scholarly journals HYDROFLASH: A 2-D Nuclear EMP Code Founded on Finite Volume Techniques

2017 ◽  
Vol 6 (2) ◽  
pp. 14 ◽  
Author(s):  
R. Roussel-Dupre
Keyword(s):  
Finite Volume ◽  
Message Passing ◽  
Maxwell Equations ◽  
Message Passing Interface ◽  
Hyperbolic Equations ◽  
Source Region ◽  
Multiprocessor Systems ◽  
Near Surface ◽  
First Approximation ◽  
Hydrodynamic Code

The basic mechanisms that govern the generation of an electromagnetic pulse (EMP) following a nuclear detonation in the atmosphere, including heights of burst (HOB) relevant to surface bursts (0 km), near surface bursts (0-2 km), air bursts (2-20 km) and high-altitude bursts (> 20 km), are reviewed. Previous computational codes developed to treat the source region and predict the EMP are discussed. A new 2-D hydrodynamic code (HYDROFLASH) that solves the fluid equations for electron and ion transport in the atmosphere and the coupled Maxwell equations using algorithms extracted from the Conservation Law (CLAW) package for solving multi-dimensional hyperbolic equations with finite volume techniques has been formulated. Simulations include the ground, atmospheric gradient, and an azimuthal applied magnetic field as a first approximation to the geomagnetic field. HYDROFLASH takes advantage of multiprocessor systems by using domain decomposition together with the Message Passing Interface (MPI) protocol for parallel processing. A detailed description of the model is presented along with computational results for a generic 10 kiloton (kT) burst detonated at 0 and 10 km altitude.

Evaluation of a Parallel Agglomeration Multigrid Finite-Volume Algorithm, Named Galatea-I, for the Simulation of Incompressible Flows on 3D Hybrid Unstructured Grids

2014 ◽  
Author(s):  
Sotirios S. Sarakinos ◽  
Georgios N. Lygidakis ◽  
Ioannis K. Nikolos
Keyword(s):  
Finite Volume ◽  
Message Passing ◽  
Data Exchange ◽  
Message Passing Interface ◽  
Incompressible Flows ◽  
Navier Stokes ◽  
Finite Volume Scheme ◽  
Computational Domain ◽  
Naca0012 Airfoil ◽  
Computational Performance

In this study an academic Computational Fluid Dynamics (CFD) code, named Galatea-I, is described, which employs the Reynolds Averaged Navier–Stokes (RANS) equations along with the artificial compressibility method and the SST (Shear Stress Transport) turbulence model for the prediction of incompressible viscous flows. For the representation of the computational domain unstructured hybrid grids are utilized, composed of tetrahedral, prismatic and pyramidical elements, while for its discretization a node-centered finite-volume scheme is implemented. Galatea-I is enhanced with a parallelization method, which employs spatial domain decomposition, while the data exchange between processors/processes is performed with the use of the Message Passing Interface (MPI) protocol. In addition, a parallel agglomeration multigrid methodology has been incorporated to improve further its computational performance. The proposed code is validated against steady-state flow benchmark test cases, concerning laminar flow over a cubic cavity and a cylindrical surface, as well as turbulent flow over a rectangular wing with a NACA0012 airfoil. The obtained results, compared with these of corresponding reference solvers, reveal Galatea-I’s potential for simulation of inviscid, viscous laminar and turbulent incompressible flows.

Multi-level Parallelization of Genotype Imputation on Supercomputers

2020 ◽  
Vol 15 ◽  
Author(s):  
Weiwen Zhang ◽  
Long Wang ◽  
Theint Theint Aye ◽  
Juniarto Samsudin ◽  
Yongqing Zhu
Keyword(s):  
Association Study ◽  
Message Passing ◽  
High Performance ◽  
Message Passing Interface ◽  
Genome Wide Association Study ◽  
Job Scheduling ◽  
Genotype Imputation ◽  
Job Level ◽  
Multi Level ◽  
High Performance Requirement

Background: Genotype imputation as a service is developed to enable researchers to estimate genotypes on haplotyped data without performing whole genome sequencing. However, genotype imputation is computation intensive and thus it remains a challenge to satisfy the high performance requirement of genome wide association study (GWAS). Objective: In this paper, we propose a high performance computing solution for genotype imputation on supercomputers to enhance its execution performance. Method: We design and implement a multi-level parallelization that includes job level, process level and thread level parallelization, enabled by job scheduling management, message passing interface (MPI) and OpenMP, respectively. It involves job distribution, chunk partition and execution, parallelized iteration for imputation and data concatenation. Due to the design of multi-level parallelization, we can exploit the multi-machine/multi-core architecture to improve the performance of genotype imputation. Results: Experiment results show that our proposed method can outperform the Hadoop-based implementation of genotype imputation. Moreover, we conduct the experiments on supercomputers to evaluate the performance of the proposed method. The evaluation shows that it can significantly shorten the execution time, thus improving the performance for genotype imputation. Conclusion: The proposed multi-level parallelization, when deployed as an imputation as a service, will facilitate bioinformatics researchers in Singapore to conduct genotype imputation and enhance the association study.

