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Title: PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite

Abstract

Tensor methods have gained increasingly attention from various applications, including machine learning, quantum chemistry, healthcare analytics, social network analysis, data mining, and signal processing, to name a few. Sparse tensors and their algorithms become critical to further improve the performance of these methods and enhance the interpretability of their output. This work presents a sparse tensor algorithm benchmark suite (PASTA) for single- and multi-core CPUs. To the best of our knowledge, this is the first benchmark suite for sparse tensor world. PASTA targets on: 1) helping application users to evaluate different computer systems using its representative computational workloads; 2) providing insights to better utilize existed computer architecture and systems and inspiration for the future design. This benchmark suite will be publicly released.

Authors:
 [1];  [2];  [3];  [1];  [1]
  1. BATTELLE (PACIFIC NW LAB)
  2. Hangzhou Dianzi University
  3. Virginia Tech
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1597826
Report Number(s):
PNNL-SA-140675
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
CCF Transactions on High Performance Computing
Additional Journal Information:
Journal Volume: 1; Journal Issue: 2
Country of Publication:
United States
Language:
English
Subject:
HPC, sparse tensor, benchmark

Citation Formats

Li, Jiajia, Ma, Yuchen, Wu, Xiaolong, Li, Ang, and Barker, Kevin J. PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite. United States: N. p., 2019. Web. doi:10.1007/s42514-019-00012-w.
Li, Jiajia, Ma, Yuchen, Wu, Xiaolong, Li, Ang, & Barker, Kevin J. PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite. United States. doi:10.1007/s42514-019-00012-w.
Li, Jiajia, Ma, Yuchen, Wu, Xiaolong, Li, Ang, and Barker, Kevin J. Mon . "PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite". United States. doi:10.1007/s42514-019-00012-w.
@article{osti_1597826,
title = {PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite},
author = {Li, Jiajia and Ma, Yuchen and Wu, Xiaolong and Li, Ang and Barker, Kevin J.},
abstractNote = {Tensor methods have gained increasingly attention from various applications, including machine learning, quantum chemistry, healthcare analytics, social network analysis, data mining, and signal processing, to name a few. Sparse tensors and their algorithms become critical to further improve the performance of these methods and enhance the interpretability of their output. This work presents a sparse tensor algorithm benchmark suite (PASTA) for single- and multi-core CPUs. To the best of our knowledge, this is the first benchmark suite for sparse tensor world. PASTA targets on: 1) helping application users to evaluate different computer systems using its representative computational workloads; 2) providing insights to better utilize existed computer architecture and systems and inspiration for the future design. This benchmark suite will be publicly released.},
doi = {10.1007/s42514-019-00012-w},
journal = {CCF Transactions on High Performance Computing},
number = 2,
volume = 1,
place = {United States},
year = {2019},
month = {8}
}

Works referenced in this record:

Scalable tensor factorizations for incomplete data
journal, March 2011

  • Acar, Evrim; Dunlavy, Daniel M.; Kolda, Tamara G.
  • Chemometrics and Intelligent Laboratory Systems, Vol. 106, Issue 1
  • DOI: 10.1016/j.chemolab.2010.08.004

Parallel Tensor Compression for Large-Scale Scientific Data
conference, May 2016

  • Austin, Woody; Ballard, Grey; Kolda, Tamara G.
  • 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
  • DOI: 10.1109/IPDPS.2016.67

Efficient MATLAB Computations with Sparse and Factored Tensors
journal, January 2008

  • Bader, Brett W.; Kolda, Tamara G.
  • SIAM Journal on Scientific Computing, Vol. 30, Issue 1
  • DOI: 10.1137/060676489

A Practical Randomized CP Tensor Decomposition
journal, January 2018

  • Battaglino, Casey; Ballard, Grey; Kolda, Tamara G.
  • SIAM Journal on Matrix Analysis and Applications, Vol. 39, Issue 2
  • DOI: 10.1137/17M1112303

F lexi F a CT: Scalable Flexible Factorization of Coupled Tensors on Hadoop
conference, April 2014

  • Beutel, Alex; Talukdar, Partha Pratim; Kumar, Abhimanu
  • Proceedings of the 2014 SIAM International Conference on Data Mining
  • DOI: 10.1137/1.9781611973440.13

The PARSEC benchmark suite: characterization and architectural implications
conference, January 2008

  • Bienia, Christian; Kumar, Sanjeev; Singh, Jaswinder Pal
  • Proceedings of the 17th international conference on Parallel architectures and compilation techniques - PACT '08
  • DOI: 10.1145/1454115.1454128

Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition
journal, September 1970

  • Carroll, J. Douglas; Chang, Jih-Jie
  • Psychometrika, Vol. 35, Issue 3
  • DOI: 10.1007/BF02310791

Rodinia: A benchmark suite for heterogeneous computing
conference, October 2009

  • Che, Shuai; Boyer, Michael; Meng, Jiayuan
  • 2009 IEEE International Symposium on Workload Characterization (IISWC)
  • DOI: 10.1109/IISWC.2009.5306797

Decompositions of a Higher-Order Tensor in Block Terms—Part I: Lemmas for Partitioned Matrices
journal, January 2008

  • De Lathauwer, Lieven
  • SIAM Journal on Matrix Analysis and Applications, Vol. 30, Issue 3
  • DOI: 10.1137/060661685

A Multilinear Singular Value Decomposition
journal, January 2000

  • De Lathauwer, Lieven; De Moor, Bart; Vandewalle, Joos
  • SIAM Journal on Matrix Analysis and Applications, Vol. 21, Issue 4
  • DOI: 10.1137/S0895479896305696

