scholarly journals Introductory Chapter: High Performance Parallel Computing

2019 ◽  
Author(s):  
Satyadhyan Chickerur
Keyword(s):  
Parallel Computing ◽  
High Performance

Matlab and Parallel Computing

2012 ◽  
Vol 17 (4) ◽  
pp. 207-216 ◽  
Author(s):  
Magdalena Szymczyk ◽  
Piotr Szymczyk
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Parallel Computing ◽  
Distributed Computing ◽  
Control Systems ◽  
High Performance ◽  
Parallel Applications ◽  
Process Simulations ◽  
Key Features ◽  
Financial Process

Abstract The MATLAB is a technical computing language used in a variety of fields, such as control systems, image and signal processing, visualization, financial process simulations in an easy-to-use environment. MATLAB offers "toolboxes" which are specialized libraries for variety scientific domains, and a simplified interface to high-performance libraries (LAPACK, BLAS, FFTW too). Now MATLAB is enriched by the possibility of parallel computing with the Parallel Computing ToolboxTM and MATLAB Distributed Computing ServerTM. In this article we present some of the key features of MATLAB parallel applications focused on using GPU processors for image processing.

Application of High Performance Parallel Computing Based on GPU

2013 ◽  
Vol 411-414 ◽  
pp. 585-588
Author(s):  
Liu Yang ◽  
Tie Ying Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Parallel Computing ◽  
Parallel Computation ◽  
High Performance ◽  
Search Process ◽  
Search Rate ◽  
Swarm Optimization ◽  
Path Search ◽  
Parallel Feature ◽  
Time And Space Complexity

This paper introduces parallel feature of the GPU, which will help GPU parallel computation methods to achieve the parallelization of PSO parallel path search process; and reduce the increasingly high problem of PSO (PSO: Particle Swarm Optimization) in time and space complexity. The experimental results show: comparing with CPU mode, GPU platform calculation improves the search rate and shortens the calculation time.

scholarly journals METHODS AND ALGORITHMS FOR PARALLEL CALCULATIONS USING VIRTUALIZATION TECHNOLOGIES IN MATERIALS SCIENCE PROBLEMS

2021 ◽  
Author(s):  
Vadim Kondrashev ◽  
Sergey Denisov
Keyword(s):  
Parallel Computing ◽  
High Performance Computing ◽  
Material Science ◽  
High Performance ◽  
Applied Research ◽  
Materials Science ◽  
Parallel Calculations ◽  
Computing Technologies ◽  
Integrated Software ◽  
Performance Computing

The paper discusses methods and algorithms for the provision of high-performance computing resources in multicomputer systems in a shared mode for fundamental and applied research in the field of materials science. Approaches are proposed for the application of applied integrated software environments (frameworks) designed to solve material science problems using virtualization and parallel computing technologies.

scholarly journals SPINET: A Parallel Computing Approach to Spine Simulations

1996 ◽  
Vol 5 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Peter G. Kropf ◽  
Edgar F. A. Lederer ◽  
Thomas Steffen ◽  
Karl Guggisberg ◽  
Jean-Guy Schneider ◽  
...  
Keyword(s):  
Finite Element ◽  
Parallel Computing ◽  
Functional Programming ◽  
High Performance ◽  
Dynamic Models ◽  
Spinal Injury ◽  
Direct Method ◽  
Finite Element Program ◽  
Biomechanical Models ◽  
Interdisciplinary Approaches

