Managing Concurrent Workloads Within a Single GPU Process Remains a Challenge

Managing Concurrent Workloads Within a Single GPU Process Remains a Challenge GPU applications frequently execute multiple concurrent tasks within a single process, making resource sharing difficult to control. Applications often combine latency-sensitive tasks with throughput-oriented kernels, preprocessing with inference, or multi-stage processing workflows on one GPU. Controlling how GPU resources are divided among these concurrent components inside a single process remains difficult.

GPU多组件并发运行下的资源共享难题 随着GPU应用日益在单个进程中同时运行多个独立组件，控制组件之间如何共享GPU资源依然面临挑战。 GPU应用越来越多地由同一进程内并发运行的多个独立组件构成，例如延迟敏感型算子与高吞吐后台内核，或数据预处理与模型推理。 在共享单个GPU的处理工作流各阶段或组件之间，有效控制GPU资源的分配与共享仍十分困难。

GPU applications increasingly consist of multiple independent components running at the same time within a single process: a latency-sensitive operator alongside a throughput-oriented background kernel; a data preprocessing stage alongside model inference; or multiple stages of a processing workflow sharing a single GPU. Controlling how GPU resources are shared between them remains difficult.