Sparse Tensor Factorization on Many-Core Processors with High-Bandwidth Memory

Shaden Smith, Jongsoo Park, George Karypis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

HPC systems are increasingly used for data intensive computations which exhibit irregular memory accesses, non-uniform work distributions, large memory footprints, and high memory bandwidth demands. To address these challenging demands, HPC systems are turning to many-core architectures that feature a large number of energy-efficient cores backed by high-bandwidth memory. These features are exemplified in Intel's recent Knights Landing many-core processor (KNL), which typically has 68 cores and 16GB of on-package multi-channel DRAM (MCDRAM). This work investigates how the novel architectural features offered by KNL can be used in the context of decomposing sparse, unstructured tensors using the canonical polyadic decomposition (CPD). The CPD is used extensively to analyze large multi-way datasets arising in various areas including precision healthcare, cybersecurity, and e-commerce. Towards this end, we (i) develop problem decompositions for the CPD which are amenable to hundreds of concurrent threads while maintaining load balance and low synchronization costs; and (ii) explore the utilization of architectural features such as MCDRAM. Using one KNL processor, our algorithm achieves up to 1.8x speedup over a dual socket Intel Xeon system with 44 cores.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium, IPDPS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1058-1067
Number of pages10
ISBN (Electronic)9781538639146
DOIs
StatePublished - Jun 30 2017
Event31st IEEE International Parallel and Distributed Processing Symposium, IPDPS 2017 - Orlando, United States
Duration: May 29 2017Jun 2 2017

Publication series

NameProceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium, IPDPS 2017

Other

Other31st IEEE International Parallel and Distributed Processing Symposium, IPDPS 2017
CountryUnited States
CityOrlando
Period5/29/176/2/17

Fingerprint Dive into the research topics of 'Sparse Tensor Factorization on Many-Core Processors with High-Bandwidth Memory'. Together they form a unique fingerprint.

  • Cite this

    Smith, S., Park, J., & Karypis, G. (2017). Sparse Tensor Factorization on Many-Core Processors with High-Bandwidth Memory. In Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium, IPDPS 2017 (pp. 1058-1067). [7967196] (Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium, IPDPS 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IPDPS.2017.84