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DTSTAMP:20181221T160743Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181114T083000
DTEND;TZID=America/Chicago:20181114T170000
UID:submissions.supercomputing.org_SC18_sess342_drs122@linklings.com
SUMMARY:Linear Algebra Is the Right Way to Think About Graphs
DESCRIPTION:Doctoral Showcase\nWorkshop Reg Pass, Tutorial Reg Pass, Tech 
 Program Reg Pass, Exhibits Reg Pass, Exhibits - Exhibit Hall Only Reg Pass
 \n\nLinear Algebra Is the Right Way to Think About Graphs\n\nYang, Owens, 
 Buluc\n\nGraph algorithms are challenging to implement on new accelerators
  such as GPUs. To address this problem, GraphBLAS is an innovative on-goin
 g effort by the graph analytics community to formulate graph algorithms as
  sparse linear algebra, so that they can be expressed in a performant, suc
 cinct and in a backend-agnostic manner. Initial research efforts in implem
 enting GraphBLAS on GPUs for graph processing and analytics have been prom
 ising, but challenges such as feature-incompleteness and poor performance 
 still exist compared to their vertex-centric ("think like a vertex") graph
  framework counterparts. For our thesis, we propose a multi-language graph
  framework aiming to simplify the development of graph algorithms, which 1
 ) provides a multi-language GraphBLAS interface for the end-users to expre
 ss, develop, and refine graph algorithms more succinctly than existing dis
 tributed graph frameworks; 2) abstracts away from the end-users performanc
 e-tuning decisions; 3) utilizes the advantages of existing low-level GPU c
 omputing primitives to maintain high performance.
URL:https://sc18.supercomputing.org/presentation/?id=drs122&sess=sess342
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