UVA-LavaLab/PIMeval-PIMbench
Dec 2024 – May 2025
4 Months active
Languages Used
C++MakefilePythonShellMarkdownCCUDA
Technical Skills
BenchmarkingBuild SystemsData ProcessingData ScienceData SerializationFile I/O
mhgholamrezaei
PROFILE
Mhgholamrezaei
Over four months, MGH contributed to UVA-LavaLab/PIMeval-PIMbench by building and modernizing benchmarking and simulation tools for processing-in-memory (PIM) architectures. He developed high-dimensional computing workflows with GPU acceleration and robust data export, enabling reproducible performance evaluation. MGH implemented mass spectrometry dataset support, flexible command-line interfaces, and top-k similarity search, refactoring C++ and Python code for extensibility. He introduced an AES S-box API with energy and performance modeling across bit-serial and bank-level PIM devices, integrating comprehensive tests and documentation. His work improved cross-device reliability, energy-aware benchmarking, and maintainability, demonstrating depth in C++, CUDA, and low-level optimization for advanced hardware research.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
24Total
Bugs
2
Commits
24
Features
6
Lines of code
3,179
Activity Months4
Your Network
8 people
Work History
May 2025
8 Commits • 2 Features
May 1, 2025May 2025 performance summary for UVA-LavaLab/PIMeval-PIMbench: Delivered energy-aware AES benchmarking capabilities and modernization of AES S-box modeling, enabling better energy/performance visibility, cross-device reliability, and maintainability across PIM devices.
8 Commits • 2 Features
May 1, 2025May 2025 performance summary for UVA-LavaLab/PIMeval-PIMbench: Delivered energy-aware AES benchmarking capabilities and modernization of AES S-box modeling, enabling better energy/performance visibility, cross-device reliability, and maintainability across PIM devices.
May 2025
April 2025
6 Commits • 1 Features
Apr 1, 2025Month: 2025-04. Summary: Delivered AES S-box API and modeling suite for the PIM eval library, enabling functional simulation of AES S-box and inverse S-box with performance and energy modeling across bit-serial and bank-level PIM architectures, and integrated tests/benchmarks. Also fixed Fulcrum S-box execution issues and robustness, including memory allocation, file handling, and device type checks, improving simulation reliability. These efforts provide accurate, architecture-aware energy/performance insights, accelerating design decisions and risk reduction for PIM-enabled accelerators. Technologies demonstrated include AES S-box API, functional simulation, energy/performance modeling, bit-serial and bank-level PIM architectures, and Fulcrum simulation.
April 2025
6 Commits • 1 Features
Apr 1, 2025Month: 2025-04. Summary: Delivered AES S-box API and modeling suite for the PIM eval library, enabling functional simulation of AES S-box and inverse S-box with performance and energy modeling across bit-serial and bank-level PIM architectures, and integrated tests/benchmarks. Also fixed Fulcrum S-box execution issues and robustness, including memory allocation, file handling, and device type checks, improving simulation reliability. These efforts provide accurate, architecture-aware energy/performance insights, accelerating design decisions and risk reduction for PIM-enabled accelerators. Technologies demonstrated include AES S-box API, functional simulation, energy/performance modeling, bit-serial and bank-level PIM architectures, and Fulcrum simulation.
January 2025
5 Commits • 2 Features
Jan 1, 2025Concise monthly summary for 2025-01 focusing on key measurable accomplishments, business value, and technical excellence for UVA-LavaLab/PIMeval-PIMbench.
5 Commits • 2 Features
Jan 1, 2025Concise monthly summary for 2025-01 focusing on key measurable accomplishments, business value, and technical excellence for UVA-LavaLab/PIMeval-PIMbench.
January 2025
December 2024
5 Commits • 1 Features
Dec 1, 2024Month 2024-12: Delivered a focused uplift for UVA-LavaLab/PIMeval-PIMbench by establishing a baseline High-Dimensional Computing (HDC) workflow with GPU acceleration and robust data export. This work enables accelerated evaluation of HDC workloads on PIM hardware, improves reproducibility, and tightens the end-to-end benchmarking pipeline for future research and optimization. Key business value: faster, reproducible benchmarking of advanced HDC workloads on PIMbench informs performance tuning, hardware evaluation, and roadmap decisions; streamlined builds reduce onboarding time for new contributors and experiments.
December 2024
5 Commits • 1 Features
Dec 1, 2024Month 2024-12: Delivered a focused uplift for UVA-LavaLab/PIMeval-PIMbench by establishing a baseline High-Dimensional Computing (HDC) workflow with GPU acceleration and robust data export. This work enables accelerated evaluation of HDC workloads on PIM hardware, improves reproducibility, and tightens the end-to-end benchmarking pipeline for future research and optimization. Key business value: faster, reproducible benchmarking of advanced HDC workloads on PIMbench informs performance tuning, hardware evaluation, and roadmap decisions; streamlined builds reduce onboarding time for new contributors and experiments.
Activity
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Quality Metrics
Correctness89.2%
Maintainability87.6%
Architecture86.6%
Performance82.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CC++CUDAMakefileMarkdownPythonShell
Technical Skills
AESAES CryptographyAPI DesignAPI DevelopmentAlgorithm OptimizationAlgorithmsBenchmarkingBug FixingBuild SystemsC++C++ DevelopmentCUDACode RefactoringCommand Line InterfaceCommand-line Interface (CLI) Development
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