Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Dependency-Based Grouping Scheduling Method for Parallel Tasks on Multi-core Cluster Servers

A cluster server and group scheduling technology, which is applied in the field of high-performance computing, can solve problems such as the inability to realize parallel energy-saving scheduling of homogeneous multi-core processors, and achieve the effect of reducing task duplication, reducing the number of uses, and achieving a simple and efficient process

Active Publication Date: 2016-08-17
济南云腾智能科技有限公司
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problem that existing scheduling technologies cannot realize parallel energy-saving scheduling on homogeneous multi-core processors considering task duplication, the present invention provides a parallel task group scheduling method based on task dependencies in homogeneous multi-core cluster servers

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Dependency-Based Grouping Scheduling Method for Parallel Tasks on Multi-core Cluster Servers
  • Dependency-Based Grouping Scheduling Method for Parallel Tasks on Multi-core Cluster Servers
  • Dependency-Based Grouping Scheduling Method for Parallel Tasks on Multi-core Cluster Servers

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0033] The relevant definitions of the present invention are firstly given below, and then related algorithms are listed, and finally a specific example is given, and the present invention is further described in conjunction with the accompanying drawings.

[0034] The present invention proposes a task assignment algorithm based on task dependency. By calculating the dependency between execution paths, the execution path can be divided into Compared with the original algorithm, the paths with large dependencies are merged to achieve a more balanced processor load, reduce unnecessary task duplication, reduce communication overhead between processors, and reduce processor idle time energy consumption, thereby achieving overall Energy consumption is reduced.

[0035] According to the task execution parameters and dependencies, the initial task execution path is obtained, and then the execution paths are grouped and merged according to the degree of dependence between the task exe...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention particularly discloses a dependence-based parallel task packet scheduling method used on an isomorphic multi-core cluster server. Through calculating the dependence between execution paths and under the premise of not increasing the maximum loads of the execution paths, execution paths with large dependence are combined, compared with the original algorithm, the method can reduce unnecessary task duplication expenditure, communication expenditure between processors and the idle time energy consumption of the processors are reduced, the load of the processors is more balanced, and the overall energy consumption is lowered. The dependence-based parallel task packet scheduling method has the greatest features that the number of the processors is effectively reduced, the resource utilization efficiency is improved, the load of the processors is more balanced, and accordingly, the overall energy consumption is reduced.

Description

technical field [0001] The invention belongs to the field of high-performance computing, and specifically relates to a parallel scheduling method for multiple tasks in an operating system on a cluster server, that is, multiple parallel tasks are assigned to multiple processors for execution, so that the overall execution time or usage of all tasks The number of processors meets the requirements. Background technique [0002] With the rapid development of high-performance computers, large multi-processor cluster systems consume more and more energy, and energy efficiency has become one of the important factors that must be considered in high-performance computing. A cluster system usually consists of hundreds to tens of thousands of computing nodes, and each node includes multiple homogeneous multi-core processors. A cluster system can run multiple applications at the same time and provide services to multiple users. How to speed up the running speed of applications and red...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/50
CPCY02D10/00
Inventor 李新贾智平赵衍恒谢帅
Owner 济南云腾智能科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com