jd-opensource/xllm
Sep 2025 – Sep 2025
1 Month active
Languages Used
C++MarkdownPythonShell
Technical Skills
Batch ProcessingC++ DevelopmentCMakeConfiguration ManagementDistributed SystemsDocumentation
guojinrong
PROFILE
Guojinrong
Worked on the jd-opensource/xllm repository to deliver multi-stream parallel processing with batched inputs, enhancing throughput and scalability for large language model inference. Refactored core components such as RemoteWorker and WorkerService to support batched data handling, and updated the engine to enable micro-batch splitting. Introduced an adaptive scheduling feature that defaults to overlapping execution, with exceptions for specific model types to maintain optimal performance. Updated documentation to reflect new usage patterns and configuration defaults. Leveraged C++, Python, and PyTorch, focusing on distributed systems, performance optimization, and system configuration to improve runtime efficiency and maintainability without introducing new bugs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
2
Lines of code
3,193
Activity Months1
Your Network
112 peopleSame Organization
@jd.com71
chenda14Member
chenjiajun79Member
chenxiaoyu8Member
cuimaoxin1Member
dengfei.59Member
Yingxu DengMember
2extliuweijian32Member
yq33victorMember
gechunfa1Member
Shared Repositories
41
Work History
September 2025
5 Commits • 2 Features
Sep 1, 20252025-09 monthly summary for jd-opensource/xllm: Key features delivered include multi-stream parallel processing with batched inputs, refactoring for batched data handling across RemoteWorker/WorkerService, engine micro-batch splitting, and updates to batch sampling, configurations, and dependencies to boost throughput. Also implemented Adaptive Enable Schedule Overlap with model-type exceptions by changing the default to true while excluding VLM/embedding models; docs updated to reflect new usage and defaults. No explicit major bug fixes documented; focus was on performance and scalability improvements.
5 Commits • 2 Features
Sep 1, 20252025-09 monthly summary for jd-opensource/xllm: Key features delivered include multi-stream parallel processing with batched inputs, refactoring for batched data handling across RemoteWorker/WorkerService, engine micro-batch splitting, and updates to batch sampling, configurations, and dependencies to boost throughput. Also implemented Adaptive Enable Schedule Overlap with model-type exceptions by changing the default to true while excluding VLM/embedding models; docs updated to reflect new usage and defaults. No explicit major bug fixes documented; focus was on performance and scalability improvements.
September 2025
Activity
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Quality Metrics
Correctness86.0%
Maintainability80.0%
Architecture86.0%
Performance86.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++MarkdownPythonShell
Technical Skills
Batch ProcessingC++ DevelopmentCMakeConfiguration ManagementDistributed SystemsDocumentationFeature ImplementationLLM InferenceModel ParallelismNPU AccelerationParallel ProcessingPerformance OptimizationPyTorchPython DevelopmentRefactoring
Repositories Contributed To
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