UVA-LavaLab/PIMeval-PIMbench
Nov 2024 – Feb 2025
3 Months active
Languages Used
C++MakefilePythonC
Technical Skills
C++Image ProcessingMakefilePIMPerformance OptimizationLow-Level Programming
hugoabbot
PROFILE
Hugoabbot
Worked on UVA-LavaLab/PIMeval-PIMbench, developing and optimizing image analytics and deep learning benchmarks for Processing-in-Memory (PIM) hardware. Built a C++-based BMP histogram computation with PIM acceleration, supporting large images through chunked processing and OpenMP parallelization, and ensured correctness via host-side verification. Enhanced reliability and benchmarking by introducing a Global Average Pooling (GAP) benchmark with both C++ and PyTorch implementations, enabling cross-platform performance comparisons. Improved command-line usability and reproducibility by refactoring argument parsing and benchmarking flows. The work demonstrated expertise in C++, Python, performance optimization, and low-level programming, focusing on robust, reproducible, and scalable benchmarking solutions.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
6Total
Bugs
2
Commits
6
Features
3
Lines of code
1,080
Activity Months3
Your Network
48 peopleShared Repositories
8
Work History
February 2025
3 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for UVA-LavaLab/PIMeval-PIMbench focusing on GAP correctness and benchmarking improvements alongside CLI robustness. Key improvements include a working Global Average Pooling (GAP) benchmark aligned with correct behavior and PIM integration, refactored parameter handling and benchmarking flow to enable consistent, reproducible results, and a fix to the DRAMSim configuration flag parsing to improve CLI usability and reduce setup errors. Overall, these changes enhance benchmarking accuracy, reproducibility, and developer productivity, accelerating validation of PIM-enabled GAP workloads.
3 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for UVA-LavaLab/PIMeval-PIMbench focusing on GAP correctness and benchmarking improvements alongside CLI robustness. Key improvements include a working Global Average Pooling (GAP) benchmark aligned with correct behavior and PIM integration, refactored parameter handling and benchmarking flow to enable consistent, reproducible results, and a fix to the DRAMSim configuration flag parsing to improve CLI usability and reduce setup errors. Overall, these changes enhance benchmarking accuracy, reproducibility, and developer productivity, accelerating validation of PIM-enabled GAP workloads.
February 2025
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 (UVA-LavaLab/PIMeval-PIMbench) focused on reliability and performance benchmarking for image analytics on PIM hardware. Key outcomes include: robust histogram processing for large images via input chunking and cleanup (addressing stability and performance) and the first iteration of a GAP benchmark with cross‑platform verification. The GAP work includes C++ implementations for PIM and CPU verification plus a PyTorch-based Python baseline for performance comparison, establishing a foundation for evaluating GAP on PIM hardware. These efforts improve production reliability, enable objective cross-hardware comparisons, and set the stage for future performance-driven optimizations. Technologies demonstrated include C++, PyTorch (Python), benchmarking methodologies, and code-quality improvements through review fixes.
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 (UVA-LavaLab/PIMeval-PIMbench) focused on reliability and performance benchmarking for image analytics on PIM hardware. Key outcomes include: robust histogram processing for large images via input chunking and cleanup (addressing stability and performance) and the first iteration of a GAP benchmark with cross‑platform verification. The GAP work includes C++ implementations for PIM and CPU verification plus a PyTorch-based Python baseline for performance comparison, establishing a foundation for evaluating GAP on PIM hardware. These efforts improve production reliability, enable objective cross-hardware comparisons, and set the stage for future performance-driven optimizations. Technologies demonstrated include C++, PyTorch (Python), benchmarking methodologies, and code-quality improvements through review fixes.
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 performance-focused delivery for UVA-LavaLab/PIMeval-PIMbench, featuring a PIM-accelerated BMP histogram computation with robust build and verification pathways.
1 Commits • 1 Features
Nov 1, 2024November 2024 performance-focused delivery for UVA-LavaLab/PIMeval-PIMbench, featuring a PIM-accelerated BMP histogram computation with robust build and verification pathways.
November 2024
Activity
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Quality Metrics
Correctness86.6%
Maintainability83.4%
Architecture81.6%
Performance81.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
CC++MakefilePython
Technical Skills
Argument ParsingBenchmarkingC++Command Line InterfaceDeep LearningEmbedded SystemsImage ProcessingLow-Level ProgrammingMakefilePIMPIM ProgrammingParallel ComputingPerformance BenchmarkingPerformance OptimizationPyTorch
Repositories Contributed To
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