Efficient Large-Scale Graph Processing on Hybrid CPU and GPU Systems
The increasing scale and wealth of inter-connected data, such as those accrued by social network applications, demand the design of new techniques and platforms to efficiently derive actionable knowledge from large-scale graphs. However, real-world graphs are famously difficult to process efficientl...
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creator | Gharaibeh, Abdullah Reza, Tahsin Santos-Neto, Elizeu Costa, Lauro Beltrao Sallinen, Scott Ripeanu, Matei |
description | The increasing scale and wealth of inter-connected data, such as those
accrued by social network applications, demand the design of new techniques and
platforms to efficiently derive actionable knowledge from large-scale graphs.
However, real-world graphs are famously difficult to process efficiently. Not
only they have a large memory footprint, but also most graph algorithms entail
memory access patterns with poor locality, data-dependent parallelism and a low
compute-to-memory access ratio. Moreover, most real-world graphs have a highly
heterogeneous node degree distribution, hence partitioning these graphs for
parallel processing and simultaneously achieving access locality and
load-balancing is difficult.
This work starts from the hypothesis that hybrid platforms (e.g.,
GPU-accelerated systems) have both the potential to cope with the heterogeneous
structure of real graphs and to offer a cost-effective platform for
high-performance graph processing. This work assesses this hypothesis and
presents an extensive exploration of the opportunity to harness hybrid systems
to process large-scale graphs efficiently. In particular, (i) we present a
performance model that estimates the achievable performance on hybrid
platforms; (ii) informed by the performance model, we design and develop TOTEM
- a processing engine that provides a convenient environment to implement graph
algorithms on hybrid platforms; (iii) we show that further performance gains
can be extracted using partitioning strategies that aim to produce partitions
that each matches the strengths of the processing element it is allocated to,
finally, (iv) we demonstrate the performance advantages of the hybrid system
through a comprehensive evaluation that uses real and synthetic workloads (as
large as 16 billion edges), multiple graph algorithms that stress the system in
various ways, and a variety of hardware configurations. |
doi_str_mv | 10.48550/arxiv.1312.3018 |
format | Article |
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accrued by social network applications, demand the design of new techniques and
platforms to efficiently derive actionable knowledge from large-scale graphs.
However, real-world graphs are famously difficult to process efficiently. Not
only they have a large memory footprint, but also most graph algorithms entail
memory access patterns with poor locality, data-dependent parallelism and a low
compute-to-memory access ratio. Moreover, most real-world graphs have a highly
heterogeneous node degree distribution, hence partitioning these graphs for
parallel processing and simultaneously achieving access locality and
load-balancing is difficult.
This work starts from the hypothesis that hybrid platforms (e.g.,
GPU-accelerated systems) have both the potential to cope with the heterogeneous
structure of real graphs and to offer a cost-effective platform for
high-performance graph processing. This work assesses this hypothesis and
presents an extensive exploration of the opportunity to harness hybrid systems
to process large-scale graphs efficiently. In particular, (i) we present a
performance model that estimates the achievable performance on hybrid
platforms; (ii) informed by the performance model, we design and develop TOTEM
- a processing engine that provides a convenient environment to implement graph
algorithms on hybrid platforms; (iii) we show that further performance gains
can be extracted using partitioning strategies that aim to produce partitions
that each matches the strengths of the processing element it is allocated to,
finally, (iv) we demonstrate the performance advantages of the hybrid system
through a comprehensive evaluation that uses real and synthetic workloads (as
large as 16 billion edges), multiple graph algorithms that stress the system in
various ways, and a variety of hardware configurations.</description><identifier>DOI: 10.48550/arxiv.1312.3018</identifier><language>eng</language><subject>Computer Science - Distributed, Parallel, and Cluster Computing</subject><creationdate>2013-12</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1312.3018$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1312.3018$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Gharaibeh, Abdullah</creatorcontrib><creatorcontrib>Reza, Tahsin</creatorcontrib><creatorcontrib>Santos-Neto, Elizeu</creatorcontrib><creatorcontrib>Costa, Lauro Beltrao</creatorcontrib><creatorcontrib>Sallinen, Scott</creatorcontrib><creatorcontrib>Ripeanu, Matei</creatorcontrib><title>Efficient Large-Scale Graph Processing on Hybrid CPU and GPU Systems</title><description>The increasing scale and wealth of inter-connected data, such as those
accrued by social network applications, demand the design of new techniques and
platforms to efficiently derive actionable knowledge from large-scale graphs.
However, real-world graphs are famously difficult to process efficiently. Not
only they have a large memory footprint, but also most graph algorithms entail
memory access patterns with poor locality, data-dependent parallelism and a low
compute-to-memory access ratio. Moreover, most real-world graphs have a highly
heterogeneous node degree distribution, hence partitioning these graphs for
parallel processing and simultaneously achieving access locality and
load-balancing is difficult.
