UVA-LavaLab/PIMeval-PIMbench
Nov 2024 – May 2025
7 Months active
Languages Used
C++BashCMakefilePythonShelltextText
Technical Skills
Bug FixCode RefactoringEmbedded SystemsPerformance AnalysisPerformance OptimizationSystem Programming
deyuan
PROFILE
Deyuan
Guo Deyuan developed and maintained core features for the UVA-LavaLab/PIMeval-PIMbench repository, focusing on scalable PIM API design, performance instrumentation, and robust configuration management. He engineered systems such as the PIM API Fusion System and BOOL-centric conditional APIs, enabling efficient batched operations and data-dependent processing. Using C++ and Python, Guo expanded bit-serial computing support, improved cross-platform reliability, and integrated detailed performance and energy modeling. His work included extensive test coverage, code refactoring, and documentation updates, resulting in a maintainable codebase that supports complex benchmarking and simulation workflows for embedded and low-level system programming environments.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
83Total
Bugs
12
Commits
83
Features
29
Lines of code
13,393
Activity Months7
Your Network
8 people
Work History
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 performance summary for UVA-LavaLab/PIMeval-PIMbench: Increased digital bit-serial PIM capacity by extending register support to 16 and refined initialization to accommodate larger configurations; corrected a documentation typo in pimCore destructor comment to improve maintainability and reduce confusion. These deliverables enhance benchmarking fidelity and scalability, enabling more complex PIM workloads and clearer code/documentation.
2 Commits • 1 Features
May 1, 2025May 2025 performance summary for UVA-LavaLab/PIMeval-PIMbench: Increased digital bit-serial PIM capacity by extending register support to 16 and refined initialization to accommodate larger configurations; corrected a documentation typo in pimCore destructor comment to improve maintainability and reduce confusion. These deliverables enhance benchmarking fidelity and scalability, enabling more complex PIM workloads and clearer code/documentation.
May 2025
April 2025
3 Commits • 2 Features
Apr 1, 2025Summary for April 2025 (UVA-LavaLab/PIMeval-PIMbench): Delivered a scalable PIM API Fusion System and refactored command handling to improve maintainability. Implemented PIM API Fusion System with pimFuse and PimProg to batch multiple PIM API calls, accompanied by test coverage validating fused operations across device types. Completed an internal refactor to separate pimCmdFuse into dedicated source/header files, enhancing maintainability and reducing compile-time coupling. Added targeted test coverage for fused operations and updated includes to support clearer interfaces. These efforts reduce integration overhead and establish a solid foundation for cross-device batched PIM workloads, enabling faster iteration and more reliable performance improvements.
April 2025
3 Commits • 2 Features
Apr 1, 2025Summary for April 2025 (UVA-LavaLab/PIMeval-PIMbench): Delivered a scalable PIM API Fusion System and refactored command handling to improve maintainability. Implemented PIM API Fusion System with pimFuse and PimProg to batch multiple PIM API calls, accompanied by test coverage validating fused operations across device types. Completed an internal refactor to separate pimCmdFuse into dedicated source/header files, enhancing maintainability and reducing compile-time coupling. Added targeted test coverage for fused operations and updated includes to support clearer interfaces. These efforts reduce integration overhead and establish a solid foundation for cross-device batched PIM workloads, enabling faster iteration and more reliable performance improvements.
