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Author

Mihael Hategan

Other affiliations: Argonne National Laboratory
Bio: Mihael Hategan is an academic researcher from University of Chicago. The author has contributed to research in topics: Grid computing & Grid. The author has an hindex of 16, co-authored 28 publications receiving 1775 citations. Previous affiliations of Mihael Hategan include Argonne National Laboratory.

Papers
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Journal ArticleDOI
01 Sep 2011
TL;DR: This work presents Swift's implicitly parallel and deterministic programming model, which applies external applications to file collections using a functional style that abstracts and simplifies distributed parallel execution.
Abstract: Scientists, engineers, and statisticians must execute domain-specific application programs many times on large collections of file-based data. This activity requires complex orchestration and data management as data is passed to, from, and among application invocations. Distributed and parallel computing resources can accelerate such processing, but their use further increases programming complexity. The Swift parallel scripting language reduces these complexities by making file system structures accessible via language constructs and by allowing ordinary application programs to be composed into powerful parallel scripts that can efficiently utilize parallel and distributed resources. We present Swift's implicitly parallel and deterministic programming model, which applies external applications to file collections using a functional style that abstracts and simplifies distributed parallel execution.

421 citations

Proceedings ArticleDOI
09 Jul 2007
TL;DR: Swift adopts and adapts ideas first explored in the GriPhyN virtual data system, improving on that system in many regards and describes application experiences and performance experiments that quantify the cost of Swift operations.
Abstract: We present Swift, a system that combines a novel scripting language called SwiftScript with a powerful runtime system based on CoG Karajan, Falkon, and Globus to allow for the concise specification, and reliable and efficient execution, of large loosely coupled computations. Swift adopts and adapts ideas first explored in the GriPhyN virtual data system, improving on that system in many regards. We describe the SwiftScript language and its use of XDTM to describe the logical structure of complex file system structures. We also present the Swift runtime system and its use of CoG Karajan, Falkon, and Globus services to dispatch and manage the execution of many tasks in parallel and grid environments. We describe application experiences and performance experiments that quantify the cost of Swift operations.

387 citations

Proceedings ArticleDOI
05 Jan 2004
TL;DR: This paper discusses the design principles, functionality, and application of the proposed GridAnt workflow manager, an extensible client-side workflow management system, called GridAnt, developed.
Abstract: Process management is an extremely important concept in both business and scientific communities. Several workflow management tools have been proposed in recent years offering advanced functionality in various domains. In the business world, workflow vendors offer commercial and customized solutions targeting specific users. In the scientific world, several open-source workflow management tools are freely available. However they are directed toward service aggregation rather than distributed process management. Little consideration is given to the needs of the client in terms of mapping the process flow of the client. In the grid community it is essential that the grid users have such a tool available enabling them to orchestrate complex work-flows on the fly without substantial help from the service providers. At the same time it is important that the grid user not be burdened with the intricacies of the workflow system. With the perspective of the grid user in mind, an extensible client-side workflow management system, called GridAnt, has been developed. This paper discusses the design principles, functionality, and application of the proposed GridAnt workflow manager.

235 citations

Journal ArticleDOI
TL;DR: Parallel scripting extends this technique to allow for the rapid development of highly parallel applications that can run efficiently on platforms ranging from multicore workstations to petascale supercomputers.
Abstract: Scripting accelerates and simplifies the composition of existing codes to form more powerful applications. Parallel scripting extends this technique to allow for the rapid development of highly parallel applications that can run efficiently on platforms ranging from multicore workstations to petascale supercomputers.

110 citations

Journal ArticleDOI
TL;DR: The Whole Tale project as discussed by the authors aims to connect computational, data-intensive research efforts with the larger research process, transforming the knowledge discovery and dissemination process into one where data products are united with research articles to create "living publications" or tales.

99 citations


Cited by
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Proceedings ArticleDOI
01 Nov 2008
TL;DR: In this article, the authors compare and contrast cloud computing with grid computing from various angles and give insights into the essential characteristics of both the two technologies, and compare the advantages of grid computing and cloud computing.
Abstract: Cloud computing has become another buzzword after Web 2.0. However, there are dozens of different definitions for cloud computing and there seems to be no consensus on what a cloud is. On the other hand, cloud computing is not a completely new concept; it has intricate connection to the relatively new but thirteen-year established grid computing paradigm, and other relevant technologies such as utility computing, cluster computing, and distributed systems in general. This paper strives to compare and contrast cloud computing with grid computing from various angles and give insights into the essential characteristics of both.

3,132 citations

Proceedings Article
01 Jan 2003

1,212 citations

Journal ArticleDOI
TL;DR: The taxonomy provides end users with a mechanism by which they can assess the suitability of workflow in general and how they might use these features to make an informed choice about which workflow system would be a good choice for their particular application.

903 citations

Proceedings ArticleDOI
21 Jun 2010
TL;DR: This paper presents the programming model and the architecture of Twister an enhanced MapReduce runtime that supports iterative Map Reduce computations efficiently and shows performance comparisons of Twisters with other similar runtimes such as Hadoop and DryadLINQ for large scale data parallel applications.
Abstract: MapReduce programming model has simplified the implementation of many data parallel applications. The simplicity of the programming model and the quality of services provided by many implementations of MapReduce attract a lot of enthusiasm among distributed computing communities. From the years of experience in applying MapReduce to various scientific applications we identified a set of extensions to the programming model and improvements to its architecture that will expand the applicability of MapReduce to more classes of applications. In this paper, we present the programming model and the architecture of Twister an enhanced MapReduce runtime that supports iterative MapReduce computations efficiently. We also show performance comparisons of Twister with other similar runtimes such as Hadoop and DryadLINQ for large scale data parallel applications.

877 citations