth with A100_80GB, improving model realism. Documentation improvements accompany performance modeling updates.
May 2025 monthly summary for UVA-LavaLab/PIMeval-PIMbench: Implemented a cross-device Prefix Sum API across bit-serial, CUDA, and CPU backends, integrated into the PIM library with device-specific variants, updated operation counts, and documentation. Fixed correctness issues (off-by-one loop and bit-serial calculation) ensuring reliable results across data types and architectures. Removed obsolete AES PIM outputs to simplify the repo. Updated HBM configurations for PIMeval/TACO/AQUABOLT simulations, adding subarray-level configs and aligning rank bandwidth with A100_80GB, improving model realism. Documentation improvements accompany performance modeling updates.
March 2025 monthly summary for UVA-LavaLab/PIMeval-PIMbench: Focused on improving cross-platform reliability for macOS by delivering a critical bug fix in string matching utilities. Implemented explicit static casts to std::min to ensure correct comparisons and prevent overflow on OS X builds, addressing compilation issues and stabilizing macOS support. Change tracked in commit 00458f6ac783e3f7b47a397729dac77f09439675 with message "PIMbench: Str match fix for OS X compilation (#247)".
March 2025 monthly summary for UVA-LavaLab/PIMeval-PIMbench: Focused on improving cross-platform reliability for macOS by delivering a critical bug fix in string matching utilities. Implemented explicit static casts to std::min to ensure correct comparisons and prevent overflow on OS X builds, addressing compilation issues and stabilizing macOS support. Change tracked in commit 00458f6ac783e3f7b47a397729dac77f09439675 with message "PIMbench: Str match fix for OS X compilation (#247)".
February 2025 monthly summary for UVA-LavaLab/PIMeval-PIMbench: Key deliverables included documentation and attribution enhancements, Fulcrum performance modeling refinements, and maintainability improvements. These efforts improve external usage guidance and reproducibility, enable more accurate performance and energy forecasts, and reduce future maintenance risk.
February 2025 monthly summary for UVA-LavaLab/PIMeval-PIMbench: Key deliverables included documentation and attribution enhancements, Fulcrum performance modeling refinements, and maintainability improvements. These efforts improve external usage guidance and reproducibility, enable more accurate performance and energy forecasts, and reduce future maintenance risk.
November 2024 performance summary for UVA-LavaLab/PIMeval-PIMbench: focused on modernization of the internal reduction command path and targeted code hygiene improvements to strengthen maintainability and readiness for future enhancements. Deliverables centered on data-type management in reduction operations and removal of a non-functional include to reduce compile-time noise. Overall impact targets easier long-term maintenance, safer extension of reduction logic, and a cleaner codebase that reduces technical debt.
November 2024 performance summary for UVA-LavaLab/PIMeval-PIMbench: focused on modernization of the internal reduction command path and targeted code hygiene improvements to strengthen maintainability and readiness for future enhancements. Deliverables centered on data-type management in reduction operations and removal of a non-functional include to reduce compile-time noise. Overall impact targets easier long-term maintenance, safer extension of reduction logic, and a cleaner codebase that reduces technical debt.

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