This work starts from the hypothesis that hybrid platforms (e.g.,
GPU-accelerated systems) have both the potential to cope with the heterogeneous
structure of real graphs and to offer a cost-effective platform for
high-performance graph processing. This work assesses this hypothesis and
presents an extensive exploration of the opportunity to harness hybrid systems
to process large-scale graphs efficiently. In particular, (i) we present a
performance model that estimates the achievable performance on hybrid
platforms; (ii) informed by the performance model, we design and develop TOTEM
- a processing engine that provides a convenient environment to implement graph
algorithms on hybrid platforms; (iii) we show that further performance gains
can be extracted using partitioning strategies that aim to produce partitions
that each matches the strengths of the processing element it is allocated to,
finally, (iv) we demonstrate the performance advantages of the hybrid system
through a comprehensive evaluation that uses real and synthetic workloads (as
large as 16 billion edges), multiple graph algorithms that stress the system in
various ways, and a variety of hardware configurations.</description><subject>Computer Science - Distributed, Parallel, and Cluster Computing</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz8tqwkAUgOHZuCjafVdlXiBxrslkWaKNhYCCdh3OXI4d0CgzUpq3b227-nc_fIQ8cVYqozVbQvqKnyWXXJSScfNAVmvE6GIYb7SHdAzF3sEp0C7B9YPu0sWFnON4pJeRbiaboqft7p3C6Gn30_2Ub-GcF2SGcMrh8b9zcnhdH9pN0W-7t_alL6DSpgAP3CmEwAUTzjPOGqmR1aAqVMxhbcHWRktpuEatfC1AeK4roWzTeLRyTp7_tr-K4ZriGdI03DXDXSO_AQC2Q2U</recordid><startdate>20131210</startdate><enddate>20131210</enddate><creator>Gharaibeh, Abdullah</creator><creator>Reza, Tahsin</creator><creator>Santos-Neto, Elizeu</creator><creator>Costa, Lauro Beltrao</creator><creator>Sallinen, Scott</creator><creator>Ripeanu, Matei</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20131210</creationdate><title>Efficient Large-Scale Graph Processing on Hybrid CPU and GPU Systems</title><author>Gharaibeh, Abdullah ; Reza, Tahsin ; Santos-Neto, Elizeu ; Costa, Lauro Beltrao ; Sallinen, Scott ; Ripeanu, Matei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a658-ada1c4fae1202cd010935f07a46f40cf7bab78533815f54d72a2d15624b99dfb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Computer Science - Distributed, Parallel, and Cluster Computing</topic><toplevel>online_resources</toplevel><creatorcontrib>Gharaibeh, Abdullah</creatorcontrib><creatorcontrib>Reza, Tahsin</creatorcontrib><creatorcontrib>Santos-Neto, Elizeu</creatorcontrib><creatorcontrib>Costa, Lauro Beltrao</creatorcontrib><creatorcontrib>Sallinen, Scott</creatorcontrib><creatorcontrib>Ripeanu, Matei</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Gharaibeh, Abdullah</au><au>Reza, Tahsin</au><au>Santos-Neto, Elizeu</au><au>Costa, Lauro Beltrao</au><au>Sallinen, Scott</au><au>Ripeanu, Matei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Efficient Large-Scale Graph Processing on Hybrid CPU and GPU Systems</atitle><date>2013-12-10</date><risdate>2013</risdate><abstract>The increasing scale and wealth of inter-connected data, such as those
accrued by social network applications, demand the design of new techniques and
platforms to efficiently derive actionable knowledge from large-scale graphs.
However, real-world graphs are famously difficult to process efficiently. Not
only they have a large memory footprint, but also most graph algorithms entail
memory access patterns with poor locality, data-dependent parallelism and a low
compute-to-memory access ratio. Moreover, most real-world graphs have a highly
heterogeneous node degree distribution, hence partitioning these graphs for
parallel processing and simultaneously achieving access locality and
load-balancing is difficult.
This work starts from the hypothesis that hybrid platforms (e.g.,
GPU-accelerated systems) have both the potential to cope with the heterogeneous
structure of real graphs and to offer a cost-effective platform for
high-performance graph processing. This work assesses this hypothesis and
presents an extensive exploration of the opportunity to harness hybrid systems
to process large-scale graphs efficiently. In particular, (i) we present a
performance model that estimates the achievable performance on hybrid
platforms; (ii) informed by the performance model, we design and develop TOTEM
- a processing engine that provides a convenient environment to implement graph
algorithms on hybrid platforms; (iii) we show that further performance gains
can be extracted using partitioning strategies that aim to produce partitions
that each matches the strengths of the processing element it is allocated to,
finally, (iv) we demonstrate the performance advantages of the hybrid system
through a comprehensive evaluation that uses real and synthetic workloads (as
large as 16 billion edges), multiple graph algorithms that stress the system in
various ways, and a variety of hardware configurations.</abstract><doi>10.48550/arxiv.1312.3018</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Distributed, Parallel, and Cluster Computing |
title | Efficient Large-Scale Graph Processing on Hybrid CPU and GPU Systems |
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