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Compiling machine learning algorithms with SystemML

Published: 01 October 2013 Publication History

Abstract

Analytics on big data range from passenger volume prediction in transportation to customer satisfaction in automotive diagnostic systems, and from correlation analysis in social media data to log analysis in manufacturing. Expressing and running these analytics for varying data characteristics and at scale is challenging. To address these challenges, SystemML implements a declarative, high-level language using an R-like syntax extended with machine-learning-specific constructs, that is compiled to a MapReduce runtime [2]. The language is rich enough to express a wide class of statistical, predictive modeling and machine learning algorithms (Fig. 1). We chose robust algorithms that scale to large, and potentially sparse data with many features.

References

[1]
M. Boehm, S. Tatikonda, B. Reinwald, P. Sen, Y. Tian, D. Burdick, and S. Vaithyanathan. Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML. in submission, 2013.
[2]
A. Ghoting, R. Krishnamurthy, E. Pednault, B. Reinwald, V. Sindhwani, S. Tatikonda, Y. Tian, and S. Vaithyanathan. SystemML: Declarative Machine Learning on MapReduce. In ICDE, pages 231--242, 2011.
[3]
Y. Tian, S. Tatikonda, and B. Reinwald. Scalable and Numerically Stable Descriptive Statistics in SystemML. In ICDE, pages 1351--1359, 2012.

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  • (2021)Aprendizaje profundo para escalas pronósticas en la prescripción a pacientes con riesgo de sangrado gastrointestinalIngeniería y Ciencia10.17230/ingciencia.17.34.117:34(7-22)Online publication date: 1-Dec-2021
  • (2017)From BigBench to TPCx-BB: Standardization of a Big Data BenchmarkPerformance Evaluation and Benchmarking. Traditional - Big Data - Interest of Things10.1007/978-3-319-54334-5_3(24-44)Online publication date: 18-Feb-2017
  • (2016)Measuring and optimizing distributed array programsProceedings of the VLDB Endowment10.14778/2994509.29945119:12(912-923)Online publication date: 1-Aug-2016

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cover image ACM Conferences
SOCC '13: Proceedings of the 4th annual Symposium on Cloud Computing
October 2013
427 pages
ISBN:9781450324281
DOI:10.1145/2523616
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 October 2013

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SOCC '13
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SOCC '13: ACM Symposium on Cloud Computing
October 1 - 3, 2013
California, Santa Clara

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SOCC '13 Paper Acceptance Rate 23 of 114 submissions, 20%;
Overall Acceptance Rate 169 of 722 submissions, 23%

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Cited By

View all
  • (2021)Aprendizaje profundo para escalas pronósticas en la prescripción a pacientes con riesgo de sangrado gastrointestinalIngeniería y Ciencia10.17230/ingciencia.17.34.117:34(7-22)Online publication date: 1-Dec-2021
  • (2017)From BigBench to TPCx-BB: Standardization of a Big Data BenchmarkPerformance Evaluation and Benchmarking. Traditional - Big Data - Interest of Things10.1007/978-3-319-54334-5_3(24-44)Online publication date: 18-Feb-2017
  • (2016)Measuring and optimizing distributed array programsProceedings of the VLDB Endowment10.14778/2994509.29945119:12(912-923)Online publication date: 1-Aug-2016

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