A method for flexible scheduling of GPU resources based on heterogeneous application platforms

Abstract

The flexible scheduling method for GPU resources based on heterogeneous application platforms of the present invention includes: a). Obtaining GPU resource utilization information; b). Setting trigger thresholds and times; c). Screening and sorting shrinking platform queues; d) .Screen and sort the expansion platform queue; 1).Select the platform to be scaled down; 2).Build a GPU node list; 3).Process the locked state node; 4).Offline the node to be migrated; 5).Add to the resource queue; 6). Judging whether the shrinkage is completed. The GPU resource elastic scheduling method of the present invention can be flexibly adjusted according to the overall platform GPU load, thereby realizing the maximum utilization of platform GPU resources. Resource monitoring, information collection, and delivery of execution operations can meet the rapid and flexible deployment and implementation of cloud computing, big data, artificial intelligence, and high-performance computing scene platforms.

Description

technical field [0001] The present invention relates to a method for flexible scheduling of GPU resources, and more specifically, to a method for flexible scheduling of GPU resources based on a heterogeneous application platform. Background technique [0002] Graphics processing unit (GPU) resources have been increasingly used in the fields of cloud computing, artificial intelligence, and high-performance computing in recent years due to their excellent parallel computing capabilities, higher bandwidth, and main frequency. . At the same time, because the price of GPU resources is generally higher than that of CPU, GPU resources are scarce resources in different computing application scenarios. In order to improve the utilization rate of GPU resources, it is generally realized mainly through resource scheduling. [0003] GPU resource scheduling can generally be divided into task-level scheduling, hardware-level scheduling, and node-level scheduling. The task-level schedulin...

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Problems solved by technology

However, this method must be unique to the scheduling system used on the same platform, otherwise it will lead to inconsistent scheduling information for GPU resources, resulting in resource occupation conflicts; the commonly used scheduling platform for physical node-level scheduling is the same as the hardware-level scheduling platform, and all GPU computing nodes belong to It is based on a certain resource management platform and does not involve sharing and multiplexing. Its elastic expansion is limited to the inside of the platform. It does not support heterogeneous resources and heterogeneous application expansion, which is not conducive to improving the overall GPU resource utilization of the platform.

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