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DTSTART:19700308T020000
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DTSTAMP:20230124T171523Z
LOCATION:C144-145
DTSTART;TZID=America/Chicago:20221118T113000
DTEND;TZID=America/Chicago:20221118T115000
UID:submissions.supercomputing.org_SC22_sess445_ws_ia101@linklings.com
SUMMARY:Compressed In-Memory Graphs for Accelerating GPU-Based Analytics
DESCRIPTION:Workshop\n\nCompressed In-Memory Graphs for Accelerating GPU-B
 ased Analytics\n\nAzami, Burtscher\n\nProcessing large graphs has become a
 n important irregular workload. We present Massively Parallel Log Graphs (
 MPLG) to accelerate GPU graph codes, including highly optimized codes. MPL
 G combines a compressed in-memory representation with low-overhead paralle
 l decompression. This yields a speedup if the boost in memory performance 
 due to the reduced footprint outweighs the overhead of the extra instructi
 ons to decompress the graph on the fly. However, achieving a sufficiently 
 low overhead is difficult, especially on GPUs with their high-bandwidth me
 mory. Prior work has only successfully employed similar ideas on CPUs, but
  those approaches exhibit limited parallelism, making them unsuitable for 
 GPUs. On large real-world inputs, MPLG speeds up graph analytics by up to 
 67% on a Titan V GPU. Averaged over 15 graphs from several domains, it imp
 roves the performance of Rodinia’s breadth-first search by 11.9%, Gardenia
 ’s connected components by 5.8%, and ECL’s graph coloring by 5.0%.\n\nSess
 ion Format: Recorded\n\nTag: Accelerator-based Architectures, Algorithms, 
 Architectures, Big Data, Data Analytics, Parallel Programming Languages an
 d Models, Productivity Tools\n\nRegistration Category: Workshop Reg Pass
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