High Performance Machine Learning (HPML) Framework to Support DDDAS Decision Support Systems: Design Overview

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

3 Scopus citations

Abstract

This paper presents a design for a High Performance Machine Learning (HPML) framework to support DDDAS decision processes. The HPML framework can provide a high performance computing environment to implement large scale machine learning algorithms that leverages Big Data tools (e.g., SPARK, Hadoop), parallel algorithms, and MapReduce programming paradigm. The framework provides the following capabilities: • High Performance Parallel Algorithms: For a suite of important ML, we will develop three parallel implementations of each algorithm that are based on Message Passing Interface (MPI), Shared Memory (SM) and MapReduce programming model. • High Performance and Scalable Platforms: This will enable us to identify the best high performance platform that maximizes performance and scalability of the parallel ML methods. We will experiment with and evaluate the performance and scalability of different parallel architectures (shared memory and message passing), Clusters of GPUs, and cloud computing systems. By leveraging the emerging Big Data tools and high performance computing algorithms (traditional and emerging paradigm such as MapReduce), we will be able to achieve the following: 1) reduce significantly the ML processing time, 2) enable StreamlinedML users to leverage Big Data tools to perform large scale ML tasks over structured and non-structured data sets; and 3) enable users to identify the best parallel platform and storage allocation and distribution that maximize performance and scalability of the selected ML algorithms.'

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages360-362
Number of pages3
ISBN (Electronic)9781509065585
DOIs
StatePublished - Oct 9 2017
Event2nd IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017 - Tucson, United States
Duration: Sep 18 2017Sep 22 2017

Other

Other2nd IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017
CountryUnited States
CityTucson
Period9/18/179/22/17

Keywords

  • Data Analytics
  • DDDAS
  • High Performance ML
  • Machine Learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Computational Mechanics

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    Ditzler, G., Hariri, S., & Akoglu, A. (2017). High Performance Machine Learning (HPML) Framework to Support DDDAS Decision Support Systems: Design Overview. In Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017 (pp. 360-362). [8064150] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FAS-W.2017.174