scholarly journals Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2284
Author(s):  
Krzysztof Przystupa ◽  
Mykola Beshley ◽  
Olena Hordiichuk-Bublivska ◽  
Marian Kyryk ◽  
Halyna Beshley ◽  
...  
Keyword(s):  
Distributed Systems ◽  
Singular Value Decomposition ◽  
Data Processing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Recommendation Systems ◽  
Singular Value ◽  
Singular Value Decomposition Method ◽  
Value Decomposition ◽  
Svd Method

The problem of analyzing a big amount of user data to determine their preferences and, based on these data, to provide recommendations on new products is important. Depending on the correctness and timeliness of the recommendations, significant profits or losses can be obtained. The task of analyzing data on users of services of companies is carried out in special recommendation systems. However, with a large number of users, the data for processing become very big, which causes complexity in the work of recommendation systems. For efficient data analysis in commercial systems, the Singular Value Decomposition (SVD) method can perform intelligent analysis of information. With a large amount of processed information we proposed to use distributed systems. This approach allows reducing time of data processing and recommendations to users. For the experimental study, we implemented the distributed SVD method using Message Passing Interface, Hadoop and Spark technologies and obtained the results of reducing the time of data processing when using distributed systems compared to non-distributed ones.

A high-performance, portable implementation of the MPI message passing interface standard

1996 ◽  
Vol 22 (6) ◽  
pp. 789-828 ◽  
Author(s):  
William Gropp ◽  
Ewing Lusk ◽  
Nathan Doss ◽  
Anthony Skjellum
Keyword(s):  
Message Passing ◽  
High Performance ◽  
Message Passing Interface

Based on Numerical Simulation of High-Performance Parallel Machine Muffler Experimental Calibration

2013 ◽  
Vol 718-720 ◽  
pp. 1645-1650
Author(s):  
Gen Yin Cheng ◽  
Sheng Chen Yu ◽  
Zhi Yong Wei ◽  
Shao Jie Chen ◽  
You Cheng
Keyword(s):  
Numerical Simulation ◽  
Finite Element ◽  
Boundary Element ◽  
Message Passing ◽  
High Performance ◽  
Message Passing Interface ◽  
Parallel Machine ◽  
Simulation Software ◽  
Experimental Calibration ◽  
The Cost

Commonly used commercial simulation software SYSNOISE and ANSYS is run on a single machine (can not directly run on parallel machine) when use the finite element and boundary element to simulate muffler effect, and it will take more than ten days, sometimes even twenty days to work out an exact solution as the large amount of numerical simulation. Use a high performance parallel machine which was built by 32 commercial computers and transform the finite element and boundary element simulation software into a program that can running under the MPI (message passing interface) parallel environment in order to reduce the cost of numerical simulation. The relevant data worked out from the simulation experiment demonstrate that the result effect of the numerical simulation is well. And the computing speed of the high performance parallel machine is 25 ~ 30 times a microcomputer.

scholarly journals A lightweight approach to performance portability with targetDP

2016 ◽  
Vol 32 (2) ◽  
pp. 288-301
Author(s):  
Alan Gray ◽  
Kevin Stratford
Keyword(s):  
Particle Physics ◽  
Message Passing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Large Scale ◽  
Message Passing Interface ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Performance Portability ◽  
Graphics Processing

Leading high performance computing systems achieve their status through use of highly parallel devices such as NVIDIA graphics processing units or Intel Xeon Phi many-core CPUs. The concept of performance portability across such architectures, as well as traditional CPUs, is vital for the application programmer. In this paper we describe targetDP, a lightweight abstraction layer which allows grid-based applications to target data parallel hardware in a platform agnostic manner. We demonstrate the effectiveness of our pragmatic approach by presenting performance results for a complex fluid application (with which the model was co-designed), plus separate lattice quantum chromodynamics particle physics code. For each application, a single source code base is seen to achieve portable performance, as assessed within the context of the Roofline model. TargetDP can be combined with Message Passing Interface (MPI) to allow use on systems containing multiple nodes: we demonstrate this through provision of scaling results on traditional and graphics processing unit-accelerated large scale supercomputers.

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