UVA-LavaLab/PIMeval-PIMbench
Nov 2024 – Jul 2025
9 Months active
Languages Used
C++textConfigurationINIMakefileCCUDAPython
Technical Skills
API IntegrationBenchmarkingC++C++ DevelopmentDevice DriversEmbedded Systems
fasiddique
PROFILE
Fasiddique
Farzana developed and maintained the UVA-LavaLab/PIMeval-PIMbench benchmarking suite, focusing on performance modeling, device integration, and energy analysis for advanced memory and accelerator systems. She engineered features such as PIM-accelerated VGG workloads, bank-level energy modeling, and support for new memory technologies like GDDR and HBM, using C++ and CUDA for low-level optimization and parallel computing. Her work included API refactoring, benchmarking automation, and detailed performance/energy metric corrections, resulting in more accurate, configurable, and maintainable tools. Farzana’s contributions demonstrated depth in embedded systems, memory management, and algorithm optimization, enabling reliable, scalable benchmarking for heterogeneous hardware environments.
Overall Statistics
Feature vs Bugs
70%Features
Repository Contributions
178Total
Bugs
26
Commits
178
Features
61
Lines of code
35,675
Activity Months9
Your Network
47 people
Work History
July 2025
33 Commits • 10 Features
Jul 1, 2025Monthly performance summary for 2025-07: Delivered expanded device support, memory technology integration, and performance-energy modeling improvements; stabilized builds through cleanup and warnings resolution; and enhanced documentation for maintainability. Key outcomes include AiM/Newton device integration with perf-energy modeling and exposure of PIM core variable, GDDR memory support, Aquabolt updates, GEMV integration and timing, IO/printf optimizations for performance and Halide compatibility, fine-grained DRAM params and bank-level modeling refinements, allocation fixes for horizontal PIM, and comprehensive documentation improvements. These workstreams collectively advance benchmarking accuracy, device coverage, and maintainability while reducing risk in release cycles.
33 Commits • 10 Features
Jul 1, 2025Monthly performance summary for 2025-07: Delivered expanded device support, memory technology integration, and performance-energy modeling improvements; stabilized builds through cleanup and warnings resolution; and enhanced documentation for maintainability. Key outcomes include AiM/Newton device integration with perf-energy modeling and exposure of PIM core variable, GDDR memory support, Aquabolt updates, GEMV integration and timing, IO/printf optimizations for performance and Halide compatibility, fine-grained DRAM params and bank-level modeling refinements, allocation fixes for horizontal PIM, and comprehensive documentation improvements. These workstreams collectively advance benchmarking accuracy, device coverage, and maintainability while reducing risk in release cycles.
July 2025
June 2025
6 Commits • 2 Features
Jun 1, 2025June 2025 – UVA-LavaLab/PIMeval-PIMbench: Focused on performance accuracy, reliability, and maintainability. Implemented key feature integrations, stabilized benchmarks, and improved energy/perf modeling, resulting in more reliable performance estimates and cleaner code.
June 2025
6 Commits • 2 Features
Jun 1, 2025June 2025 – UVA-LavaLab/PIMeval-PIMbench: Focused on performance accuracy, reliability, and maintainability. Implemented key feature integrations, stabilized benchmarks, and improved energy/perf modeling, resulting in more reliable performance estimates and cleaner code.
May 2025
12 Commits • 1 Features
May 1, 2025May 2025 monthly summary for UVA-LavaLab/PIMeval-PIMbench. The month focused on delivering PIM-based acceleration for VGG workloads, expanding benchmarking coverage, and tightening metric accuracy. Key outcomes include end-to-end PIM-accelerated convolution and softmax for VGG with layer-wide validation, batched operations, and PIM+Host integration, plus expanded benchmarking with taco config 16 ranks. Additionally, CUDA Prefix Sum and bitserial optimization paths were implemented, with radix-sort prefix sum enhancements to improve scalability. Infrastructure improvements include a Slurm script to streamline experiments. A major bug fix corrected AES performance and energy metric calculations, improving reliability of performance reporting. The work enabled faster VGG inference with PIM accelerators, more representative benchmarking across configurations, and more trustworthy energy/performance metrics, supporting data-driven optimization and decision-making for GPU-PIM deployments.