March 2025
42 Commits • 16 Features
Mar 1, 2025March 2025 (2025-03) UVA-LavaLab/PIMeval-PIMbench highlights: Key features delivered: - BOOL conditional PIM APIs support: added conditional copy, broadcast, select, and select scalar, enabling efficient, data-dependent operations. See commits 9d0437519312018a031ccdb3f9e4e5f6808bc895 and related tests (8596fceb3d26a67732bd596c5986181a4ccc9634). - Debug flags integration in pimCmd: introduced and wired in the new debug flags to improve diagnostics and troubleshooting; commit 8e68d82c74bf8a76276941c5031af58709f8e740. - BOOL return type support for FP command template functions and relational API BOOL path: aligned return types and results to BOOL across FP templates and relational kernels (commit a9511da756ff7be34d144cad794d1dc511700695; 29793341f821fab9449023e0e6718aa8f0326b1f). - PIM energy and performance modeling enhancements: added cond-broadcast performance/energy formulas and pimConvertType models, enabling energy-aware planning for PIM workloads (commits a0c7ba48a0c8026afd8f84b4ad6ec0c4ec9b4d3b and 4de4c30b5b4c7ade2f684ed9ccf84d6fdaa9fb7a). - PIMeval launcher and usability improvements: added a PIMeval app launcher script and improved path handling in app_launcher.py; bug fixes to clang-related build issues and test inputs (commits fd95c9568c12f469d7f599b108587f9d8ad70aca; 834d7c6fb78396ff080942ed942d6aeb707680c9; 93a6e4b0ac11f6a01ab27b2f419a6ecff9134989). Major bugs fixed: - Debuggability and correctness fixes in new code paths: fixed debuggability assert when querying obj info with -1; fixed a pimCmd new code bug; addressed divide-by-zero in runtime percentage calculations; removed an unused variable in filter-by-key; updated tests to reflect API/relational changes (commits 8879b4c16534de1cb211576bfa94106dcffd7093; 375d587f0ccd3feb69debd17e8c63044a163bdad; 7e5416df7add6e8bba50d8e917dc6c8a918bf6d2; 9f62d35d41f5a2ad6853784309299e1fa530f788; 5f3f72264b89a3b7834468fb1f99278f7204eeb7; 834d7c6fb78396ff080942ed942d6aeb707680c9; 93a6e4b0ac11f6a01ab27b2f419a6ecff9134989; 629c29257820da46134c98c6cecbce3cd28b1472). Overall impact and accomplishments: - Expanded BOOL-centric data paths across the PIM ecosystem (PIM APIs, kernels, benchmarks), improving correctness and performance visibility for conditional operations. - Strengthened reliability with test augments (test-cond, re-golden functional tests, nan/percentage outputs) and a modernized launcher/build workflow, accelerating future development and reducing risk in production deployments. - Enabled energy-aware planning with new perf/energy models for conditional operations and type conversions, supporting more accurate budgeting and performance tuning. Technologies/skills demonstrated: - C/C++ development for PIM APIs and kernels, test harnessing, and build tooling (Make/clang). - BOOL-oriented data-path design across modules (PIM_EQ, redsum, hist, k-means, etc.) and cross-kernel consistency. - Performance and energy modeling for conditional operations and data-type conversions. - Debugging, test automation, and release-quality golden tests. - Utilities and tooling: app launcher scripts and improved path handling for streamlined workflows.
42 Commits • 16 Features
Mar 1, 2025March 2025 (2025-03) UVA-LavaLab/PIMeval-PIMbench highlights: Key features delivered: - BOOL conditional PIM APIs support: added conditional copy, broadcast, select, and select scalar, enabling efficient, data-dependent operations. See commits 9d0437519312018a031ccdb3f9e4e5f6808bc895 and related tests (8596fceb3d26a67732bd596c5986181a4ccc9634). - Debug flags integration in pimCmd: introduced and wired in the new debug flags to improve diagnostics and troubleshooting; commit 8e68d82c74bf8a76276941c5031af58709f8e740. - BOOL return type support for FP command template functions and relational API BOOL path: aligned return types and results to BOOL across FP templates and relational kernels (commit a9511da756ff7be34d144cad794d1dc511700695; 29793341f821fab9449023e0e6718aa8f0326b1f). - PIM energy and performance modeling enhancements: added cond-broadcast performance/energy formulas and pimConvertType models, enabling energy-aware planning for PIM workloads (commits a0c7ba48a0c8026afd8f84b4ad6ec0c4ec9b4d3b and 4de4c30b5b4c7ade2f684ed9ccf84d6fdaa9fb7a). - PIMeval launcher and usability improvements: added a PIMeval app launcher script and improved path handling in app_launcher.py; bug fixes to clang-related build issues and test inputs (commits fd95c9568c12f469d7f599b108587f9d8ad70aca; 834d7c6fb78396ff080942ed942d6aeb707680c9; 93a6e4b0ac11f6a01ab27b2f419a6ecff9134989). Major bugs fixed: - Debuggability and correctness fixes in new code paths: fixed debuggability assert when querying obj info with -1; fixed a pimCmd new code bug; addressed divide-by-zero in runtime percentage calculations; removed an unused variable in filter-by-key; updated tests to reflect API/relational changes (commits 8879b4c16534de1cb211576bfa94106dcffd7093; 375d587f0ccd3feb69debd17e8c63044a163bdad; 7e5416df7add6e8bba50d8e917dc6c8a918bf6d2; 9f62d35d41f5a2ad6853784309299e1fa530f788; 5f3f72264b89a3b7834468fb1f99278f7204eeb7; 834d7c6fb78396ff080942ed942d6aeb707680c9; 93a6e4b0ac11f6a01ab27b2f419a6ecff9134989; 629c29257820da46134c98c6cecbce3cd28b1472). Overall impact and accomplishments: - Expanded BOOL-centric data paths across the PIM ecosystem (PIM APIs, kernels, benchmarks), improving correctness and performance visibility for conditional operations. - Strengthened reliability with test augments (test-cond, re-golden functional tests, nan/percentage outputs) and a modernized launcher/build workflow, accelerating future development and reducing risk in production deployments. - Enabled energy-aware planning with new perf/energy models for conditional operations and type conversions, supporting more accurate budgeting and performance tuning. Technologies/skills demonstrated: - C/C++ development for PIM APIs and kernels, test harnessing, and build tooling (Make/clang). - BOOL-oriented data-path design across modules (PIM_EQ, redsum, hist, k-means, etc.) and cross-kernel consistency. - Performance and energy modeling for conditional operations and data-type conversions. - Debugging, test automation, and release-quality golden tests. - Utilities and tooling: app launcher scripts and improved path handling for streamlined workflows.
