{"title":"基于并行群的独立任务调度算法","authors":"Robert Dietze, Maximilian Kränert","doi":"10.3233/his-230006","DOIUrl":null,"url":null,"abstract":"Task scheduling is crucial for achieving high performance in parallel computing. Since task scheduling is NP-hard, the efficient assignment of tasks to compute resources remains an issue. Across the literature, several algorithms have been proposed to solve different scheduling problems. One group of promising approaches in this field is formed by swarm-based algorithms which have a potential to benefit from a parallel execution. Common swarm-based algorithms are Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). In this article, we propose two new scheduling methods based on parallel ACO, PSO and, Hill Climbing, respectively. These algorithms are used to solve the problem of scheduling independent tasks onto heterogeneous multicore platforms. The results of performance measuements demonstrate the improvements on the makespan and the scheduling time achieved by the parallel variants.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"9 1","pages":"79-93"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Parallel swarm-based algorithms for scheduling independent tasks\",\"authors\":\"Robert Dietze, Maximilian Kränert\",\"doi\":\"10.3233/his-230006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Task scheduling is crucial for achieving high performance in parallel computing. Since task scheduling is NP-hard, the efficient assignment of tasks to compute resources remains an issue. Across the literature, several algorithms have been proposed to solve different scheduling problems. One group of promising approaches in this field is formed by swarm-based algorithms which have a potential to benefit from a parallel execution. Common swarm-based algorithms are Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). In this article, we propose two new scheduling methods based on parallel ACO, PSO and, Hill Climbing, respectively. These algorithms are used to solve the problem of scheduling independent tasks onto heterogeneous multicore platforms. The results of performance measuements demonstrate the improvements on the makespan and the scheduling time achieved by the parallel variants.\",\"PeriodicalId\":88526,\"journal\":{\"name\":\"International journal of hybrid intelligent systems\",\"volume\":\"9 1\",\"pages\":\"79-93\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of hybrid intelligent systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/his-230006\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of hybrid intelligent systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/his-230006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parallel swarm-based algorithms for scheduling independent tasks
Task scheduling is crucial for achieving high performance in parallel computing. Since task scheduling is NP-hard, the efficient assignment of tasks to compute resources remains an issue. Across the literature, several algorithms have been proposed to solve different scheduling problems. One group of promising approaches in this field is formed by swarm-based algorithms which have a potential to benefit from a parallel execution. Common swarm-based algorithms are Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). In this article, we propose two new scheduling methods based on parallel ACO, PSO and, Hill Climbing, respectively. These algorithms are used to solve the problem of scheduling independent tasks onto heterogeneous multicore platforms. The results of performance measuements demonstrate the improvements on the makespan and the scheduling time achieved by the parallel variants.