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Matrix Factorizations at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies:

  • Author(s): Gittens, Alex;
  • Devarakonda, Aditya;
  • Racah, Evan;
  • Ringenburg, Michael;
  • Gerhardt, Lisa;
  • Kottaalam, Jey;
  • Liu, Jialin;
  • Maschhoff, Kristyn;
  • Canon, Shane;
  • Chhugani, Jatin;
  • Sharma, Pramod;
  • Yang, Jiyan;
  • Demmel, James;
  • Harrell, Jim;
  • Krishnamurthy, Venkat;
  • Mahoney, Michael W.;
  • Prabhat, Mr
  • et al.
Abstract

We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms. Spark is designed for data analytics on cluster computing platforms with access to local disks and is optimized for data-parallel tasks. We examine three widely-used and important matrix factorizations: NMF (for physical plausibility), PCA (for its ubiquity) and CX (for data interpretability). We apply these methods to TB-sized problems in particle physics, climate modeling and bioimaging. The data matrices are tall-and-skinny which enable the algorithms to map conveniently into Spark's data-parallel model. We perform scaling experiments on up to 1600 Cray XC40 nodes, describe the sources of slowdowns, and provide tuning guidance to obtain high performance.

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