Analyzing Scalability of Parallel Algorithms and Architectures

Kumar, Vipin; Gupta, Anshul

doi:10.1006/jpdc.1994.1099

Cited by 139 publications

(48 citation statements)

References 25 publications

(35 reference statements)

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“…A comprehensive discussion of various scalability and performance measures can be found in the survey by Kumar and Gupta [50]. While over ten years old, the results cited in this survey and their applications are particularly relevant now, as scalable parallel platforms are finally being realized.…”

Section: Cost-optimality and The Isoefficiency Functionmentioning

confidence: 99%

Evaluating Sparse Linear System Solvers on Scalable Parallel Architectures

Grama¹,

Manguoğlu²,

Koyutürk³

et al. 2008

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Section: Cost-optimality and The Isoefficiency Functionmentioning

confidence: 99%

Evaluating Sparse Linear System Solvers on Scalable Parallel Architectures

Grama¹,

Manguoğlu²,

Koyutürk³

et al. 2008

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“…The crucial step is to identify the overhead as a function of problem scale and system scale. The scalability is evaluated with a realistic tool-the isoefficiency function (Kumar et al 1994) based on the characterized communication patterns and overhead sources. Kumar et al (1994) point out that if the parallel efficiency can be maintained constant as the total work grows at least linearly with p, then the parallel implementation is scalable.…”

Section: Scalability Analysismentioning

confidence: 99%

“…The scalability is evaluated with a realistic tool-the isoefficiency function (Kumar et al 1994) based on the characterized communication patterns and overhead sources. Kumar et al (1994) point out that if the parallel efficiency can be maintained constant as the total work grows at least linearly with p, then the parallel implementation is scalable. The authors have previously used the isoefficiency function to analyze the performance of the restarted generalized minimum residual (GMRES) method (Saad 2003) for the iterative solution of large scale sparse linear systems (Sosonkina et al 2002).…”

Section: Scalability Analysismentioning

confidence: 99%

Performance Modeling and Analysis of a Massively Parallel Direct—Part 1

Verstak

Watson

et al. 2009

The International Journal of High Performance Computing Applica

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Modeling and analysis techniques are used to investigate the performance of a massively parallel version of DIRECT, a global search algorithm widely used in multidisciplinary design optimization applications. Several high-dimensional benchmark functions and real world problems are used to test the design effectiveness under various problem structures. In this second part of a two-part work, theoretical and experimental results are compared for two parallel clusters with different system scale and network connectivity. The first part studied performance sensitivity to important parameters for problem configurations and parallel schemes, using performance metrics such as memory usage, load balancing, and parallel efficiency. Here linear regression models are used to characterize two major overhead sources-interprocessor communication and processor idleness-and also applied to the isoefficiency functions in scalability analysis. For a variety of high-dimensional problems and large scale systems, the massively parallel design has achieved reasonable performance. The results of the performance study provide guidance for efficient problem and scheme configuration. More importantly, the design considerations and analysis techniques generalize to the transformation of other global search algorithms into effective large scale parallel optimization tools.

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“…A nice summary of existing approaches is presented in [7,11]. We suggest a model for a coarse subdivision of parallel runtime into "good" and "bad" parts.…”

Section: Introductionmentioning

confidence: 99%

Estimating parallel performance, a skeleton-based approach

Lobachev

Loogen

2010

Proceedings of the Fourth International Workshop on High-Level Parallel Programming and Applications

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Abstract. In this paper we estimate parallel execution times, based on identifying separate "parts" of the work done by parallel programs which are defined using algorithmic skeletons. Our runtime analysis works without any source code inspection. The time of parallel program execution is expressed in terms of the sequential work and the parallel penalty. We obtain these values for different problem sizes and numbers of processors and estimate them for unknown values in both dimensions. This allows us to predict parallel execution time for unknown inputs and non-available processor numbers. Another useful application of our formalism is a measure of parallel program quality. We analyse the values for parallel penalty both for growing input size and for increasing the number of processing elements. From the behaviour of these data, conclusions on parallel performance and scalability are drawn.

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Analyzing Scalability of Parallel Algorithms and Architectures

Cited by 139 publications

References 25 publications

Evaluating Sparse Linear System Solvers on Scalable Parallel Architectures

Evaluating Sparse Linear System Solvers on Scalable Parallel Architectures

Performance Modeling and Analysis of a Massively Parallel Direct—Part 1

Estimating parallel performance, a skeleton-based approach

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