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DTSTAMP:20181221T160729Z
LOCATION:C146
DTSTART;TZID=America/Chicago:20181113T140000
DTEND;TZID=America/Chicago:20181113T143000
UID:submissions.supercomputing.org_SC18_sess178_pap140@linklings.com
SUMMARY:TriCore: Parallel Triangle Counting on GPUs
DESCRIPTION:Paper\nAlgorithms, Architectures, Data Analytics, Deep Learnin
 g, Networks, Scientific Computing, Visualization, Tech Program Reg Pass\n\
 nTriCore: Parallel Triangle Counting on GPUs\n\nHu, Liu, Huang\n\nTriangle
  counting algorithm enumerates the triangles in a graph by identifying the
  common neighbors between two vertices of every edge. In this work, we pre
 sent TriCore, a new GPU-based high-performance and scalable triangle count
 ing system that consists of three main techniques. First, we design a bina
 ry search based counting algorithm that tremendously increases both thread
  parallelism and memory performance. Second, TriCore exploits a 2-D partit
 ion method to distribute the CSR representation across multiple GPUs, comb
 ined with a new streaming buffer to load the edge list from outside of GPU
 s. Third, we develop a dynamic workload management technique to balance th
 e workload across multiple GPUs. Our evaluation demonstrates TriCore is 22
 × faster than the state-of-the-art parallel triangle counting projects. In
  addition, TriCore can not only process big graphs that are significant la
 rger than the memory size of one GPU but also achieve 24× speedup when sca
 ling to 32 GPUs.
URL:https://sc18.supercomputing.org/presentation/?id=pap140&sess=sess178
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