The CCA core specification in a distributed memory SPMD framework

Allan, Benjamin A.; Armstrong, Robert C.; Wolfe, Alicia P.; Ray, Jaideep; Bernholdt, David E.; Kohl, James Arthur

doi:10.1002/cpe.651

Cited by 97 publications

(72 citation statements)

References 10 publications

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“…For this purpose we have already done some experiments using software development frameworks that should be well suited for Grid aware software development and deployment. In particular, we have considered the use of ASSIST [54], a high level structured parallel programming environment, and Ccaffeine [55], a Common Component Architecture (CCA) compliant framework for parallel computing. …”

Section: Discussionmentioning

confidence: 99%

Parallel isosurface extraction for 3D data analysis workflows in distributed environments

D’Agostino

Clematis

Gianuzzi

2011

Concurrency and Computation

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In this paper we discuss the issues related to the development of efficient parallel implementations of the Marching Cubes algorithm, one of the most used methods for isosurface extraction, which is a fundamental operation for 3D data analysis and visualization. We present three possible parallelization strategies and we outline the pros and cons of each of them, considering isosurface extraction as stand-alone operation or as part of a dynamic workflow. Our analysis shows that none of these implementations represents the most efficient solution for arbitrary situations. This is a major issue, because in many cases the quality of the service provided by a workflow depends on the possibility of selecting dynamically the operations to perform and, consequently, the more efficient basic building block for each stage. In this paper we present a set of guidelines that permits to achieve the highest performance for the extraction of isosurface in the most common situations, considering the characteristics of the data to process and of the workflow. These guidelines represent a suitable example to support the efficient configurations of workflows for 3D data processing in a dynamic and complex computing environment

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Section: Discussionmentioning

confidence: 99%

Parallel isosurface extraction for 3D data analysis workflows in distributed environments

D’Agostino

Clematis

Gianuzzi

2011

Concurrency and Computation

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“…Ccaffeine (Allan et al 2002(Allan et al , 2003, the main CCA framework implementation for HPC parallel computing, supports the component analogs of both the single program/multiple data (SPMD) and multiple program/multiple data (MPMD) models. We refer to these as single or multiple component/multiple data (SCMD or MCMD) models.…”

Section: Parallel Componentsmentioning

confidence: 99%

A Component Architecture for High-Performance Scientific Computing

Allan

Armstrong²,

Bernholdt

et al. 2006

The International Journal of High Performance Computing Applica

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177

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The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of large-scale scientific simulations and to move toward a plug-and-play environment for high-performance computing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individuals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed computing as well as local high-performance connections between components in a language-independent manner. The design places minimal requirements on components and thus facilitates the integration of existing code into the CCA environment. The CCA model imposes minimal overhead to minimize the impact on application performance. The focus on high performance distinguishes the CCA from most other component models. The CCA is being applied within an increasing range of disciplines, including combustion research, global climate simulation, and computational chemistry.

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“…The main difference with Fractal is that CCA lacks hierarchical composition as a first-class part of the model. Ccaffeine [2] is an implementation of CCA that supports parallel computing. Ccaffeine-based components interact within a given process using CCA ports; parallel instances of Ccaffeine-based components interact across different processes using a separate programming model, typically MPI.…”

Section: Related Workmentioning

confidence: 99%