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DTSTAMP:20181221T160910Z
LOCATION:C141/143/149
DTSTART;TZID=America/Chicago:20181113T153000
DTEND;TZID=America/Chicago:20181113T170000
UID:submissions.supercomputing.org_SC18_sess212@linklings.com
SUMMARY:Algorithms on Sparse Data
DESCRIPTION:Paper\nAlgorithms, Graph Algorithms, Linear Algebra, Machine L
 earning, Sparse Computation, Tech Program Reg Pass\n\nHiCOO: Hierarchical 
 Storage of Sparse Tensors\n\nLi, Sun, Vuduc\n\nThis paper proposes a new s
 torage format for sparse tensors, called Hierarchical COOrdinate (HiCOO; p
 ronounced: “haiku”). It derives from coordinate (COO) format, arguably the
  de facto standard for general sparse tensor storage. HiCOO improves upon 
 COO by compressing the indices in units of sparse t...\n\n----------------
 -----\nPruneJuice:  Pruning Trillion-Edge Graphs to a Precise Pattern-Matc
 hing Solution\n\nReza, Ripeanu, Tripoul, Sanders, Pearce\n\nPattern matchi
 ng is a powerful graph analysis tool. Unfortunately, existing solutions ha
 ve limited scalability, support only a limited set of search patterns, and
 /or focus on only a subset of the real-world problems associated with patt
 ern matching. This paper presents a new algorithmic pipeline tha...\n\n---
 ------------------\nDistributed Memory Sparse Inverse Covariance Matrix Es
 timation on High-Performance Computing Architectures\n\nEftekhari, Bollhöf
 er, Schenk\n\nWe consider the problem of estimating sparse inverse covaria
 nce matrices for high-dimensional datasets using the l1-regularized Gaussi
 an maximum likelihood method. This task is particularly challenging as the
  required computational resources increase superlinearly with the dimensio
 nality of the data...\n
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