Research in scientitic programming enables us to realize more and more complex applications, and on the other hand, application-driven demands on computing methods and power are continuously growing. Therefore, interdisciplinary approaches become more widely used. The interdisciplinary SPINET project presented in this article applies modern scientific computing tools to biomechanical simulations: parallel computing and symbolic and modern functional programming. The target application is the human spine. Simulations of the spine help us to investigate and better understand the mechanisms of back pain and spinal injury. Two approaches have been used: the first uses the finite element method for high-performance simulations of static biomechanical models, and the second generates a simulation developmenttool for experimenting with different dynamic models. A finite element program for static analysis has been parallelized for the MUSIC machine. To solve the sparse system of linear equations, a conjugate gradient solver (iterative method) and a frontal solver (direct method) have been implemented. The preprocessor required for the frontal solver is written in the modern functional programming language SML, the solver itself in C, thus exploiting the characteristic advantages of both functional and imperative programming. The speedup analysis of both solvers show very satisfactory results for this irregular problem. A mixed symbolic-numeric environment for rigid body system simulations is presented. It automatically generates C code from a problem specification expressed by the Lagrange formalism using Maple.

scholarly journals New Computing Technology in Reliability Engineering

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Radim Briš ◽  
Simona Domesová
Keyword(s):  
Parallel Computing ◽  
High Performance ◽  
Rapid Development ◽  
Computation Time ◽  
Scientific Discipline ◽  
Reliability Engineering ◽  
Computing Technology ◽  
Source Codes ◽  
Computation Efficiency ◽  
Cuda Technology

Reliability engineering is relatively new scientific discipline which develops in close connection with computers. Rapid development of computer technology recently requires adequate novelties of source codes and appropriate software. New parallel computing technology based on HPC (high performance computing) for availability calculation will be demonstrated in this paper. The technology is particularly effective in context with simulation methods; nevertheless, analytical methods are taken into account as well. In general, basic algorithms for reliability calculations must be appropriately modified and improved to achieve better computation efficiency. Parallel processing is executed by two ways, firstly by the use of the MATLAB function parfor and secondly by the use of the CUDA technology. The computation efficiency was significantly improved which is clearly demonstrated in numerical experiments performed on selected testing examples as well as on industrial example. Scalability graphs are used to demonstrate reduction of computation time caused by parallel computing.

scholarly journals High-Performance Parallel Computing with Cloud and Cloud Technologies

2010 ◽  
pp. 275-308 ◽  
Author(s):  
Jaliya Ekanayake ◽  
Xiaohong Qiu ◽  
Thilina Gunarathne ◽  
Scott Beason ◽  
Geoffrey Fox
Keyword(s):  
Parallel Computing ◽  
High Performance ◽  
Cloud Technologies

scholarly journals A parallel computational framework for ultra-large-scale sequence clustering analysis

2018 ◽  
Vol 35 (3) ◽  
pp. 380-388 ◽  
Author(s):  
Wei Zheng ◽  
Qi Mao ◽  
Robert J Genco ◽  
Jean Wactawski-Wende ◽  
Michael Buck ◽  
...  
Keyword(s):  
Parallel Computing ◽  
High Performance ◽  
Large Scale ◽  
De Novo ◽  
Rapid Development ◽  
Operational Taxonomic Unit ◽  
Supplementary Information ◽  
Computational Framework ◽  
Speed Up ◽  
Scale Sequence

Abstract Motivation The rapid development of sequencing technology has led to an explosive accumulation of genomic data. Clustering is often the first step to be performed in sequence analysis. However, existing methods scale poorly with respect to the unprecedented growth of input data size. As high-performance computing systems are becoming widely accessible, it is highly desired that a clustering method can easily scale to handle large-scale sequence datasets by leveraging the power of parallel computing. Results In this paper, we introduce SLAD (Separation via Landmark-based Active Divisive clustering), a generic computational framework that can be used to parallelize various de novo operational taxonomic unit (OTU) picking methods and comes with theoretical guarantees on both accuracy and efficiency. The proposed framework was implemented on Apache Spark, which allows for easy and efficient utilization of parallel computing resources. Experiments performed on various datasets demonstrated that SLAD can significantly speed up a number of popular de novo OTU picking methods and meanwhile maintains the same level of accuracy. In particular, the experiment on the Earth Microbiome Project dataset (∼2.2B reads, 437 GB) demonstrated the excellent scalability of the proposed method. Availability and implementation Open-source software for the proposed method is freely available at https://www.acsu.buffalo.edu/~yijunsun/lab/SLAD.html. Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document