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DTSTAMP:20181221T160728Z
LOCATION:D172
DTSTART;TZID=America/Chicago:20181112T162000
DTEND;TZID=America/Chicago:20181112T164500
UID:submissions.supercomputing.org_SC18_sess168_ws_ia107@linklings.com
SUMMARY:High-Performance GPU Implementation of PageRank with Reduced Preci
 sion Based on Mantissa Segmentation
DESCRIPTION:Workshop\nArchitectures, Data Analytics, Graph Algorithms, Wor
 kshop Reg Pass\n\nHigh-Performance GPU Implementation of PageRank with Red
 uced Precision Based on Mantissa Segmentation\n\nGrützmacher, Anzt, 
 Scheidegger, Quintana-Ortí\n\nWe address the acceleration of the PageRank 
 algorithm for web information retrieval on graphics processing units (GPUs
 ) via a modular precision framework that adapts the input data format in m
 emory to the numerical requirements as the iteration converges. In detail,
  we abandon the ieee 754 single- and double-precision number representatio
 n formats, employed in the standard implementation of PageRank, to instead
  store the data in memory in some specialized formats. Furthermore, we avo
 id the data duplication by leveraging a data layout based on mantissa segm
 entation. Our evaluation on a V100 graphics card from NVIDIA shows acceler
 ation factors of up to 30% with respect to the standard algorithm operatin
 g in double-precision.
URL:https://sc18.supercomputing.org/presentation/?id=ws_ia107&sess=sess168
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