_:n9ab092968d2f4255936e662e900db988xb15
<http://schema.org/description>
"Modern ARM-based servers such as ThunderX and ThunderX2 offer a tremendous\namount of parallelism by providing dozens or even hundreds of processors.\nHowever, exploiting these computing resources for reuse-heavy, data dependent\nworkloads is a big challenge because of shared cache resources. In particular,\nschedulers have to conservatively co-locate processes to avoid cache conflicts\nsince miss penalties are detrimental and conservative co-location decisions\nlead to lower resource utilization.\n  To address these challenges, in this paper we explore the utility of\npredictive analysis of applications' execution to dynamically forecast\nresource-heavy workload regions, and to improve the efficiency of resource\nmanagement through the use of new proactive methods. Our approach relies on the\ncompiler to insert \"beacons\" in the application at strategic program points to\nperiodically produce and/or update the attributes of anticipated\nresource-intense program region(s). The compiler classifies loops in programs\nbased on predictability of their execution time and inserts different types of\nbeacons at their entry/exit points. The precision of the information carried by\nbeacons varies as per the analyzability of the loops, and the scheduler uses\nperformance counters to fine tune co-location decisions. The information\nproduced by beacons in multiple processes is aggregated and analyzed by the\nproactive scheduler to respond to the anticipated workload requirements. For\nthroughput environments, we develop a framework that demonstrates high-quality\npredictions and improvements in throughput over CFS by 1.4x on an average and\nup to 4.7x on ThunderX and 1.9x on an average and up to 5.2x on ThunderX2\nservers on consolidated workloads across 45 benchmarks."
<https://deepai.org/profile/girish-mururu>