UVA-LavaLab/PIMeval-PIMbench
Feb 2025 – May 2025
4 Months active
Languages Used
C++MakefilePythonShellBashMarkdown
Technical Skills
Algorithm ImplementationC++CPU ComputingGPU ComputingMakefilePIM
Kyle Durrer
PROFILE
Kyle Durrer
During four months on the UVA-LavaLab/PIMeval-PIMbench repository, Kmd2zjw developed and refined a cross-platform Random Forest benchmarking suite targeting CPU, GPU, and PIM architectures. They implemented the initial Random Forest algorithm in C++ and Python, established multi-hardware build support with Makefiles, and integrated benchmarking pipelines using scikit-learn and cuML. Their work unified benchmarking scripts, added energy-aware performance metrics via NVML, and ensured reproducibility through deterministic random number generation. Kmd2zjw also led a focused refactor of the Random Forest operator logic, emphasizing code maintainability and clarity. This work provided a robust, extensible foundation for heterogeneous hardware benchmarking.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
8Total
Bugs
1
Commits
8
Features
4
Lines of code
6,365
Activity Months4
Your Network
47 people
Work History
May 2025
2 Commits • 1 Features
May 1, 2025In May 2025, delivered a focused Random Forest operator-based refactor and code cleanup for UVA-LavaLab/PIMeval-PIMbench. The changes improve maintainability, determinism in benchmarking logic, and reduce technical debt, laying groundwork for future feature iterations and stable benchmarks.
2 Commits • 1 Features
May 1, 2025In May 2025, delivered a focused Random Forest operator-based refactor and code cleanup for UVA-LavaLab/PIMeval-PIMbench. The changes improve maintainability, determinism in benchmarking logic, and reduce technical debt, laying groundwork for future feature iterations and stable benchmarks.
May 2025
April 2025
4 Commits • 1 Features
Apr 1, 2025Month: 2025-04. Focused on delivering reproducible, energy-aware benchmarking for RF workloads across CPU and GPU, with an emphasis on maintainability and clear business value. Implemented a unified benchmarking workflow, enhanced GPU acceleration, added energy metrics, and ensured reproducibility for performance analysis.
April 2025
4 Commits • 1 Features
Apr 1, 2025Month: 2025-04. Focused on delivering reproducible, energy-aware benchmarking for RF workloads across CPU and GPU, with an emphasis on maintainability and clear business value. Implemented a unified benchmarking workflow, enhanced GPU acceleration, added energy metrics, and ensured reproducibility for performance analysis.
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for UVA-LavaLab/PIMeval-PIMbench focused on delivering a cross-platform Random Forest prototype and laying the groundwork for CPU-vs-GPU benchmarking.
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for UVA-LavaLab/PIMeval-PIMbench focused on delivering a cross-platform Random Forest prototype and laying the groundwork for CPU-vs-GPU benchmarking.
March 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 performance summary for UVA-LavaLab/PIMeval-PIMbench focusing on feature delivery and technical milestones. Delivered the groundwork for cross-hardware Random Forest evaluation by implementing the initial RF algorithm and multi-hardware build support. Establishing Makefiles for PIM, CPU, and GPU variants, along with core PIM C++ code and baseline CPU/GPU implementations, lays the foundation for RF performance comparisons across architectures. No major bugs fixed this month. This work tightens the feedback loop for RF performance on heterogeneous hardware and sets the stage for ongoing benchmarking.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 performance summary for UVA-LavaLab/PIMeval-PIMbench focusing on feature delivery and technical milestones. Delivered the groundwork for cross-hardware Random Forest evaluation by implementing the initial RF algorithm and multi-hardware build support. Establishing Makefiles for PIM, CPU, and GPU variants, along with core PIM C++ code and baseline CPU/GPU implementations, lays the foundation for RF performance comparisons across architectures. No major bugs fixed this month. This work tightens the feedback loop for RF performance on heterogeneous hardware and sets the stage for ongoing benchmarking.
Activity
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Quality Metrics
Correctness81.2%
Maintainability80.0%
Architecture76.4%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
BashC++MakefileMarkdownPythonShell
Technical Skills
Algorithm ImplementationBash ScriptingBenchmarkingC++CPU ComputingCUDAClean Code PracticesCode CleanupCode RefactoringCuMLGPU ComputingMachine LearningMakefilePIMParallel Computing
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