OpenXiangShan/GEM5
Oct 2025 – Oct 2025
1 Month active
Languages Used
C++Python
Technical Skills
Branch PredictionCPU ArchitecturePerformance OptimizationSystem Simulation
Cao Jiaming
PROFILE
Cao Jiaming
Cao Jie worked on the OpenXiangShan/GEM5 repository, focusing on enhancing CPU branch prediction by integrating the MicroTAGE predictor into the S1 stage of the pipeline. This involved updating the predictor’s configuration and refactoring the existing branch prediction logic to ensure seamless integration and maintainability. Using C++ and Python, Cao Jie addressed performance optimization and system simulation challenges, delivering a targeted architectural improvement for dynamic branch prediction. The work demonstrated a clear understanding of CPU architecture and branch prediction mechanisms, resulting in a well-structured codebase that is ready for further evaluation and future optimization within the GEM5 simulation environment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
108
Activity Months1
Your Network
40 people
Work History
October 2025
1 Commits • 1 Features
Oct 1, 2025OpenXiangShan/GEM5 (2025-10) focused on a targeted performance enhancement by integrating MicroTAGE into the S1 stage of the CPU pipeline. This work adds a MicroTAGE branch predictor to S1, updates predictor configuration, and refactors code for maintainability and easier integration with existing branch-prediction logic. The effort delivers a concrete architectural advancement for dynamic branch prediction, ready for broader evaluation and future optimization. Delivered commit: 340af13e8310dbbcdf9797b1d77ead2159321d2b ("Microtage on s1 perf (#567)").
1 Commits • 1 Features
Oct 1, 2025OpenXiangShan/GEM5 (2025-10) focused on a targeted performance enhancement by integrating MicroTAGE into the S1 stage of the CPU pipeline. This work adds a MicroTAGE branch predictor to S1, updates predictor configuration, and refactors code for maintainability and easier integration with existing branch-prediction logic. The effort delivers a concrete architectural advancement for dynamic branch prediction, ready for broader evaluation and future optimization. Delivered commit: 340af13e8310dbbcdf9797b1d77ead2159321d2b ("Microtage on s1 perf (#567)").
October 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Branch PredictionCPU ArchitecturePerformance OptimizationSystem Simulation
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