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DTSTART;TZID=America/Chicago:20181113T143000
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UID:submissions.supercomputing.org_SC18_sess178_pap141@linklings.com
SUMMARY:Distributed-Memory Hierarchical Compression of Dense SPD Matrices
DESCRIPTION:Chenhan D. Yu (University of Texas), Severin Reiz (Technical U
 niversity Munich), and George Biros (University of Texas)\n\nWe present a 
 distributed-memory algorithm for the hierarchical compression of SPD matri
 ces. Our method is based on GOFMM, an algorithm that appeared in doi:10.11
 45/3126908.3126921.\n\nFor many SPD matrices, GOFMM enables compression an
 d approximate matrix-vector multiplication in NlogN time---as opposed to q
 uadratic work required for a dense matrix. But GOFMM supports only shared 
 memory parallelism. In this paper, we use the message passing interface, e
 xtending the ideas of GOFMM to the distributed memory setting. We also int
 roduce an asynchronous algorithm for faster multiplication. We present dif
 ferent usage scenarios of SPD matrices that are related to graphs, neural-
 networks, and covariance operators. We also compare with STRUMPACK, which,
  to our knowledge, is the only other parallel software that can compress a
 rbitrary SPD matrices. In our largest run, we were able to compress a 67M-
 by-67M matrix within three minutes and perform a multiplication with 512 v
 ectors within 5 seconds on 6,144 Intel Skylake cores.\n\nTag: Algorithms, 
 Architectures, Data Analytics, Deep Learning, Networks, Scientific Computi
 ng, Visualization\n\nRegistration Category: Tech Program Reg Pass\n\nFinal
 ist: BSP Finalist\n\nSession Chair: Tom Peterka (Argonne National Laborato
 ry (ANL))\n\n
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