Recent studies and industry practices build data-center-scale computer
systems to meet the high storage and processing demands
of data-intensive and compute-intensive applications, such as web
searches. The Map-Reduce programming model is one of the most
popular programming paradigms on these systems. In this paper,
we report our experiences and insights gained from implementing
three data-intensive and compute-intensive tasks that have different
characteristics from previous studies: a large-scale machine learning
computation, a physical simulation task, and a digital media
processing task. We identify desirable features and places to improve
in the Map-Reduce model. Our goal is to better understand
such large-scale computation and data processing in order to design
better supports for them.
%0 Report
%1 IRP-TR-08-05
%A Chen, Shimin
%A Schlosser, Steven W.
%D 2008
%K awm2010 awmhadoop hadoop map meets reduce varieties wider
%T Map-Reduce Meets Wider Varieties of Applications.
%U http://www.pittsburgh.intel-research.net/~chensm/papers/IRP-TR-08-05.pdf
%X Recent studies and industry practices build data-center-scale computer
systems to meet the high storage and processing demands
of data-intensive and compute-intensive applications, such as web
searches. The Map-Reduce programming model is one of the most
popular programming paradigms on these systems. In this paper,
we report our experiences and insights gained from implementing
three data-intensive and compute-intensive tasks that have different
characteristics from previous studies: a large-scale machine learning
computation, a physical simulation task, and a digital media
processing task. We identify desirable features and places to improve
in the Map-Reduce model. Our goal is to better understand
such large-scale computation and data processing in order to design
better supports for them.
@techreport{IRP-TR-08-05,
abstract = {Recent studies and industry practices build data-center-scale computer
systems to meet the high storage and processing demands
of data-intensive and compute-intensive applications, such as web
searches. The Map-Reduce programming model is one of the most
popular programming paradigms on these systems. In this paper,
we report our experiences and insights gained from implementing
three data-intensive and compute-intensive tasks that have different
characteristics from previous studies: a large-scale machine learning
computation, a physical simulation task, and a digital media
processing task. We identify desirable features and places to improve
in the Map-Reduce model. Our goal is to better understand
such large-scale computation and data processing in order to design
better supports for them.},
added-at = {2010-05-10T09:33:14.000+0200},
author = {Chen, Shimin and Schlosser, Steven W.},
biburl = {https://www.bibsonomy.org/bibtex/2e3b284e09d2a3fd3992f7eb70ddd061a/muehlburger},
interhash = {77d8b41029ab54cd6c78ca06d0ad4d59},
intrahash = {e3b284e09d2a3fd3992f7eb70ddd061a},
keywords = {awm2010 awmhadoop hadoop map meets reduce varieties wider},
timestamp = {2010-06-23T08:22:48.000+0200},
title = {Map-Reduce Meets Wider Varieties of Applications.},
url = {http://www.pittsburgh.intel-research.net/~chensm/papers/IRP-TR-08-05.pdf},
year = 2008
}