{"title":"基于 NoC 的 MPSoC 上流媒体应用的能量感知任务调度","authors":"Suhaimi Abd Ishak , Hui Wu , Umair Ullah Tariq","doi":"10.1016/j.jksuci.2024.102082","DOIUrl":null,"url":null,"abstract":"<div><p>Streaming applications are being extensively run on portable embedded systems, which are battery-operated and with limited memory. Thus, minimizing the total energy consumption of such a system is important. We investigate the problem of offline scheduling for streaming applications composed of non-preemptible periodic dependent tasks on homogeneous Network-on-Chip (NoC)-based Multiprocessor System-on-Chip (MPSoCs) such that their total energy consumption is minimized under memory constraints. We propose a novel unified approach that integrates task-level software pipelining with Dynamic Voltage and Frequency Scaling (DVFS) to solve the problem. Our approach is supported by a set of novel techniques, which include constructing an initial schedule based on a list scheduling where the priority of each task is its approximate successor-tree-consistent deadline such that the workload across all the processors is balanced, a retiming heuristic to transform intra-period dependencies into inter-period dependencies for enhancing parallelism, assigning an optimal discrete frequency for each task and each message using a Non-Linear Programming (NLP)-based algorithm and an Integer-Linear Programming (ILP)-based algorithm, and an incremental approach to reduce the memory usage of the retimed schedule in case of memory size violations. Using a set of real and synthetic benchmarks, we have implemented and compared our unified approach with two state-of-the-art approaches, RDAG+GeneS (<span>Wang et al., 2011</span>) , and JCCTS (<span>Wang et al., 2013a</span>). Experimental results show that our approach’s maximum, average, and minimum improvements over RDAG+GeneS (<span>Wang et al., 2011</span>) are 31.72%, 14.05%, and 7.00%, respectively. Our approach’s maximum, average, and minimum improvement over JCCTS (<span>Wang et al., 2013a</span>) are 35.58%, 17.04%, and 8.21%, respectively.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S131915782400171X/pdfft?md5=9b5bfc05f1869e383654a6be6e74e93b&pid=1-s2.0-S131915782400171X-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Energy-aware task scheduling for streaming applications on NoC-based MPSoCs\",\"authors\":\"Suhaimi Abd Ishak , Hui Wu , Umair Ullah Tariq\",\"doi\":\"10.1016/j.jksuci.2024.102082\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Streaming applications are being extensively run on portable embedded systems, which are battery-operated and with limited memory. Thus, minimizing the total energy consumption of such a system is important. We investigate the problem of offline scheduling for streaming applications composed of non-preemptible periodic dependent tasks on homogeneous Network-on-Chip (NoC)-based Multiprocessor System-on-Chip (MPSoCs) such that their total energy consumption is minimized under memory constraints. We propose a novel unified approach that integrates task-level software pipelining with Dynamic Voltage and Frequency Scaling (DVFS) to solve the problem. Our approach is supported by a set of novel techniques, which include constructing an initial schedule based on a list scheduling where the priority of each task is its approximate successor-tree-consistent deadline such that the workload across all the processors is balanced, a retiming heuristic to transform intra-period dependencies into inter-period dependencies for enhancing parallelism, assigning an optimal discrete frequency for each task and each message using a Non-Linear Programming (NLP)-based algorithm and an Integer-Linear Programming (ILP)-based algorithm, and an incremental approach to reduce the memory usage of the retimed schedule in case of memory size violations. Using a set of real and synthetic benchmarks, we have implemented and compared our unified approach with two state-of-the-art approaches, RDAG+GeneS (<span>Wang et al., 2011</span>) , and JCCTS (<span>Wang et al., 2013a</span>). Experimental results show that our approach’s maximum, average, and minimum improvements over RDAG+GeneS (<span>Wang et al., 2011</span>) are 31.72%, 14.05%, and 7.00%, respectively. Our approach’s maximum, average, and minimum improvement over JCCTS (<span>Wang et al., 2013a</span>) are 35.58%, 17.04%, and 8.21%, respectively.</p></div>\",\"PeriodicalId\":48547,\"journal\":{\"name\":\"Journal of King Saud University-Computer and Information Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S131915782400171X/pdfft?md5=9b5bfc05f1869e383654a6be6e74e93b&pid=1-s2.0-S131915782400171X-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of King Saud University-Computer and Information Sciences\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S131915782400171X\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of King Saud University-Computer and Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S131915782400171X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Energy-aware task scheduling for streaming applications on NoC-based MPSoCs
Streaming applications are being extensively run on portable embedded systems, which are battery-operated and with limited memory. Thus, minimizing the total energy consumption of such a system is important. We investigate the problem of offline scheduling for streaming applications composed of non-preemptible periodic dependent tasks on homogeneous Network-on-Chip (NoC)-based Multiprocessor System-on-Chip (MPSoCs) such that their total energy consumption is minimized under memory constraints. We propose a novel unified approach that integrates task-level software pipelining with Dynamic Voltage and Frequency Scaling (DVFS) to solve the problem. Our approach is supported by a set of novel techniques, which include constructing an initial schedule based on a list scheduling where the priority of each task is its approximate successor-tree-consistent deadline such that the workload across all the processors is balanced, a retiming heuristic to transform intra-period dependencies into inter-period dependencies for enhancing parallelism, assigning an optimal discrete frequency for each task and each message using a Non-Linear Programming (NLP)-based algorithm and an Integer-Linear Programming (ILP)-based algorithm, and an incremental approach to reduce the memory usage of the retimed schedule in case of memory size violations. Using a set of real and synthetic benchmarks, we have implemented and compared our unified approach with two state-of-the-art approaches, RDAG+GeneS (Wang et al., 2011) , and JCCTS (Wang et al., 2013a). Experimental results show that our approach’s maximum, average, and minimum improvements over RDAG+GeneS (Wang et al., 2011) are 31.72%, 14.05%, and 7.00%, respectively. Our approach’s maximum, average, and minimum improvement over JCCTS (Wang et al., 2013a) are 35.58%, 17.04%, and 8.21%, respectively.
期刊介绍:
In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.