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Existing workload management systems often promote non-sharing of resources among different co-executing applications to achieve performance isolation. Such schemes lead to poor resource utilization and suboptimal process throughput, adversely affecting user productivity. Tackling this problem in a scalable fashion is extremely challenging, since it requires the workload scheduler to possess an in-depth knowledge about various application resource requirements and runtime phases at fine granularities within individual applications.<\/jats:p>\n          <jats:p>\n            In this work, we show that\n            <jats:italic>applications\u2019 resource requirements and execution phase behaviour can be captured<\/jats:italic>\n            in a scalable and lightweight manner at runtime by estimating important program artifacts termed as \u201c\n            <jats:bold>dynamic loop characteristics<\/jats:bold>\n            \u201d. Specifically,\n            <jats:italic>we propose a solution to the problem of efficient workload scheduling by designing a compiler and runtime cooperative framework that leverages novel loop-based compiler analysis for resource allocation<\/jats:italic>\n            .\n          <\/jats:p>\n          <jats:p>\n            We present\n            <jats:bold>Beacons Framework<\/jats:bold>\n            , an end-to-end compiler and scheduling framework, that\n            <jats:italic>estimates<\/jats:italic>\n            dynamic loop characteristics,\n            <jats:italic>encapsulates<\/jats:italic>\n            them in compiler-instrumented\n            <jats:bold>beacons<\/jats:bold>\n            in an application, and\n            <jats:italic>broadcasts<\/jats:italic>\n            them during application runtime, for proactive workload scheduling.\n            <jats:italic>We focus on estimating four important loop characteristics<\/jats:italic>\n            :\n            <jats:bold>loop trip-count<\/jats:bold>\n            ,\n            <jats:bold>loop timing<\/jats:bold>\n            ,\n            <jats:bold>loop memory footprint<\/jats:bold>\n            , and\n            <jats:bold>loop data-reuse behaviour<\/jats:bold>\n            , through a combination of compiler analysis and machine learning.\n          <\/jats:p>\n          <jats:p>\n            The novelty of the Beacons Framework also lies in its ability to tackle\n            <jats:italic>irregular loops that exhibit complex control flow with indeterminate loop bounds involving structure fields, aliased variables and function calls<\/jats:italic>\n            , which are highly prevalent in modern workloads. At the backend, Beacons Framework entails a\n            <jats:italic>proactive workload scheduler that leverages the runtime information to orchestrate aggressive process co-locations, for maximizing resource concurrency, without causing cache thrashing<\/jats:italic>\n            . Our results show that Beacons Framework can predict different loop characteristics with an accuracy of\n            <jats:bold>85%<\/jats:bold>\n            to\n            <jats:bold>95%<\/jats:bold>\n            on average, and the proactive scheduler obtains an average throughput improvement of\n            <jats:bold>1.9x<\/jats:bold>\n            (up to\n            <jats:bold>3.2x<\/jats:bold>\n            ) over the state-of-the-art schedulers on an Amazon Graviton2 machine on consolidated workloads involving 1000-10000 co-executing processes, across 51 benchmarks.\n          <\/jats:p>","DOI":"10.1145\/3622803","type":"journal-article","created":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T15:41:29Z","timestamp":1697470889000},"page":"173-203","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Beacons: An End-to-End Compiler Framework for Predicting and Utilizing Dynamic Loop Characteristics"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0795-1434","authenticated-orcid":false,"given":"Girish","family":"Mururu","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4563-4619","authenticated-orcid":false,"given":"Sharjeel","family":"Khan","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3098-6256","authenticated-orcid":false,"given":"Bodhisatwa","family":"Chatterjee","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0028-3124","authenticated-orcid":false,"given":"Chao","family":"Chen","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2821-2668","authenticated-orcid":false,"given":"Chris","family":"Porter","sequence":"additional","affiliation":[{"name":"IBM Research, Yorktown Heights, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4199-2512","authenticated-orcid":false,"given":"Ada","family":"Gavrilovska","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6723-8062","authenticated-orcid":false,"given":"Santosh","family":"Pande","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,10,16]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CLUSTER.2015.71"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.parco.2016.05.009"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPPW.2014.15"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/360018.360025"},{"volume-title":"Natural language processing with Python: analyzing text with the natural language toolkit. \" O\u2019Reilly Media","author":"Bird Steven","key":"e_1_2_2_5_1","unstructured":"Steven Bird , Ewan Klein , and Edward Loper . 2009. 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