Operator and Workflow Optimization for High-Performance Analytics

Hans Vandierendonck; Karen L. Murphy; Mahwish Arif; Jiawen Sun; Dimitrios S. Nikolopoulos

Operator and Workflow Optimization for High-Performance Analytics

Hans Vandierendonck, Karen L. Murphy, Mahwish Arif, Jiawen Sun, Dimitrios S. Nikolopoulos

Research output: Contribution to conference › Paper › peer-review

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Abstract

We make a case for studying the impact of intra-node parallelism on the performance of data analytics. We identify four performance optimizations that are enabled by an increasing number of processing cores on a chip. We discuss the performance impact of these opimizations on two analytics operators and we identify how these optimizations affect each another.

Original language	English
Number of pages	4
Publication status	Accepted - 15 Mar 2016
Event	1st International Workshop on Multi-Engine Data Analytics (MEDAL) - Bordeaux, France Duration: 15 Mar 2016 → 15 Mar 2016

Conference

Conference	1st International Workshop on Multi-Engine Data Analytics (MEDAL)
Country/Territory	France
City	Bordeaux
Period	15/03/2016 → 15/03/2016

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Operator and Workflow Optimization for High-Performance Analytics
© 2016 The Authors This is an open access article published under a Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits distribution and reproduction for non-commercial purposes, provided the author and source are cited.
Accepted author manuscript, 207 KB

R1451CSC: Hybrid Static/Dynamic Scheduling for Task Dataflow Parallel Programs
Vandierendonck, H.
28/07/2014 → 02/03/2017
Project: Research
R6438CSC: An Adaptive, highly Scalable Analytics Platform
Vandierendonck, H., Nikolopoulos, D. & Robinson, P.
21/03/2014 → 28/02/2017
Project: Research

MEDAL workshop paper dataset
Vandierendonck, H. (Creator), Queen's University Belfast, 08 Feb 2016
DOI: 10.17034/fdcd81c1-7c89-402e-95ab-951cecfc8af7
Dataset

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Cite this

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title = "Operator and Workflow Optimization for High-Performance Analytics",

abstract = "We make a case for studying the impact of intra-node parallelism on the performance of data analytics. We identify four performance optimizations that are enabled by an increasing number of processing cores on a chip. We discuss the performance impact of these opimizations on two analytics operators and we identify how these optimizations affect each another.",

author = "Hans Vandierendonck and Murphy, {Karen L.} and Mahwish Arif and Jiawen Sun and Nikolopoulos, {Dimitrios S.}",

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AU - Vandierendonck, Hans

AU - Murphy, Karen L.

AU - Arif, Mahwish

AU - Sun, Jiawen

AU - Nikolopoulos, Dimitrios S.

PY - 2016/3/15

Y1 - 2016/3/15

N2 - We make a case for studying the impact of intra-node parallelism on the performance of data analytics. We identify four performance optimizations that are enabled by an increasing number of processing cores on a chip. We discuss the performance impact of these opimizations on two analytics operators and we identify how these optimizations affect each another.

AB - We make a case for studying the impact of intra-node parallelism on the performance of data analytics. We identify four performance optimizations that are enabled by an increasing number of processing cores on a chip. We discuss the performance impact of these opimizations on two analytics operators and we identify how these optimizations affect each another.

M3 - Paper

T2 - 1st International Workshop on Multi-Engine Data Analytics (MEDAL)

Y2 - 15 March 2016 through 15 March 2016

ER -

Operator and Workflow Optimization for High-Performance Analytics

Abstract

Conference

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Projects

R1451CSC: Hybrid Static/Dynamic Scheduling for Task Dataflow Parallel Programs

R6438CSC: An Adaptive, highly Scalable Analytics Platform

Datasets

MEDAL workshop paper dataset

Cite this