List scheduling algorithm of improved priority with considering load balance

Bo, Ye; Benfa, Zhang; Li, Feng; Zhang, Xiaoyu; Li, Xinjuan; Wang, Fang; Zhitao, Zhan

doi:10.1109/icdsca53499.2021.9650131

Cited by 1 publication

(2 citation statements)

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“…A clustering scheduling strategy maps the grouped tasks into an infinite number of computational units. In the steps of the clustering algorithm, each step of the clustering is an arbitrary task that may not be the ready task, so it is necessary to go back in each group to select the ready task for scheduling, where there is [16] formalized the scheduling problem as an integer planning clustering problem with grouped clustering scheduling [17]. Scheduling algorithms based on replication strategies are also task computational resources are unbounded and replication-based strategies use redundant tasks to reduce communication overhead [18,19].…”

Section: Related Workmentioning

confidence: 99%

“…If a given HSDFG has multiple terminating instances, then makespan is defined as follows. makespan = max(AFT(α exit )) (16) Our goal is to make full use of the limited number of processor cores to minimize s. Therefore, the objective function is defined as follows: min makespan 4.3.2. Instance Selection level(α i ) is the level value of instance α i , which represents the sum of the execution times of all instances contained in the longest path from α i to the endpoint instance, as defined below.…”

Section: Notation Descriptionmentioning

confidence: 99%

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Improved Scheduling Algorithm for Synchronous Data Flow Graphs on a Homogeneous Multi-Core Systems

Wang

2022

Algorithms

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In order to accelerate the execution of streaming applications on multi-core systems, this article studies the scheduling problem of synchronous data flow graphs (SDFG) on homogeneous multi-core systems. To describe the data flow computation process, we propose the SDAG (Super DAG) computation model based on the DAG model combined with data-driven thoughts. Further, we analyze the current common SDFG scheduling algorithms and propose an improved SDFG scheduling algorithm, LSEFT (level-shortest-first-earliest-finish time). The LSEFT algorithm uses an inverse traversal algorithm to calculate the priority of tasks in the task-selection phase; the shortest-job-priority earliest-finish-time policy is used in the processor selection phase to replace the original long job priority policy. In the experimental part, we designed an SDFG random generator and generated 958 SDFGs with the help of the random generator as test cases to verify the scheduling algorithm. The experimental results show that our improved algorithm performs well for different numbers of processor cores, especially for 8 cores, where the speedup of our improved algorithm improves by 10.17% on average.

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Section: Related Workmentioning

confidence: 99%

Section: Notation Descriptionmentioning

confidence: 99%