jd-opensource/xllm
Sep 2025 – Sep 2025
1 Month active
Languages Used
C++CMake
Technical Skills
C++CMakeGraph ExecutionMemory ManagementNPU OptimizationPerformance Engineering
Zhang Minchao
PROFILE
Zhang Minchao
Worked on the jd-opensource/xllm repository to deliver an ACL Graph Execution feature targeting NPU-backed workloads. Developed a C++ and CMake-based graph executor that leverages NPUGraph for optimized execution, introducing configurable feature flags and maximum token controls for flexible deployment. Implemented a lazy graph capture and replay mechanism to optimize performance, while ensuring correctness through a safe fallback to eager execution when graph optimization was not applicable. Focused on reducing memory allocation overhead during the decode phase, resulting in improved throughput and latency. The work demonstrated depth in graph execution, memory management, and NPU optimization within a performance engineering context.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,364
Activity Months1
Your Network
51 peopleWork History
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for jd-opensource/xllm: Delivered the ACL Graph Execution feature for NPUs, enabling a graph-accelerated execution path via NPUGraph with configurable options and a safe fallback to eager execution. Implemented lazy graph capture and replay to optimize performance while preserving correctness when graph optimization isn't applicable. This work targets reduced memory allocation overhead during the decode phase and higher throughput on NPU-backed deployments.
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for jd-opensource/xllm: Delivered the ACL Graph Execution feature for NPUs, enabling a graph-accelerated execution path via NPUGraph with configurable options and a safe fallback to eager execution. Implemented lazy graph capture and replay to optimize performance while preserving correctness when graph optimization isn't applicable. This work targets reduced memory allocation overhead during the decode phase and higher throughput on NPU-backed deployments.
September 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CMake
Technical Skills
C++CMakeGraph ExecutionMemory ManagementNPU OptimizationPerformance Engineering
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