March 2025
February 2025
31 Commits • 8 Features
Feb 1, 2025February 2025 monthly summary for UVA-LavaLab/PIMeval-PIMbench focused on stabilizing configuration, expanding data-type support, and strengthening testability and cross-platform reliability. Delivered significant configuration improvements, broader data-type support, and enhanced test pipelines, enabling faster iteration, safer data handling, and more robust performance assessments. Notable fixes improved build stability and runtime behavior on macOS.
February 2025
31 Commits • 8 Features
Feb 1, 2025February 2025 monthly summary for UVA-LavaLab/PIMeval-PIMbench focused on stabilizing configuration, expanding data-type support, and strengthening testability and cross-platform reliability. Delivered significant configuration improvements, broader data-type support, and enhanced test pipelines, enabling faster iteration, safer data handling, and more robust performance assessments. Notable fixes improved build stability and runtime behavior on macOS.
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly performance summary for UVA-LavaLab/PIMeval-PIMbench. Focused on delivering performance timing instrumentation and robust test coverage to enable data-driven optimization of PIM kernels and runtime in the presence of CPU runtime and DRAM refresh considerations.
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly performance summary for UVA-LavaLab/PIMeval-PIMbench. Focused on delivering performance timing instrumentation and robust test coverage to enable data-driven optimization of PIM kernels and runtime in the presence of CPU runtime and DRAM refresh considerations.
January 2025
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for UVA-LavaLab/PIMeval-PIMbench: Delivered PIMeval Analysis Mode to accelerate analysis by bypassing PIM computation, enabling faster performance and lower energy usage during benchmarking runs. Implemented a pimIsAnalysisMode flag and updated core analysis functions to respect this mode; also optimized data handling to avoid unnecessary transfers during analysis. These changes reduce analysis time and energy footprint, enabling quicker insights for performance benchmarking and energy analysis.
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for UVA-LavaLab/PIMeval-PIMbench: Delivered PIMeval Analysis Mode to accelerate analysis by bypassing PIM computation, enabling faster performance and lower energy usage during benchmarking runs. Implemented a pimIsAnalysisMode flag and updated core analysis functions to respect this mode; also optimized data handling to avoid unnecessary transfers during analysis. These changes reduce analysis time and energy footprint, enabling quicker insights for performance benchmarking and energy analysis.
November 2024
1 Commits
Nov 1, 2024Month: 2024-11. Focus on code quality and reliability in the UVA-LavaLab/PIMeval-PIMbench repository. Completed a targeted bug fix that improves benchmark accuracy and reduces potential confusion in metric reporting.
1 Commits
Nov 1, 2024Month: 2024-11. Focus on code quality and reliability in the UVA-LavaLab/PIMeval-PIMbench repository. Completed a targeted bug fix that improves benchmark accuracy and reduces potential confusion in metric reporting.
November 2024
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Quality Metrics
Correctness89.2%
Maintainability87.8%
Architecture83.4%
Performance80.4%
AI Usage20.6%
Skills & Technologies
Programming Languages
BashCC++MakefilePythonShellTexttext
Technical Skills
API DesignAPI DevelopmentAPI IntegrationAPI TestingAPI designAPI developmentAlgorithm AnalysisAlgorithm ImplementationAlgorithm optimizationBit ManipulationBit manipulationBit-serial ComputingBitwise OperationsBug FixBug Fixing
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