12 Commits • 1 Features
May 1, 2025May 2025 monthly summary for UVA-LavaLab/PIMeval-PIMbench. The month focused on delivering PIM-based acceleration for VGG workloads, expanding benchmarking coverage, and tightening metric accuracy. Key outcomes include end-to-end PIM-accelerated convolution and softmax for VGG with layer-wide validation, batched operations, and PIM+Host integration, plus expanded benchmarking with taco config 16 ranks. Additionally, CUDA Prefix Sum and bitserial optimization paths were implemented, with radix-sort prefix sum enhancements to improve scalability. Infrastructure improvements include a Slurm script to streamline experiments. A major bug fix corrected AES performance and energy metric calculations, improving reliability of performance reporting. The work enabled faster VGG inference with PIM accelerators, more representative benchmarking across configurations, and more trustworthy energy/performance metrics, supporting data-driven optimization and decision-making for GPU-PIM deployments.
May 2025
April 2025
22 Commits • 11 Features
Apr 1, 2025April 2025 performance summary for UVA-LavaLab/PIMeval-PIMbench. Delivered a coordinated set of feature updates and bug fixes across the PIMbench suite, enhancing configurability, energy modeling accuracy, and benchmarking coverage. Key outcomes include TACO configuration updates, convolution and fulcrum modeling refinements, HBM/bank-level energy modeling improvements, CUDA-related power tooling and benchmarking enhancements, and CPU makefile/benchmark optimizations. These efforts improve energy/latency estimation fidelity, broaden hardware support, and streamline end-to-end benchmarking for faster optimization cycles.
April 2025
22 Commits • 11 Features
Apr 1, 2025April 2025 performance summary for UVA-LavaLab/PIMeval-PIMbench. Delivered a coordinated set of feature updates and bug fixes across the PIMbench suite, enhancing configurability, energy modeling accuracy, and benchmarking coverage. Key outcomes include TACO configuration updates, convolution and fulcrum modeling refinements, HBM/bank-level energy modeling improvements, CUDA-related power tooling and benchmarking enhancements, and CPU makefile/benchmark optimizations. These efforts improve energy/latency estimation fidelity, broaden hardware support, and streamline end-to-end benchmarking for faster optimization cycles.
March 2025
50 Commits • 14 Features
Mar 1, 2025March 2025 monthly summary for UVA-LavaLab/PIMeval-PIMbench: Delivered substantial energy modeling and performance enhancements across Fulcrum, Bitserial, and bank-level components, along with build-system improvements and targeted bug fixes. The work enabled clearer performance-per-watt insights, more accurate energy accounting, and a more maintainable codebase, accelerating benchmarking and optimization cycles.
50 Commits • 14 Features
Mar 1, 2025March 2025 monthly summary for UVA-LavaLab/PIMeval-PIMbench: Delivered substantial energy modeling and performance enhancements across Fulcrum, Bitserial, and bank-level components, along with build-system improvements and targeted bug fixes. The work enabled clearer performance-per-watt insights, more accurate energy accounting, and a more maintainable codebase, accelerating benchmarking and optimization cycles.
March 2025
February 2025
32 Commits • 18 Features
Feb 1, 2025February 2025 achieved meaningful progress on benchmarking utilities, feature expansion, and reliability for UVA-LavaLab/PIMeval-PIMbench. The month delivered a refactor/cleanup of benchmark utilities, configuration-driven load balancing improvements, convolution-related enhancements, richer timing metrics, and energy/operand modeling improvements while maintaining backward compatibility and improving test hygiene. These efforts collectively enhanced benchmarking accuracy, configurability, and maintainability, enabling faster release cycles and clearer decision signals for performance comparisons.
