Programming Grid Applications with Gridge

Pukacki, Juliusz; Kosiedowski, Michal; Mikolajczak, Rafal; Adamski, Marcin; Grabowski, Piotr; Jankowski, Michał; Kupczyk, Miroslaw; Mazurek, Cezary; Meyer, Norbert; Nabrzyski, Jarek; Piontek, Tomasz; Russell, Michael; Stroiński, Maciej; Wolski, Marcin

doi:10.12921/cmst.2006.12.01.47-68

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…The grid infrastructure uses R as interface and client. The server side implementation of GridR uses several external software components: Globus Toolkit 4 grid middleware, an installation of the R environment on the grid machines, and a GRMS-Server installation from the Gridge toolkit (Pukacki et al 2006) on a central machine in the grid environment.…”

Section: Gridrmentioning

confidence: 99%

State of the Art in Parallel Computing withR

Schmidberger¹,

Morgan²,

Eddelbuettel³

et al. 2009

J. Stat. Soft.

106

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R is a mature open-source programming language for statistical computing and graphics. Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A common approach is to use parallel computing. This paper presents an overview of techniques for parallel computing with R on computer clusters, on multi-core systems, and in grid computing. It reviews sixteen different packages, comparing them on their state of development, the parallel technology used, as well as on usability, acceptance, and performance. Two packages (snow, Rmpi) stand out as particularly suited to general use on computer clusters. Packages for grid computing are still in development, with only one package currently available to the end user. For multi-core systems five different packages exist, but a number of issues pose challenges to early adopters. The paper concludes with ideas for further developments in high performance computing with R. Example code is available in the appendix.

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Section: Gridrmentioning

confidence: 99%

State of the Art in Parallel Computing withR

Schmidberger¹,

Morgan²,

Eddelbuettel³

et al. 2009

J. Stat. Soft.

106

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“…Mainly, it uses semantic web and ontologies for data integration and knowledge discovery and Globus toolkit with its WS-GRAM, MDS and GSI services for cross organization resource sharing, job execution, monitoring and result visualization. Additionally, ACGT also uses some higher level grid services from Gridge framework developed at Poznan Supercomputing and Networking Centre (PSNC) [116]. These services include GRMS (Grid Resource Management System), GAS (Grid Authorization System) and DMS (Data Management System).…”

Section: Advancing Clinico-genomic Trials On Cancer (Acgt)mentioning

confidence: 99%

Web and Grid Technologies in Bioinformatics, Computational and Systems Biology: A Review

Krasnogor¹,

Shah²,

Barthel³

et al. 2008

CBIO

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The acquisition of biological data, ranging from molecular characterization and simulations (e.g. protein folding dynamics), to systems biology endeavors (e.g. whole organ simulations) all the way up to ecological observations (e.g. as to ascertain climate change's impact on the biota) is growing at unprecedented speed. The use of computational and networking resources is thus unavoidable. As the datasets become bigger and the acquisition technology more refined, the biologist is empowered to ask deeper and more complex questions. These, in turn, drive a runoff effect where large research consortia emerge that span beyond organizations and national boundaries. Thus the need for reliable, robust, certified, curated, accessible, secure and timely data processing and management becomes entrenched within, and crucial to, 21 st century biology. Furthermore, the proliferation of biotechnologies and advances in biological sciences has produced a strong drive for new informatics solutions, both at the basic science and technological levels. The previously unknown situation of dealing with, on one hand, (potentially) exabytes of data, much of which is noisy, has large experimental errors or theoretical uncertainties associated with it, or on the other hand, large quantities of data that require automated computationally intense analysis and processing, have produced important innovations in web and grid technology. In this paper we present a trace of these technological changes in Web and Grid technology, including details of emerging infrastructures, standards, languages and tools, as they apply to bioinformatics, computational biology and systems biology. A major focus of this technological review is to collate up-to-date information regarding the design and implementation of various bioinformatics Webs, Grids, Web-based grids or Grid-based webs in terms of their infrastructure, standards, protocols, services, applications and other tools. This review, besides surveying the current state-of-the-art, will also provide a road map for future research and open questions.

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“…In the ACGT platform, the GridR service is implemented as a GSI-secured grid service based on the Globus Toolkit 4 libraries [7] and on the Gridge Toolkit [8], mainly using the DMS (data management system) and GRMS (grid resource management system) components of the grid substrate. The interface of the service supports the execution of userdefined scripts as well as the execution of scripts that had been pre-registered in a repository.…”

Section: Gridr In Acgtmentioning

confidence: 99%

Supporting Parallel R Code in Clinical Trials: A Grid-Based Approach

Wegener

Sengstag

Sfakianakis

et al. 2008

2008 IEEE International Symposium on Parallel and Distributed Processing With Applications

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In this paper, we describe an extension to the ACGT GridR environment which allows the parallelization of loops in R scripts in view of their distributed execution on a computational grid. The ACGT GridR service is extended by a component that uses a set of preprocessor-like directives to organize and distribute calculations. The use of parallelization directives as special R comments provides users with the potential to accelerate lengthy calculations with changes to preexisting code.The GridR service and its extension are developed as components of the ACGT platform, one aim of which is to facilitate the data mining of clinical trials involving large datasets. In ACGT, GridR scripts are executed in the framework of a specifically developed workflow environment, which is also briefly outlined in the present article.

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Programming Grid Applications with Gridge

Abstract: Not many fully integrated Grid solutions exist on today's market. In this paper we present the PSNC's Grid toolkit, called Gridge, which is fully integrated Grid environment, consisting of tools and Grid services to enable challenging scientific and commercial applications on the Grid.

Cited by 10 publications

References 6 publications

State of the Art in Parallel Computing withR

State of the Art in Parallel Computing withR

Web and Grid Technologies in Bioinformatics, Computational and Systems Biology: A Review

Supporting Parallel R Code in Clinical Trials: A Grid-Based Approach

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