{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/97444"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/97444","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Intelligent scheduling for simultaneous CPU-GPU applications","abstract":"Heterogeneous computing systems with both general purpose multicore central processing units (CPU) and specialized accelerators has emerged recently. Graphics processing unit (GPU) is the most widely used accelerator. To fully utilize such a heterogeneous system’s full computing power, coordination between the two distinct devices, CPU and GPU, is necessary. Previous research has addressed this issue of partitioning the workloads between CPU and GPU from various aspects for regular applications which have high parallelism and little data dependent control flows. However, it is still not clear how irregular applications, which behave differently on different inputs, could be efficiently scheduled on such heterogeneous computing systems. Since CPUs and GPUs have different characteristics, task chunks of these irregular applications show preference, or affinity, to a particular device in heterogeneous computing systems. In this work, we show that by using the method of allocating workloads at task chunk granularity based on each chunk’s device affinity, accompanied with work-stealing as the load balancing mechanism, we can achieve a performance improvement of as much as 1.5x over traditional ratio-based allocation, and up to 5x over naive GPU-only allocation on three irregular graph analytics applications.","abstract_html":"Heterogeneous computing systems with both general purpose multicore central processing units (CPU) and specialized accelerators has emerged recently. Graphics processing unit (GPU) is the most widely used accelerator. To fully utilize such a heterogeneous system’s full computing power, coordination between the two distinct devices, CPU and GPU, is necessary. Previous research has addressed this issue of partitioning the workloads between CPU and GPU from various aspects for regular applications which have high parallelism and little data dependent control flows. However, it is still not clear how irregular applications, which behave differently on different inputs, could be efficiently scheduled on such heterogeneous computing systems. Since CPUs and GPUs have different characteristics, task chunks of these irregular applications show preference, or affinity, to a particular device in heterogeneous computing systems. In this work, we show that by using the method of allocating workloads at task chunk granularity based on each chunk’s device affinity, accompanied with work-stealing as the load balancing mechanism, we can achieve a performance improvement of as much as 1.5x over traditional ratio-based allocation, and up to 5x over naive GPU-only allocation on three irregular graph analytics applications.","abstract_has_math":false,"creators":["Cheng, Lin"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Adve, Sarita V."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-10T19:15:57Z","date_published":"2017-08-10T19:15:57Z","updated_at":"2026-07-22T22:24:34Z","subjects":["Heterogeneous computing","Workload partitioning","Device affinity"],"languages":["en"],"rights":["Copyright 2017 Lin Cheng"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/97444","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Adve, Sarita V."]},{"key":"dc:creator","label":"Author","values":["Cheng, Lin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-08-10T19:15:57Z","2017-04-24","2017-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Heterogeneous computing","Workload partitioning","Device affinity"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Lin Cheng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/97444"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Heterogeneous computing systems with both general purpose multicore central processing units (CPU) and specialized accelerators has emerged recently. Graphics processing unit (GPU) is the most widely used accelerator. To fully utilize such a heterogeneous system’s full computing power, coordination between the two distinct devices, CPU and GPU, is necessary. Previous research has addressed this issue of partitioning the workloads between CPU and GPU from various aspects for regular applications which have high parallelism and little data dependent control flows. However, it is still not clear how irregular applications, which behave differently on different inputs, could be efficiently scheduled on such heterogeneous computing systems. Since CPUs and GPUs have different characteristics, task chunks of these irregular applications show preference, or affinity, to a particular device in heterogeneous computing systems. In this work, we show that by using the method of allocating workloads at task chunk granularity based on each chunk’s device affinity, accompanied with work-stealing as the load balancing mechanism, we can achieve a performance improvement of as much as 1.5x over traditional ratio-based allocation, and up to 5x over naive GPU-only allocation on three irregular graph analytics applications.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Lin Cheng, accepted the attached license on 2017-04-21 at 19:20.","The student, Lin Cheng, submitted this Thesis for approval on 2017-04-21 at 19:26.","This Thesis was approved for publication on 2017-04-24 at 15:45.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10997 on 2017-08-10 at 13:45:32","Made available in DSpace on 2017-08-10T19:15:57Z (GMT). No. of bitstreams: 2 CHENG-THESIS-2017.pdf: 1539230 bytes, checksum: 075ace5f8c13e356e5abaeba348ba78d (MD5) LICENSE.txt: 4206 bytes, checksum: 0c11b0666963c70ccb6681769e6fe4e2 (MD5) Previous issue date: 2017-04-24"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Intelligent scheduling for simultaneous CPU-GPU applications"]}]}],"canonical_facts":{"dc:contributor":["Adve, Sarita V."],"dc:creator":["Cheng, Lin"],"dc:date":["2017-08-10T19:15:57Z","2017-04-24","2017-05"],"dc:description":["Heterogeneous computing systems with both general purpose multicore central processing units (CPU) and specialized accelerators has emerged recently. Graphics processing unit (GPU) is the most widely used accelerator. To fully utilize such a heterogeneous system’s full computing power, coordination between the two distinct devices, CPU and GPU, is necessary. Previous research has addressed this issue of partitioning the workloads between CPU and GPU from various aspects for regular applications which have high parallelism and little data dependent control flows. However, it is still not clear how irregular applications, which behave differently on different inputs, could be efficiently scheduled on such heterogeneous computing systems. Since CPUs and GPUs have different characteristics, task chunks of these irregular applications show preference, or affinity, to a particular device in heterogeneous computing systems. In this work, we show that by using the method of allocating workloads at task chunk granularity based on each chunk’s device affinity, accompanied with work-stealing as the load balancing mechanism, we can achieve a performance improvement of as much as 1.5x over traditional ratio-based allocation, and up to 5x over naive GPU-only allocation on three irregular graph analytics applications.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Lin Cheng, accepted the attached license on 2017-04-21 at 19:20.","The student, Lin Cheng, submitted this Thesis for approval on 2017-04-21 at 19:26.","This Thesis was approved for publication on 2017-04-24 at 15:45.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10997 on 2017-08-10 at 13:45:32","Made available in DSpace on 2017-08-10T19:15:57Z (GMT). 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