February 2025
32 Commits • 18 Features
Feb 1, 2025February 2025 achieved meaningful progress on benchmarking utilities, feature expansion, and reliability for UVA-LavaLab/PIMeval-PIMbench. The month delivered a refactor/cleanup of benchmark utilities, configuration-driven load balancing improvements, convolution-related enhancements, richer timing metrics, and energy/operand modeling improvements while maintaining backward compatibility and improving test hygiene. These efforts collectively enhanced benchmarking accuracy, configurability, and maintainability, enabling faster release cycles and clearer decision signals for performance comparisons.
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 (Month: 2025-01) – PIMbench work focused on API simplification and maintainability. Delivered a Histogram API refactor to use the general reduction API pimRedSum, setting the foundation for a more consistent benchmarking stack.
1 Commits • 1 Features
Jan 1, 2025January 2025 (Month: 2025-01) – PIMbench work focused on API simplification and maintainability. Delivered a Histogram API refactor to use the general reduction API pimRedSum, setting the foundation for a more consistent benchmarking stack.
January 2025
December 2024
16 Commits • 1 Features
Dec 1, 2024December 2024 focused on expanding FP support, improving memory timing accuracy, and strengthening performance/energy visibility in PIMbench. The changes enhance correctness, predictability, and test coverage, enabling more accurate hardware-modeling decisions and reducing risk in future adaptations. Highlights include FP8/FP16/BF16 reductions, generalization of FP broadcast naming to fix compile-time issues, DDR/LPDDR timing corrections, and a refined reduction performance/energy model with improved logging.
December 2024
16 Commits • 1 Features
Dec 1, 2024December 2024 focused on expanding FP support, improving memory timing accuracy, and strengthening performance/energy visibility in PIMbench. The changes enhance correctness, predictability, and test coverage, enabling more accurate hardware-modeling decisions and reducing risk in future adaptations. Highlights include FP8/FP16/BF16 reductions, generalization of FP broadcast naming to fix compile-time issues, DDR/LPDDR timing corrections, and a refined reduction performance/energy model with improved logging.
November 2024
6 Commits • 3 Features
Nov 1, 2024November 2024 monthly summary focused on expanding hardware evaluation coverage, enhancing data handling, and standardizing performance benchmarks for UVA-LavaLab/PIMeval-PIMbench. Delivered key features for AQUABOLT PIM device support, introduced a flexible data container via std::variant, and generalized reduction operations across core and benchmarks with new REDMIN/REDMAX capabilities. Updated tests and benchmarks to validate the new API and ensure consistent performance comparisons. Overall, strengthened hardware evaluation capabilities, data integrity, and benchmarking reliability with clear technical foundations for future expansions. No major bugs fixed this month; stability improvements came from API refactors and enhanced test coverage.
6 Commits • 3 Features
Nov 1, 2024November 2024 monthly summary focused on expanding hardware evaluation coverage, enhancing data handling, and standardizing performance benchmarks for UVA-LavaLab/PIMeval-PIMbench. Delivered key features for AQUABOLT PIM device support, introduced a flexible data container via std::variant, and generalized reduction operations across core and benchmarks with new REDMIN/REDMAX capabilities. Updated tests and benchmarks to validate the new API and ensure consistent performance comparisons. Overall, strengthened hardware evaluation capabilities, data integrity, and benchmarking reliability with clear technical foundations for future expansions. No major bugs fixed this month; stability improvements came from API refactors and enhanced test coverage.
November 2024
Activity
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Quality Metrics
Correctness87.0%
Maintainability85.6%
Architecture81.4%
Performance81.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
AssemblyCC++CUDAConfigurationHeaderINIMakefilePythonShell
Technical Skills
API DevelopmentAPI IntegrationAPI RefactoringAlgorithm AnalysisAlgorithm ImplementationAlgorithm OptimizationAlgorithm RefactoringBackward CompatibilityBenchmarkingBug FixingBuild SystemBuild System ConfigurationBuild SystemsC Standard LibraryC++
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