Programming Model for Resource-Constrained Scheduling

    公开(公告)号:US20210294660A1

    公开(公告)日:2021-09-23

    申请号:US17204508

    申请日:2021-03-17

    Abstract: The present technology augments the GPU compute model to provide system-provided data marshalling characteristics of graphics pipelining to increase efficiency and reduce overhead. A simple scheduling model based on scalar counters semaphores) abstract the availability of hardware resources. Resource releases can be done programmatically, and a system scheduler only needs to track the states of such counters/semaphores to make work launch decisions. Semantics of the counters/sema.phores are defined by an application, which can use the counters/semaphores to represent the availability of free space in a memory buffer, the amount of cache pressure induced by the data flow in the network, or the presence of work items to be processed.

    Programming Model for Resource-Constrained Scheduling

    公开(公告)号:US20250094232A1

    公开(公告)日:2025-03-20

    申请号:US18961452

    申请日:2024-11-27

    Abstract: The present technology augments the GPU compute model to provide system-provided data marshalling characteristics of graphics pipelining to increase efficiency and reduce overhead. A simple scheduling model based on scalar counters (e.g., semaphores) abstract the availability of hardware resources. Resource releases can be done programmatically, and a system scheduler only needs to track the states of such counters/semaphores to make work launch decisions. Semantics of the counters/semaphores are defined by an application, which can use the counters/semaphores to represent the availability of free space in a memory buffer, the amount of cache pressure induced by the data flow in the network, or the presence of work items to be processed.

    SIMULTANEOUS COMPUTE AND GRAPHICS SCHEDULING

    公开(公告)号:US20200043123A1

    公开(公告)日:2020-02-06

    申请号:US16053341

    申请日:2018-08-02

    Abstract: A parallel processing unit (e.g., a GPU), in some examples, includes a hardware scheduler and hardware arbiter that launch graphics and compute work for simultaneous execution on a SIMD/SIMT processing unit. Each processing unit (e.g., a streaming multiprocessor) of the parallel processing unit operates in a graphics-greedy mode or a compute-greedy mode at respective times. The hardware arbiter, in response to a result of a comparison of at least one monitored performance or utilization metric to a user-configured threshold, can selectively cause the processing unit to run one or more compute work items from a compute queue when the processing unit is operating in the graphics-greedy mode, and cause the processing unit to run one or more graphics work items from a graphics queue when the processing unit is operating in the compute-greedy mode. Associated methods and systems are also described.

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