A medical artificial intelligence and high-performance computing resource scheduling system and scheduling method

A high-performance computing and artificial intelligence technology, applied in the field of big data processing, can solve problems such as inability to perform high-performance computing, low effective utilization rate, and complex management, so as to improve hardware utilization rate and payload, construction and operation and maintenance costs The effect of low cost and simple system architecture

Active Publication Date: 2022-04-26
SICHUAN UNIV +1
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of the fact that artificial intelligence generally uses physical machines for resource allocation, there is no job scheduling mechanism, the effective utilization rate is low, and there are often insufficient machines; although a small number of training and development platforms for artificial intelligence have appeared, they cannot perform high-performance computing; and artificial intelligence and high-performance Computing is built separately, with high construction costs, complex management, and low resource reuse; clusters generally use shared storage, which requires high storage performance and high construction costs

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  • A medical artificial intelligence and high-performance computing resource scheduling system and scheduling method
  • A medical artificial intelligence and high-performance computing resource scheduling system and scheduling method

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[0035] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments It is a part of the embodiments of this application, not all of them. The components of the embodiments of the application generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations. Accordingly, the following detailed description of the embodiments of the application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the application. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without cr...

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Abstract

The invention discloses a medical artificial intelligence and high-performance computing resource scheduling system and a scheduling method. The system includes: a user login system: used for customers to log in to the system from an office environment, and provides functions such as job submission, data upload and download; Management system: used to deploy basic management processes and services such as database and monitoring; job scheduling system: through unified scheduling and management of computing resources such as CPU, GPU, FPGA, memory, etc.; container mirror library: used to store container mirror templates and User-defined or self-defined images; computing resource system: physical resources for computing; parallel file system: used to store data uploaded by users, data completed by calculation, and other data; the subsystems are connected through high-speed network communication. The invention can realize integrated scheduling of artificial intelligence and high-performance computing resources, reduce construction costs, and improve resource utilization.

Description

technical field [0001] The invention relates to the technical field of big data processing, in particular to a medical artificial intelligence and high-performance computing resource scheduling system and scheduling method. Background technique [0002] The medical field involves a lot of artificial intelligence and high-performance computing. In the field of high-performance computing, computing operations are mainly computing-intensive, and are executed on one or more computing nodes in the computing cluster through platform scheduling. Every job has different requirements for computing resources. The two most important indicators of computing resource requirements are the number of CPU cores and memory size, and the second indicators include storage, GPU, and bandwidth. After each computing job is assigned to the required resources and starts to execute, it cannot be interrupted or migrated. If an interruption occurs, it needs to be re-executed. The main function of the...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/50
CPCG06F9/5027G06F9/5083
Inventor 应志野李春漾陈一龙于浩澎龚力匡亚岚
Owner SICHUAN UNIV
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