Efficient and Effective Sparse Tensor Reordering

Conference · Wed Jun 26 00:00:00 EDT 2019

DOI:https://doi.org/10.1145/3330345.3330366· OSTI ID:1574893

Li, Jiajia ^[1]; Ucar, Bora ^[2]; Catalyurek, Umit ^[3]; Sun, Jimeng ^[3]; Barker, Kevin J. ^[1]; Vuduc, Richard ^[3]

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1. BATTELLE (PACIFIC NW LAB)
2. UMR5668, CNRS and ENS Lyon, France
3. Georgia Institute of Technology

This paper proposed two reordering schemes for sparse tensors: BFS-MCS and LEXI-ORDER. BFS-MCS is a Breadth First Search (BFS)-like heuristic approach based on the maximum cardinality search family; LEXI-ORDER is an extension of doubly lexical ordering of matrices to tensors. CANDECOMP/PARAFAC decomposition (CPD) is taken as an example to show their effect on existing three sparse tensor formats for CPUs: coordinate (COO), compressed sparse fiber (CSF), and hierarchical coordinate (HICOO). LEXI-ORDER obtains up to 4.14× speedup on sequential HICOO-MTTKRP and 11.88× speedup on its parallel case. COO- and CSF-MTTKRP also achieves performance improvement in different degree. Our two reordering methods are more efficient and effective than other state-of-the-art reordering methods used in SPLATT.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1574893
• Report Number(s): PNNL-SA-138751
• Resource Relation: Conference: Proceedings of the ACM International Conference on Supercomputing (ICS 2019), June 26-28, 2019, Phoenix, AZ
• Country of Publication: United States
• Language: English

Related Subjects

HPC