
Analytical Estimation of the Scalability of Iterative Numerical Algorithms on Distributed Memory Multiprocessors
This article presents a new highlevel parallel computational model name...
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BSFskeleton: A Template for Parallelization of Iterative Numerical Algorithms on Cluster Computing Systems
This article describes a method for creating applications for cluster co...
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BSF: a parallel computation model for scalability estimation of iterative numerical algorithms on cluster computing systems
This paper examines a new parallel computation model called bulk synchro...
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Verification of BSF Parallel Computational Model
The article is devoted to the verification of the BSF parallel computing...
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Scalable Simple Linear Iterative Clustering (SSLIC) Using a Generic and Parallel Approach
Superpixel algorithms have proven to be a useful initial step for segmen...
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Scalable Algorithms for High Order Approximations on Compact Stencils
The recent development of parallel technologies on modern desktop comput...
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Systematic Generation of Algorithms for Iterative Methods
The FLAME methodology makes it possible to derive provably correct algor...
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Scalability Evaluation of Iterative Algorithms Used for Supercomputer Simulation of Physical processes
The paper is devoted to the development of a methodology for evaluating the scalability of computeintensive iterative algorithms used in simulating complex physical processes on supercomputer systems. The proposed methodology is based on the BSF (Bulk Synchronous Farm) parallel computation model, which makes it possible to predict the upper scalability bound of an iterative algorithm in early phases of its design. The BSF model assumes the representation of the algorithm in the form of operations on lists using highorder functions. Two classes of representations are considered: BSFM (Map BSF) and BSFMR (MapReduce BSF). The proposed methodology is described by the example of the solution of the system of linear equations by the Jacobi method. For the Jacobi method, two iterative algorithms are constructed: JacobiM based on the BSFM representation and JacobiMR based on the BSFMR representation. Analytical estimations of the speedup, parallel efficiency and upper scalability bound are constructed for these algorithms using the BSF cost metrics on multiprocessor computing systems with distributed memory. An information about the implementation of these algorithms in C++ language using the BSF program skeleton and MPI parallel programming library are given. The results of largescale computational experiments performed on a cluster computing system are demonstrated. Based on the experimental results, an analysis of the adequacy of estimations obtained analytically by using the cost metrics of the BSF model is made.
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