Mixed Data-Parallel Scheduling for Distributed Continuous Integration

Olivier Beaumont 1, 2 Nicolas Bonichon 1, 2 Ludovic Courtès 1 Xavier Hanin 3 Eelco Dolstra 4
1 CEPAGE - Algorithmics for computationally intensive applications over wide scale distributed platforms
Université Sciences et Technologies - Bordeaux 1, Inria Bordeaux - Sud-Ouest, École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), CNRS - Centre National de la Recherche Scientifique : UMR5800
Abstract : In this paper, we consider the problem of schedul- ing a special kind of mixed data-parallel applications arising in the context of continuous integration. Continuous integration (CI) is a software engineering technique, which consists in re- building and testing interdependent software components as soon as developers modify them. The CI tool is able to provide quick feedback to the developers, which allows them to fix the bug soon after it has been introduced. The CI process can be described as a DAG where nodes represent package build tasks, and edges represent dependencies among these packages; build tasks themselves can in turn be run in parallel. Thus, CI can be viewed as a mixed data-parallel application. A crucial point for a successful CI process is its ability to provide quick feedback. Thus, makespan minimization is the main goal. Our contribution is twofold. First we provide and analyze a large dataset corresponding to a build DAG. Second, we compare the performance of several scheduling heuristics on this dataset.
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Communication dans un congrès
IEEE. Heterogeneity in Computing Workshop, in IPDPS 2012, May 2012, Shangaï, China. 2012, Proceedings IPDPS 2012
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Olivier Beaumont, Nicolas Bonichon, Ludovic Courtès, Xavier Hanin, Eelco Dolstra. Mixed Data-Parallel Scheduling for Distributed Continuous Integration. IEEE. Heterogeneity in Computing Workshop, in IPDPS 2012, May 2012, Shangaï, China. 2012, Proceedings IPDPS 2012. 〈hal-00684220〉

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