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DTSTAMP:20181221T160729Z
LOCATION:C141/143/149
DTSTART;TZID=America/Chicago:20181113T153000
DTEND;TZID=America/Chicago:20181113T160000
UID:submissions.supercomputing.org_SC18_sess212_pap511@linklings.com
SUMMARY:HiCOO: Hierarchical Storage of Sparse Tensors
DESCRIPTION:Paper\nAlgorithms, Graph Algorithms, Linear Algebra, Machine L
 earning, Sparse Computation, Tech Program Reg Pass, BSP Finalist\n\nHiCOO:
  Hierarchical Storage of Sparse Tensors\n\nLi, Sun, Vuduc\n\nThis paper pr
 oposes a new storage format for sparse tensors, called Hierarchical COOrdi
 nate (HiCOO; pronounced: “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 tensor blo
 cks, with the goals of preserving the “mode-agnostic” simplicity of COO wh
 ile reducing the bytes needed to represent the tensor and promoting data l
 ocality. We evaluate HiCOO by implementing a single-node, multicore-parall
 el version of the matricized tensor-times-Khatri-Rao product (MTTKRP) oper
 ation, which is the most expensive computational core in the widely used C
 ANDECOMP/PARAFAC decomposition(CPD) algorithm. This MTTKRP implementation 
 achieves up to 23.0× (6.8× on average) speedup over COO format and up to 1
 5.6× (3.1× on average) speedup over another state-of-the-art format, compr
 essed sparse fiber (CSF), by using less or comparable storage of them. Whe
 n used within CPD, we also observe speedups against COO- and CSF-based imp
 lementations.
URL:https://sc18.supercomputing.org/presentation/?id=pap511&sess=sess212
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