Introduction to Parallel Computing

Trobec, Roman; Slivnik, Boštjan; Buli, Patricio; Robi, Borut

doi:10.1007/978-3-319-98833-7

Cited by 25 publications

(11 citation statements)

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“…15 The previous MPI and open MPI is a well-known parallel algorithm for solving 1D heat equation was described. 16 For solving high dimensional problems of SLEs particularly, the distribution of subsets of the equation can be done by sending each into a number of computing machines or cores. One has been done was by using the accelerated projection-based consensus.…”

Section: Parallel Of Switching Modelsmentioning

confidence: 99%

Hybrid Lanczos-switching models for solving large linear systems and their parallel versions

Bakar

Thalib

Pratama

et al. 2023

Journal of Algorithms & Computational Technology

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Lanczos iterative methods for solving a large sparse linear systems typically face the latent breakdown which strikes every time these methods are deployed. A number of approaches to deal with this issue have been investigated. One of them is by switching between the solvers preemptively breakdown. However, the problem is not fully solved yet. Here, we propose switching models combined with particular Lanczos iterative methods. The first model is by using the last iterate as the switching point with an unlimited number of iterations, the second model is by using the iterate with the minimum residual norm as the initial point and the third model is by using the iterate with the minimum of minimum residual norms as the switching point. These three models lead algorithms of SLULast, SLUMinRes, and SLUMoM, respectively. The parallel version of the proposed algorithms is also provided to speed up their convergence. In this case, we constructed the parallel of SLUMoM and we call it pSLUMoM. The numerical results showed that our switching models performed better than the existing switching strategy in terms of robustness and efficiency. In fact, under a parallel framework, pSLUMoM showed a performance gain of up to 50% in our experiments.

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Section: Parallel Of Switching Modelsmentioning

confidence: 99%

Hybrid Lanczos-switching models for solving large linear systems and their parallel versions

Bakar

Thalib

Pratama

et al. 2023

Journal of Algorithms & Computational Technology

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“…Hence, each thread has one distinct random number generator. 40 We use the Linear Congruential Generator (LCG) algorithm to create our random number…”

Section: Tabu-numa Algorithmmentioning

confidence: 99%

“…We create a distinct seed for each thread using the thread id parameter to do this. Hence, each thread has one distinct random number generator 40 . We use the Linear Congruential Generator (LCG) algorithm to create our random number generator for Tabu‐NUMA.…”

Section: Proposed Algorithm Tabu‐numamentioning

confidence: 99%

A new parallel tabu search algorithm for the optimization of the maximum vertex weight clique problem

Dülger,

Dökeroğlu

2023

Concurrency and Computation

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SummaryThe efficiency of metaheuristic algorithms depends significantly on the number of fitness value evaluations performed on candidate solutions. In addition to various intelligent techniques used to obtain better results, parallelization of calculations can substantially improve the solutions in cases where the problem is NP‐hard and requires many evaluations. This study proposes a new parallel tabu search method for solving the Maximum Vertex Weight Clique Problem (MVWCP) on the Non‐Uniform Memory Access (NUMA) architectures using the OpenMP parallel programming paradigm. Achieving scalability in the NUMA architectures presents significant challenges due to the high complexity of their memory systems, which can lead to performance loss. However, our proposed Tabu‐NUMA algorithm provides up to speed‐up with 64 cores for ten basic problem instances in DIMACS‐W and BHOSLIB‐W benchmarks. And it improves the performance of the serial Multi Neighborhood Tabu Search (MN/TS) algorithm for 38 problem instances in DIMACS‐W and BHOSLIB‐W benchmarks. We further evaluate our algorithm on larger datasets with thousands of edges and vertices from Network Data Repository benchmark problem instances, and we report significant improvements in terms of speed up. Our results confirm that the Tabu‐NUMA algorithm is among the best recent algorithms for solving MVWCP on the NUMA architectures.

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“…Many other features are also available within OpenMP, and we refer the reader to the specifications 6 for details. Trobec et al (2018) and van der Pas et al (2017) also provide details on parallel computing and HPC. Alternatives to OpenMP are discussed in Section 5.…”

Section: Vectorisation and Multithreading With Openmpmentioning

confidence: 99%

“…The paradigms of parallelism are distributed computing, multithreading, vectorisation, and pipelining (Trobec et al, 2018;van der Pas et al, 2017). Each paradigm has a granularity that refers to the ratio of communication to computation for a parallel workload.…”

Section: Introductionmentioning

confidence: 99%

Vector Operations for Accelerating Expensive Bayesian Computations – A Tutorial Guide

2021

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Many applications in Bayesian statistics are extremely computationally intensive. However, they are often inherently parallel, making them prime targets for modern massively parallel processors. Multi-core and distributed computing is widely applied in the Bayesian community, however, very little attention has been given to fine-grain parallelisation using single instruction multiple data (SIMD) operations that are available on most modern CPUs. In this work, we practically demonstrate, using standard programming libraries, the utility of the SIMD approach for several topical Bayesian applications. Using the C programming language, we show that SIMD can improve the single-core floating point arithmetic performance by up to a factor of 6× compared scalar C code and more than 25× compared with optimised R code. Such improvements are multiplicative to any gains achieved through multi-core processing. We illustrate the potential of SIMD for accelerating Bayesian computations and provide the reader with techniques for exploiting modern massively parallel processing environments.

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Introduction to Parallel Computing

Cited by 25 publications

References 0 publications

Hybrid Lanczos-switching models for solving large linear systems and their parallel versions

Hybrid Lanczos-switching models for solving large linear systems and their parallel versions

A new parallel tabu search algorithm for the optimization of the maximum vertex weight clique problem

Vector Operations for Accelerating Expensive Bayesian Computations – A Tutorial Guide

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