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Scalable I/O-bound parallel incremental gradient descent for big data analytics in GLADE

Authors:

Florin RusuAuthors Info & Claims

DanaC '13: Proceedings of the Second Workshop on Data Analytics in the Cloud

Pages 16 - 20

https://doi.org/10.1145/2486767.2486771

Published: 23 June 2013 Publication History

Abstract

Incremental gradient descent is a general technique to solve a large class of convex optimization problems arising in many machine learning tasks. GLADE is a parallel infrastructure for big data analytics providing a generic task specification interface. In this paper, we present a scalable and efficient parallel solution for incremental gradient descent in GLADE. We provide empirical evidence that our solution is limited only by the physical hardware characteristics, uses effectively the available resources, and achieves maximum scalability. When deployed in the cloud, our solution has the potential to dramatically reduce the cost of complex analytics over massive datasets.

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Y. Cheng, C. Qin, and F. Rusu. GLADE: Big Data Analytics Made Easy. In SIGMOD 2012.

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Cited By

Marz SZanden BGao W(2019)Reducing Event Latency and Power Consumption in Mobile Devices by Using a Kernel-Level Display ServerIEEE Transactions on Mobile Computing10.1109/TMC.2018.285780918:5(1174-1187)Online publication date: 1-May-2019
https://doi.org/10.1109/TMC.2018.2857809
Ma YRusu FTorres M(2019)Stochastic Gradient Descent on Modern Hardware: Multi-core CPU or GPU? Synchronous or Asynchronous?2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS.2019.00113(1063-1072)Online publication date: May-2019
https://doi.org/10.1109/IPDPS.2019.00113
Jain YYogesh (2019)A Survey on Railway Assets: A Potential Domain for Big Data2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)10.1109/ICICT46931.2019.8977714(1-6)Online publication date: Sep-2019
https://doi.org/10.1109/ICICT46931.2019.8977714
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Scalable I/O-bound parallel incremental gradient descent for big data analytics in GLADE
1. Information systems
  1. Data management systems

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Published In

cover image ACM Conferences

DanaC '13: Proceedings of the Second Workshop on Data Analytics in the Cloud

June 2013

49 pages

ISBN:9781450322027

DOI:10.1145/2486767

Conference Chairs:
Kostas Tzoumas
Technische Universität Berlin
,
Shivnath Babu
Duke University

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 June 2013

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SIGMOD/PODS'13

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SIGMOD

SIGMOD/PODS'13: International Conference on Management of Data

June 23, 2013

New York, New York

Acceptance Rates

DanaC '13 Paper Acceptance Rate 9 of 16 submissions, 56%;

Overall Acceptance Rate 19 of 34 submissions, 56%

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Cited By

Marz SZanden BGao W(2019)Reducing Event Latency and Power Consumption in Mobile Devices by Using a Kernel-Level Display ServerIEEE Transactions on Mobile Computing10.1109/TMC.2018.285780918:5(1174-1187)Online publication date: 1-May-2019
https://doi.org/10.1109/TMC.2018.2857809
Ma YRusu FTorres M(2019)Stochastic Gradient Descent on Modern Hardware: Multi-core CPU or GPU? Synchronous or Asynchronous?2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS.2019.00113(1063-1072)Online publication date: May-2019
https://doi.org/10.1109/IPDPS.2019.00113
Jain YYogesh (2019)A Survey on Railway Assets: A Potential Domain for Big Data2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)10.1109/ICICT46931.2019.8977714(1-6)Online publication date: Sep-2019
https://doi.org/10.1109/ICICT46931.2019.8977714
Jain PSharma S(2019)Prioritizing Factors Used in Designing of Test Cases: An ISM-MICMAC Based Analysis2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)10.1109/ICICT46931.2019.8977643(1-4)Online publication date: Sep-2019
https://doi.org/10.1109/ICICT46931.2019.8977643
Qin CRusu FChoudhary AWu KDong B(2017)Dot-Product JoinProceedings of the 29th International Conference on Scientific and Statistical Database Management10.1145/3085504.3085512(1-12)Online publication date: 27-Jun-2017
https://dl.acm.org/doi/10.1145/3085504.3085512
Jiang JCui BZhang CYu LChirkova RYang JSuciu D(2017)Heterogeneity-aware Distributed Parameter ServersProceedings of the 2017 ACM International Conference on Management of Data10.1145/3035918.3035933(463-478)Online publication date: 9-May-2017
https://dl.acm.org/doi/10.1145/3035918.3035933
Sparks EVenkataraman SKaftan TFranklin MRecht B(2017)KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics2017 IEEE 33rd International Conference on Data Engineering (ICDE)10.1109/ICDE.2017.109(535-546)Online publication date: Apr-2017
https://doi.org/10.1109/ICDE.2017.109
Schleich MOlteanu DCiucanu RÖzcan FKoutrika GMadden S(2016)Learning Linear Regression Models over Factorized JoinsProceedings of the 2016 International Conference on Management of Data10.1145/2882903.2882939(3-18)Online publication date: 26-Jun-2016
https://dl.acm.org/doi/10.1145/2882903.2882939
Wang BTorres MLi DZhao JRusu F(2016)Performance Implications of Processing-in-Memory Designs on Data-Intensive Applications2016 45th International Conference on Parallel Processing Workshops (ICPPW)10.1109/ICPPW.2016.31(115-122)Online publication date: Aug-2016
https://doi.org/10.1109/ICPPW.2016.31
Qin CRusu F(2015)Speculative Approximations for Terascale Distributed Gradient Descent OptimizationProceedings of the Fourth Workshop on Data analytics in the Cloud10.1145/2799562.2799563(1-10)Online publication date: 31-May-2015
https://dl.acm.org/doi/10.1145/2799562.2799563
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