[Perf] Add stream priority in gpu context#2728
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Signed-off-by: YaoJiayi <120040070@link.cuhk.edu.cn>
Summary of ChangesHello, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request enhances performance by integrating high-priority CUDA streams into the GPU context management. By dedicating a high-priority stream for specific operations, such as memory retrieval, the system can ensure that critical tasks are processed more quickly, potentially reducing latency and improving overall responsiveness for these performance-sensitive workflows. Highlights
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Code Review
This pull request integrates high-priority CUDA streams into the GPU context for performance optimization, particularly for retrieve operations. However, this change introduces a critical race condition: retrieval and storage operations now use different streams while sharing the same temporary GPU buffer, which can lead to data corruption. Additionally, server-side cleanup logic in the retrieval path is not correctly synchronized. Furthermore, there is significant code duplication in lmcache/v1/multiprocess/gpu_context.py regarding the initialization and property definitions for cuda_stream_, high_priority_cuda_stream_, cupy_stream_, and high_priority_cupy_stream_ across GPUCacheContext and PlainGPUCacheContext, which should be refactored for better maintainability.
add stream priority Signed-off-by: YaoJiayi <120040070@link.cuhk.edu.cn>
add stream priority Signed-off-by: YaoJiayi <120040070@link.cuhk.edu.cn> Signed-off-by: Aaron Wu <aaron.wu@dell.com>
add stream priority Signed-off-by: YaoJiayi <120040070@link.cuhk.edu.cn>
add stream priority Signed-off-by: YaoJiayi <120040070@link.cuhk.edu.cn>
What this PR does / why we need it:
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