Method and system for GPU virtualization based on container

Abstract

A GPU virtualization method based on a container comprises the steps of: transmitting, if the container is created, a configuration file including GPU resource constraint information and an API profile to the container, by a node controller; and implementing a virtual GPU, when the container is executed, by intercepting a library call and changing an argument related to a GPU resource amount by a library controller provided in the container, and by intercepting a system call and changing argument and return values by a system call controller.

Description

TECHNICAL FIELD[0001]The present invention relates to a method and a system for GPU virtualization based on a container, and particularly, to a method and a system for GPU virtualization based on a container, which implements the GPU virtualization by changing argument values or the like related to GPU resources, by a library controller and a system controller in the container.BACKGROUND ART[0002]Recently, virtualization techniques are used much to improve efficiency, security and compatibility of large-scale computing for multiple users. Representatively, there is a virtual machine, which is applied in various fields such as applications, servers, storages, networks and the like. However, although the level of compatibility and isolation is the highest since the virtual machine virtualizes all physical hardware components from CPUs to disks, networks and even I / O devices, there is a disadvantage in that additional consumption (overheads) of computing resources is large.[0003]Meanwh...

AI Technical Summary

Benefits of technology

The present invention is a technique to allocate multiple graphics processing units (GPUs) to containers in a graphical user interface (GUI) computing system. This allows for efficient use of system resources and easy update of applications by moving them between containers. The technique uses a container virtualization technique, which simplifies the scaling process. Overall, this invention results in a more efficient and flexible GPU computing system.

Problems solved by technology

However, although the level of compatibility and isolation is the highest since the virtual machine virtualizes all physical hardware components from CPUs to disks, networks and even I / O devices, there is a disadvantage in that additional consumption (overheads) of computing resources is large.

However, a virtualization technique based on a container provided by an existing operating system supports only sharing and limitation of resources for the CPU, memory, disk, and file system of each container, and a technique for simultaneously sharing the accelerated processing hardware like GPU among several containers is not provided.

Accordingly, there is a difficulty in efficiently sharing and managing the GPU.

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