OpenXiangShan/GEM5
Sep 2025 – Sep 2025
1 Month active
Languages Used
C++HaskellPython
Technical Skills
CPU ArchitectureCache CoherenceMemory System DesignPerformance Optimization
Xincheng Cao
PROFILE
Xincheng Cao
Worked on enhancing the memory subsystem within the OpenXiangShan/GEM5 repository by implementing per-cycle single-entry MSHR arbitration and introducing configurable allocation limits to cache structures. This approach improved the fidelity of memory system performance modeling by restricting MSHR allocations to one per cycle and refining the handling of alias failures and write buffer hits, thereby reducing stalls and increasing robustness. Leveraged expertise in CPU architecture, cache coherence, and memory system design, utilizing C++ and Python to deliver these targeted improvements. The work enabled more accurate hardware-software co-design analyses and contributed to the stability and throughput of the GEM5 simulation environment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
330
Activity Months1
Your Network
94 peopleSame Organization
@gatech.edu67
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Akshat-ShenoiMember
Swami, AdityaMember
bihanMember
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bpatel347Member
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Shared Repositories
27
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary focusing on key implementations, stability, and impact across the GEM5 repository (OpenXiangShan/GEM5). Highlighted a targeted improvement in the memory subsystem with per-cycle MSHR arbitration and configurable allocation limits, plus robustness enhancements for alias failure and write buffer hit scenarios. This work advances memory subsystem performance modeling fidelity and throughput, enabling more accurate hardware-software co-design analyses.
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary focusing on key implementations, stability, and impact across the GEM5 repository (OpenXiangShan/GEM5). Highlighted a targeted improvement in the memory subsystem with per-cycle MSHR arbitration and configurable allocation limits, plus robustness enhancements for alias failure and write buffer hit scenarios. This work advances memory subsystem performance modeling fidelity and throughput, enabling more accurate hardware-software co-design analyses.
September 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++HaskellPython
Technical Skills
CPU ArchitectureCache CoherenceMemory System DesignPerformance Optimization
Repositories Contributed To
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