A GPU resource pool scheduling system and method

Abstract

The invention discloses a GPU resource pool scheduling system, which includes a GPU cloud computing power center and a GPU cloud control node. The GPU cloud computing power center includes a plurality of GPU computing power units, and each GPU computing power unit includes VMM and RC. The control node includes RS; the GPU computing power unit is used to provide GPU computing power; VMM is used to provide a control interface and receive resource scheduling instructions from RS, create a virtual machine according to the instructions, allocate vGPU resources to the virtual machine and start the virtual machine; RC It is used to count the resource data of the GPU computing power unit and report it to RS; RS is used to collect the resource data reported by each RC, and send resource scheduling instructions to each VMM, and globally schedule the resources of the GPU computing power unit, including the allocation of GPU resources. Gather to form multiple groups of GPU hardware collections, and multiple groups of GPU hardware collections form a GPU resource pool; the system realizes the unified resource pool scheduling management of multiple manufacturers, GPU models, and GPU virtualization methods in the cloud computing platform; the invention also discloses A GPU resource pool scheduling method is proposed.

Description

technical field [0001] The invention relates to the technical field of cloud computing resource management, in particular to a GPU resource pool scheduling system and method. Background technique [0002] With the generalization of GPU, the use of GPU in data centers is increasing day by day, and it has very important uses in the fields of graphics and image rendering, parallel computing, and artificial intelligence; the important elements of data centers are mainly computing, storage and network; with the cloud With the in-depth development of computing, how to manage GPU resources more effectively in the data center has become very important. [0003] The various components of cloud computing and the computing part usually need to use virtualization technology. Virtualization technology includes CPU and GPU virtualization technology. The details of physical hardware are shielded through virtualized hardware, so that physical resources can be used more flexibly; while compu...

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Problems solved by technology

[0008] Although a variety of GPU manufacturers, product lines, and models can be used for different usage scenarios, such as image rendering and scientific computing; however, due to the existence of various GPU manufacturers, product lines, and models, it is difficult to implement the current GPU virtualization technology. Moreover, for large-scale GPU computing power centers, the business form is very complex, and there are usually multiple application scenarios such as graphics rendering, scientific computing, artificial intelligence, and oblique photography modeling; in actual business use, there are a large number of graphics in the GIS industry. Rendering and virtual desktop requirements, as well as a large number of oblique photographic modeling requirements and artificial intelligence requirements, while the user's graphics workstations, GPU servers and other resources are independent of each other, becoming information islands, unable to share resources, and the conversion of GPU resource usage scenarios Re-deployment of physical servers usually takes several days; therefore, how to effectively manage and schedule these GPU resources in the cloud computing architecture becomes particularly important

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