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One of the methods to decrease the encoding time is to employ a parallel algorithm on a multicore system.
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In our earlier work, we developed a parallel algorithm for microwave tomography on CPU-based homogeneous, multi-core, distributed memory machines.
We report on the performance of a parallel algorithm for solving the Poisson equation on irregular domains.
Recently, Yang et al. proposed a parallel algorithm for generating optimal ISTs on the hypercube.
In this paper we present a parallel algorithm for CTL* model checking on a virtual shared-memory high-performance parallel machine architecture.
In this paper, we present a parallel algorithm to solve the above problem on a star graph.
The working environment is represented by a Bump-surface entity, constantly updated by a parallel algorithm implemented on a graphical processing unit (GPU).
In this subsection, we will introduce a parallel algorithm based on MapReduce, which utilizes the key-value processing procedure to avoid the data gathering and sorting procedures.
This paper presents a parallel algorithm implemented on graphics processing units (GPUs) for rapidly evaluating spatial convolutions between the Helmholtz potential and a large-scale source distribution.
Because of the highly complex nature of this approximate problem, a parallel algorithm based on the filled function method is constructed to solve this approximate problem.
In this paper, we suggest a parallel algorithm based on a shared memory SIMD architecture for solving an n item subset-sum problem in time O(2n/2/p) by using p = 2q processors, 0⩽q⩽n2−2log2n.
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