Towards a High Level Programming Paradigm to Deploy e-Science Applications with Dynamic Workflows on Large Scale Distributed Systems

This papers targeted scientists and programmers who need to easily develop and run e-science applications on large scale distributed systems. We present a rich programming paradigm and environment used to develop and deploy high performance applications (HPC) on large scale distributed and heterogeneous platforms. We particularly target iterative e-science applications where (i) convergence conditions and number of jobs are not known in advance, (ii) jobs are created on the fly and (iii) jobs could be persistent. We propose two programming paradigms so as to provide intuitive statements enabling an easy writing of HPC e-science applications. Non-expert developers (scientific researchers) can use them to guarantee fast development and efficient deployment of their applications.

[1]  Mohamed Ben Belgacem,et al.  Programming distributed medical applications with XWCH2 , 2010, HealthGrid.

[2]  Hélène Coullon,et al.  Parallelization of Shallow-Water Equations with the Algorithmic Skeleton Library SkelGIS , 2013, ICCS.

[3]  Dana Petcu and José Luis Vázquez-Poletti.  European Research Activities in Cloud Computing , 2012 .

[4]  Da Di.  Histoire du peuplement de l'Asie orientale révélée par le système HLA , 2013 .

[5]  Antonio Laganà,et al.  A Grid Execution Model for Computational Chemistry Applications Using the GC3Pie Framework and the AppPot VM Environment , 2012, ICCSA.

[6]  Weizhong Qiang,et al.  Towards cross-middleware authentication and single sign-on for ARC Grid middleware , 2009, Computer Science - Research and Development.

[7]  Mohamed Ben Belgacem,et al.  Virtual EZ Grid: A Volunteer Computing Infrastructure for Scientific Medical Applications , 2010, Int. J. Handheld Comput. Res..

[8]  Mario Leyton,et al.  Skandium: Multi-core Programming with Algorithmic Skeletons , 2010, 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing.

[9]  Michael Isard,et al.  Distributed aggregation for data-parallel computing: interfaces and implementations , 2009, SOSP '09.

[10]  Horacio González-Vélez,et al.  A survey of algorithmic skeleton frameworks: high‐level structured parallel programming enablers , 2010, Softw. Pract. Exp..

[11]  Thierry Pun,et al.  Content-based query of image databases: inspirations from text retrieval , 2000, Pattern Recognit. Lett..

[12]  ปฏิวิชช์ สาระพิน และคณะ.  การศึกษาความสัมพันธ์ระหว่างการเปลี่ยนแปลงการใช้ประโยชน์ที่ดินกับสมดุลน้ำ ในพื้นที่ชุ่มน้ำบึงบอระเพ็ดด้วยแบบจำลอง Soil and Water Assessment Tool , 2016 .

[13]  Michael Isard,et al.  DryadLINQ: A System for General-Purpose Distributed Data-Parallel Computing Using a High-Level Language , 2008, OSDI.

[14]  Michel C. Milinkovitch,et al.  MetaPIGA v2.0: maximum likelihood large phylogeny estimation using the metapopulation genetic algorithm and other stochastic heuristics , 2010, BMC Bioinformatics.

[15]  Sergio Maffioletti,et al.  Computational workflows with GC3Pie , 2012 .

[16]  Sergei Gorlatch,et al.  SkelCL - A Portable Skeleton Library for High-Level GPU Programming , 2011, 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum.

[17]  Florian Dittmann.  Algorithmic Skeletons for the Programming of Reconfigurable Systems , 2007, SEUS.

[18]  Nabil Abdennadher,et al.  Deploying PHYLIP phylogenetic package on a Large Scale Distributed System , 2007, Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07).

[19]  Coullon Helene,et al.  Parallelization of Shallow-water Equations with the Algorithmic Skeleton Library SkelGIS☆ , 2013, ICCS 2013.