Affinity-based task scheduling on heterogeneous multicore systems using cbs and qbictm

Sohaib Iftikhar Abbasi, Shaharyar Kamal, Munkhjargal Gochoo, Ahmad Jalal, Kibum Kim

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

This work presents the grouping of dependent tasks into a cluster using the Bayesian analysis model to solve the affinity scheduling problem in heterogeneous multicore systems. The non-affinity scheduling of tasks has a negative impact as the overall execution time for the tasks increases. Furthermore, non-affinity-based scheduling also limits the potential for data reuse in the caches so it becomes necessary to bring the same data into the caches multiple times. In heterogeneous multicore systems, it is essential to address the load balancing problem as all cores are operating at varying frequencies. We propose two techniques to solve the load balancing issue, one being designated “chunk-based scheduler” (CBS) which is applied to the heterogeneous systems while the other system is “quantum-based intra-core task migration” (QBICTM) where each task is given a fair and equal chance to run on the fastest core. Results show 30–55% improvement in the average execution time of the tasks by applying our CBS or QBICTM scheduler compare to other traditional schedulers when compared using the same operating system.

Original languageEnglish
Article number5740
JournalApplied Sciences (Switzerland)
Volume11
Issue number12
DOIs
Publication statusPublished - Jun 2 2021

Keywords

  • Affinity-based scheduling
  • Bayesian generative model
  • High-performance computing
  • Load balancing
  • Parallel computing

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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