On the Best Rank-1 and Rank-( R 1 , R 2 ,. . ., R N ) Approximation of Higher-Order Tensors
journal, January 2000

  • De Lathauwer, Lieven; De Moor, Bart; Vandewalle, Joos
  • SIAM Journal on Matrix Analysis and Applications, Vol. 21, Issue 4
  • DOI: 10.1137/S0895479898346995

New implementation of high-level correlated methods using a general block tensor library for high-performance electronic structure calculations
journal, July 2013

  • Epifanovsky, Evgeny; Wormit, Michael; Kuś, Tomasz
  • Journal of Computational Chemistry, Vol. 34, Issue 26
  • DOI: 10.1002/jcc.23377

Algorithms for entanglement renormalization
journal, April 2009


Hierarchical Singular Value Decomposition of Tensors
journal, January 2010

  • Grasedyck, Lars
  • SIAM Journal on Matrix Analysis and Applications, Vol. 31, Issue 4
  • DOI: 10.1137/090764189

A literature survey of low-rank tensor approximation techniques
journal, August 2013

  • Grasedyck, Lars; Kressner, Daniel; Tobler, Christine
  • GAMM-Mitteilungen, Vol. 36, Issue 1
  • DOI: 10.1002/gamm.201310004

A New Scheme for the Tensor Representation
journal, October 2009


The Expression of a Tensor or a Polyadic as a Sum of Products
journal, April 1927


In-Datacenter Performance Analysis of a Tensor Processing Unit
conference, January 2017

  • Jouppi, Norman P.; Borchers, Al; Boyle, Rick
  • Proceedings of the 44th Annual International Symposium on Computer Architecture - ISCA '17
  • DOI: 10.1145/3079856.3080246

Scalable sparse tensor decompositions in distributed memory systems
conference, January 2015

  • Kaya, Oguz; Uçar, Bora
  • Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis on - SC '15
  • DOI: 10.1145/2807591.2807624

Scalable Tensor Decompositions for Multi-aspect Data Mining
conference, December 2008

  • Kolda, Tamara G.; Sun, Jimeng
  • 2008 Eighth IEEE International Conference on Data Mining (ICDM)
  • DOI: 10.1109/ICDM.2008.89

Clustered Low-Rank Tensor Format: Introduction and Application to Fast Construction of Hartree–Fock Exchange
journal, November 2016

  • Lewis, Cannada A.; Calvin, Justus A.; Valeev, Edward F.
  • Journal of Chemical Theory and Computation, Vol. 12, Issue 12
  • DOI: 10.1021/acs.jctc.6b00884

SMAT: an input adaptive auto-tuner for sparse matrix-vector multiplication
conference, January 2013

  • Li, Jiajia; Tan, Guangming; Chen, Mingyu
  • Proceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation - PLDI '13
  • DOI: 10.1145/2491956.2462181

A Unified Optimization Approach for Sparse Tensor Operations on GPUs
conference, September 2017

  • Liu, Bangtian; Wen, Chengyao; Sarwate, Anand D.
  • 2017 IEEE International Conference on Cluster Computing (CLUSTER)
  • DOI: 10.1109/CLUSTER.2017.75

Efficient tree tensor network states (TTNS) for quantum chemistry: Generalizations of the density matrix renormalization group algorithm
journal, April 2013

  • Nakatani, Naoki; Chan, Garnet Kin-Lic
  • The Journal of Chemical Physics, Vol. 138, Issue 13
  • DOI: 10.1063/1.4798639

Tensor-Train Decomposition
journal, January 2011

  • Oseledets, I. V.
  • SIAM Journal on Scientific Computing, Vol. 33, Issue 5
  • DOI: 10.1137/090752286

Massively Parallel Implementation of Explicitly Correlated Coupled-Cluster Singles and Doubles Using TiledArray Framework
journal, December 2016

  • Peng, Chong; Calvin, Justus A.; Pavošević, Fabijan
  • The Journal of Physical Chemistry A, Vol. 120, Issue 51
  • DOI: 10.1021/acs.jpca.6b10150

A Benchmark Characterization of the EEMBC Benchmark Suite
journal, September 2009

  • Poovey, Jason A.; Conte, Thomas M.; Levy, Markus
  • IEEE Micro, Vol. 29, Issue 5
  • DOI: 10.1109/MM.2009.74

Tensor Decomposition for Signal Processing and Machine Learning
journal, July 2017

  • Sidiropoulos, Nicholas D.; De Lathauwer, Lieven; Fu, Xiao
  • IEEE Transactions on Signal Processing, Vol. 65, Issue 13
  • DOI: 10.1109/TSP.2017.2690524

Atomic orbital-based SOS-MP2 with tensor hypercontraction. I. GPU-based tensor construction and exploiting sparsity
journal, May 2016

  • Song, Chenchen; Martínez, Todd J.
  • The Journal of Chemical Physics, Vol. 144, Issue 17
  • DOI: 10.1063/1.4948438

Optimization-Based Algorithms for Tensor Decompositions: Canonical Polyadic Decomposition, Decomposition in Rank-$(L_r,L_r,1)$ Terms, and a New Generalization
journal, January 2013

  • Sorber, Laurent; Van Barel, Marc; De Lathauwer, Lieven
  • SIAM Journal on Optimization, Vol. 23, Issue 2
  • DOI: 10.1137/120868323

A comparison of algorithms for fitting the PARAFAC model
journal, April 2006


Some mathematical notes on three-mode factor analysis
journal, September 1966


Multilayer formulation of the multiconfiguration time-dependent Hartree theory
journal, July 2003

  • Wang, Haobin; Thoss, Michael
  • The Journal of Chemical Physics, Vol. 119, Issue 3
  • DOI: 10.1063/1.1580111