[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84764-en":3,"doc-seo-84764-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84764,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Performance Evaluation of Scheduling Tasks in Many-Core Systems Using Processes and Threads","This study evaluates scalability of process-based and thread-based schedulers for many-core shared-memory systems using a memory-intensive row-wise quick-sort workload on large 3D tensors. Process scheduling compares bounded prolific, bounded collective, and three pipe-based producer–consumer designs (one-to-one, one-to-many, many-to-many) with streaming task identifiers to workers to improve runtime load balancing. Thread scheduling analyzes static, dynamic, guided, chunk-based, chunkstealing, adaptive chunk, and AIMD strategies, where AIMD uses TCP-like additive-increase/multiplicative-decrease with EWMA CPU utilization to control a contention window. Experiments on a 24-core x86-64 platform show thread schedulers achieve the highest overall performance, with dynamic and guided approaches performing best, while pipe-based process designs scale strongly, selecting one-to-one for smaller workloads and many-to-many for larger ones.","Performance evaluation of scheduling tasks in many-core systems utilizing  \nprocesses and threads  \narXiv :2607 .0482 1v 1 [ cs .DC] 6 Jul 2026  \nMejgan Dedaj MicroComputer Systems Laboratory Dept. of Mathematics, University of Ioannina  \n[dedajmegi@gmail.com](dedajmegi@gmail.com)  \nTheofanis Ioannou MicroComputer Systems Laboratory Dept. of Mathematics, University of Ioannina  \n[th.ioannou01@gmail.com](th.ioannou01@gmail.com)  \nHermione Kimpouropoulou MicroComputer Systems Laboratory Dept. of Mathematics, University of Ioannina  \n[hermionekimp@gmail.com](hermionekimp@gmail.com)  \nKleopatra Kontogianni MicroComputer Systems Laboratory Dept. of Mathematics, University of Ioannina  \n[kontkleo13@yahoo.gr](kontkleo13@yahoo.gr)  \nMichail Panagiotidis Kannas MicroComputer Systems Laboratory Dept. of Mathematics, University of Ioannina  \n[kannasmichael@gmail.com](kannasmichael@gmail.com)  \nAnna Maria Sidiropoulou MicroComputer Systems Laboratory Dept. of Mathematics, University of Ioannina  \n[annasidi360@gmail.com](annasidi360@gmail.com)  \nArgyro Gailla MicroComputer Systems Laboratory Dept. of Mathematics, University of Ioannina  \n[silvigail@hotmail.com](silvigail@hotmail.com)  \nStamatia Kastrinaki MicroComputer Systems Laboratory Dept. of Mathematics, University of Ioannina  \n[matinakst@gmail.com](matinakst@gmail.com)  \nDimitrios Kontodimos MicroComputer Systems Laboratory Dept. of Mathematics, University of Ioannina  \n[Jimkon03@gmail.com](Jimkon03@gmail.com)  \nSotirios Kontogiannis* MicroComputer Systems Laboratory Dept. of Mathematics, University of Ioannina  \n[skontog@uoi.gr](skontog@uoi.gr)  \nAnastasia Papouda MicroComputer Systems Laboratory Dept. of Mathematics, University of Ioannina  \n[natasapapouda@gmail.com](natasapapouda@gmail.com)  \nGeorge Tavridis MicroComputer Systems Laboratory Dept. of Mathematics, University of Ioannina  \n[georgetavridis7@gmail.com](georgetavridis7@gmail.com)  \nAbstract  \nThis study assesses the scalability of process-based and thread-based schedulers for many-core shared-memory systems using a memory-intensive row-wise quick-sort workload on large three-dimensional tensors. The process-based evaluation considers bounded prolific, bounded collective, and three pipe-based producer-consumer schedulers: oneto-one, one-to-many, and many-to-many. These pipe schedulers dynamically stream task identifiers to worker processes, exchanging increased inter-process communication  \nThese authors contributed equally to this work.  \n* Correspondence to: [skontog@uoi.gr](skontog@uoi.gr)  \noverhead for enhanced runtime load balancing and flexible chunk-based task dispatching. The thread-based evaluation examines static, dynamic, guided, chunk-based, chunkstealing, adaptive chunk, and AIMD adaptive scheduling strategies. The AIMD scheduler employs an additiveincrease multiplicative-decrease policy inspired by TCP congestion control, utilizing an exponentially weighted moving average (EWMA) of CPU utilization to regulate a contention window that limits the number of concurrently active chunks. The adaptive chunk scheduler further modifies chunk size based on observed per-thread execution speed. Experimental results on a 24-core x86- 64 platform indicate that thread schedulers deliver the  \nhighest overall performance, with dynamic and guided scheduling yielding the most favorable practical outcomes. Among process schedulers, pipe-based designs demonstrate the strongest scalability, with one-to-one pipes excelling for smaller workloads and many-to-many pipes preferred for larger workloads. In summary, lightweight thread scheduling is optimal for shared-memory row sorting, while AIMD/adaptive scheduling and pipe-based process scheduling remain valuable for contention-aware execution, explicit inter-process coordination, and distributedstyle heterogeneous workload management.  \n1. Introduction  \nProcess-based scheduling is a central perspective for heavyweight tasks executed in a Single Instruction Multiple Process (SIM","cbCaisfavzpCJOpf","https://ap.wps.com/l/cbCaisfavzpCJOpf","pdf",2033206,1,21,"English","en",105,"# Introduction\n## Process-based scheduling\n## Thread scheduling\n## Scalability evaluation","[{\"question\":\"What workload and system model are used to evaluate scheduling performance?\",\"answer\":\"The evaluation uses a memory-intensive row-wise quick-sort workload on large three-dimensional tensors in many-core shared-memory systems.\"},{\"question\":\"Which thread scheduling strategies are compared in the study?\",\"answer\":\"The thread-based evaluation includes static, dynamic, guided, chunk-based, chunkstealing, adaptive chunk, and AIMD adaptive scheduling strategies.\"},{\"question\":\"How do process-based pipe schedulers differ, and what scalability trends are reported?\",\"answer\":\"Process pipe schedulers stream task identifiers using one-to-one, one-to-many, and many-to-many producer–consumer designs; one-to-one excels for smaller workloads, while many-to-many is preferred for larger workloads.\"}]",1784198105,53,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"performance-evaluation-of-scheduling-tasks-in-many-core-systems-using-processes-and-threads","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/performance-evaluation-of-scheduling-tasks-in-many-core-systems-using-processes-and-threads/84764/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What workload and system model are used to evaluate scheduling performance?","Question",{"text":75,"@type":76},"The evaluation uses a memory-intensive row-wise quick-sort workload on large three-dimensional tensors in many-core shared-memory systems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which thread scheduling strategies are compared in the study?",{"text":80,"@type":76},"The thread-based evaluation includes static, dynamic, guided, chunk-based, chunkstealing, adaptive chunk, and AIMD adaptive scheduling strategies.",{"name":82,"@type":73,"acceptedAnswer":83},"How do process-based pipe schedulers differ, and what scalability trends are reported?",{"text":84,"@type":76},"Process pipe schedulers stream task identifiers using one-to-one, one-to-many, and many-to-many producer–consumer designs; one-to-one excels for smaller workloads, while many-to-many is preferred for larger workloads.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":45,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":45,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]