scholarly journals High performance computing for linear acoustic wave simulation

2021 ◽  
Author(s):  
Fouad Butt
Keyword(s):  
Parallel Computing ◽  
Acoustic Wave ◽  
High Performance ◽  
Three Dimensional ◽  
Wave Model ◽  
Acoustic Fields ◽  
Performance Simulation ◽  
Temporal Complexity ◽  
Spatio Temporal ◽  
Performance Computing

Parallel computing techniques are applied to a linear acoustic wave model to reduce execution time. Three parallel computing models are developed to parallelize computations. The fork-and-join, SPMD and SIMT models define the execution of parallel computations. The precision and efficiency of the linear acoustic wave model are improved through substantial speedups in all implementations. Furthermore, axisymmetric properties of certain acoustic fields lead to a reduction in the spatio-temporal complexity of those acoustic fields by removing redundant computations. The same linear acoustic wave model is also modified and extended to describe wave propagation across multiple media instead of only a single medium. The developed implementations are integrated into a particularly useful package for high performance simulation of two- or three-dimensional linear acoustic elds generated by realistic sources in various fluid media.

scholarly journals High performance computing for linear acoustic wave simulation

2021 ◽  
Author(s):  
Fouad Butt
Keyword(s):  
Parallel Computing ◽  
Acoustic Wave ◽  
High Performance ◽  
Three Dimensional ◽  
Wave Model ◽  
Acoustic Fields ◽  
Performance Simulation ◽  
Temporal Complexity ◽  
Spatio Temporal ◽  
Performance Computing

Parallel computing techniques are applied to a linear acoustic wave model to reduce execution time. Three parallel computing models are developed to parallelize computations. The fork-and-join, SPMD and SIMT models define the execution of parallel computations. The precision and efficiency of the linear acoustic wave model are improved through substantial speedups in all implementations. Furthermore, axisymmetric properties of certain acoustic fields lead to a reduction in the spatio-temporal complexity of those acoustic fields by removing redundant computations. The same linear acoustic wave model is also modified and extended to describe wave propagation across multiple media instead of only a single medium. The developed implementations are integrated into a particularly useful package for high performance simulation of two- or three-dimensional linear acoustic elds generated by realistic sources in various fluid media.

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.

Large Scale Mosaic Using Parallel Computing for Remote Sensed Images

2014 ◽  
Vol 556-562 ◽  
pp. 4746-4749
Author(s):  
Bin Chu ◽  
Da Lin Jiang ◽  
Bo Cheng
Keyword(s):  
Parallel Computing ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Computing System ◽  
Physical Relationship ◽  
High Performance Computing System ◽  
Performance Computing

This paper concerns about Large-scale mosaic for remote sensed images. Base on High Performance Computing system, we offer a method to decompose the problem and integrate them with logical and physical relationship. The mosaic of Large-scale remote sensed images has been improved both at performance and effectiveness.

A look back on 30 years of the Gordon Bell Prize

2017 ◽  
Vol 31 (6) ◽  
pp. 469-484 ◽  
Author(s):  
Gordon Bell ◽  
David H Bailey ◽  
Jack Dongarra ◽  
Alan H Karp ◽  
Kevin Walsh
Keyword(s):  
Parallel Computing ◽  
High Performance ◽  
Large Scale ◽  
Peak Performance ◽  
Outstanding Achievement ◽  
Computing Machinery ◽  
Large Scale Data ◽  
The Us ◽  
The Impact ◽  
Performance Computing

The Gordon Bell Prize is awarded each year by the Association for Computing Machinery to recognize outstanding achievement in high-performance computing (HPC). The purpose of the award is to track the progress of parallel computing with particular emphasis on rewarding innovation in applying HPC to applications in science, engineering, and large-scale data analytics. Prizes may be awarded for peak performance or special achievements in scalability and time-to-solution on important science and engineering problems. Financial support for the US$10,000 award is provided through an endowment by Gordon Bell, a pioneer in high-performance and parallel computing. This article examines the evolution of the Gordon Bell Prize and the impact it has had on the field.

scholarly journals Computational experiment in era of HPC

2019 ◽  
Vol 27 (3) ◽  
pp. 263-267
Author(s):  
Alexander S. Ayriyan
Keyword(s):  
Parallel Computing ◽  
Life Cycle ◽  
High Performance Computing ◽  
High Performance ◽  
Computational Experiment ◽  
Computing Systems ◽  
The Impact ◽  
Performance Computing

In this note we discuss the impact of development of architecture and technology of parallel computing on the typical life-cycle of the computational experiment. In particular, it is argued that development and installation of high-performance computing systems is indeed important itself regardless of specific scientific tasks, since the presence of cutting-age HPC systems within an academic infrastructure gives wide possibilities and stimulates new researches.

scholarly journals Computational experiment in era of HPC

2019 ◽  
Vol 27 (3) ◽  
pp. 263-267
Author(s):  
Alexander S. Ayriyan
Keyword(s):  
Parallel Computing ◽  
Life Cycle ◽  
High Performance Computing ◽  
High Performance ◽  
Computational Experiment ◽  
Computing Systems ◽  
The Impact ◽  
Performance Computing

In this note we discuss the impact of development of architecture and technology of parallel computing on the typical life-cycle of the computational experiment. In particular, it is argued that development and installation of high-performance computing systems is indeed important itself regardless of specific scientific tasks, since the presence of cutting-age HPC systems within an academic infrastructure gives wide possibilities and stimulates new researches.

Improving in situ GPU simulation of quantum computing in the D-GM environment

2019 ◽  
Vol 33 (3) ◽  
pp. 462-472
Author(s):  
Anderson Avila ◽  
Renata Hax Sander Reiser ◽  
Maurício Lima Pilla ◽  
Adenauer Correa Yamin
Keyword(s):  
Quantum Computing ◽  
High Performance Computing ◽  
High Performance ◽  
Temporal Complexity ◽  
Exponential Increase ◽  
Memory States ◽  
Global Access ◽  
Performance Computing ◽  
Gpu Implementation

Exponential increase and global access to read/write memory states in quantum computing (QC) simulation limit both the number of qubits and quantum transformations which can be currently simulated. Although QC simulation is parallel by nature, spatial and temporal complexity are major performance hazards, making this a nontrivial application for high performance computing. A new methodology employing reduction and decomposition optimizations has shown relevant results, but its GPU implementation could be further improved. In this work, we develop a new kernel for in situ GPU simulation that better explores its resources without requiring further hardware. Shor’s and Grover’s algorithms are simulated up to 25 and 21 qubits respectively and compared to our previous version, to [Formula: see text] simulator and to ProjectQ framework, showing better results with relative speedups up to 4.38×, 3357.76× and 333× respectively.

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