Comparing Message Passing Interface and MapReduce for large-scale parallel ranking and selection

2015 ◽  
Author(s):  
Eric C. Ni ◽  
Dragos F. Ciocan ◽  
Shane G. Henderson ◽  
Susan R. Hunter
Keyword(s):  
Message Passing ◽  
Large Scale ◽  
Message Passing Interface ◽  
Ranking And Selection

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.

Parallelization of the Lattice Boltzmann Method in Simulating Buoyancy-Driven Convection Heat Transfer

2004 ◽  
Author(s):  
Anoosheh Niavarani-Kheirier ◽  
Masoud Darbandi ◽  
Gerry E. Schneider
Keyword(s):  
Lattice Boltzmann Method ◽  
Lattice Boltzmann ◽  
Message Passing ◽  
Large Scale ◽  
Message Passing Interface ◽  
Parallel Machines ◽  
Convection Heat Transfer ◽  
Wide Range ◽  
Buoyancy Driven Convection ◽  
Boltzmann Method

The main objective of the current work is to utilize Lattice Boltzmann Method (LBM) for simulating buoyancy-driven flow considering the hybrid thermal lattice Boltzmann equation (HTLBE). After deriving the required formulations, they are validated against a wide range of Rayleigh numbers in buoyancy-driven square cavity problem. The performance of the method is investigated on parallel machines using Message Passing Interface (MPI) library and implementing domain decomposition technique to solve problems with large order of computations. The achieved results show that the code is highly efficient to solve large scale problems with excellent speedup.

Interpretive MPI for Parallel Computing

2008 ◽  
Author(s):  
Yu-Cheng Chou ◽  
Harry H. Cheng
Keyword(s):  
Parallel Computing ◽  
Programming Languages ◽  
Message Passing ◽  
Large Scale ◽  
Message Passing Interface ◽  
Rapid Development ◽  
Web Based ◽  
Heterogeneous Platforms ◽  
C Programs ◽  
Computation Speedup

Message Passing Interface (MPI) is a standardized library specification designed for message-passing parallel programming on large-scale distributed systems. A number of MPI libraries have been implemented to allow users to develop portable programs using the scientific programming languages, Fortran, C and C++. Ch is an embeddable C/C++ interpreter that provides an interpretive environment for C/C++ based scripts and programs. Combining Ch with any MPI C/C++ library provides the functionality for rapid development of MPI C/C++ programs without compilation. In this article, the method of interfacing Ch scripts with MPI C implementations is introduced by using the MPICH2 C library as an example. The MPICH2-based Ch MPI package provides users with the ability to interpretively run MPI C program based on the MPICH2 C library. Running MPI programs through the MPICH2-based Ch MPI package across heterogeneous platforms consisting of Linux and Windows machines is illustrated. Comparisons for the bandwidth, latency, and parallel computation speedup between C MPI, Ch MPI, and MPI for Python in an Ethernet-based environment comprising identical Linux machines are presented. A Web-based example is given to demonstrate the use of Ch and MPICH2 in C based CGI scripting to facilitate the development of Web-based applications for parallel computing.

scholarly journals Analysis of the Calculation of a Plasma Sheath Using the Parallel SO-DGTD Method

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Qian Yang ◽  
Bing Wei ◽  
Linqian Li ◽  
Debiao Ge
Keyword(s):  
Discontinuous Galerkin ◽  
Cross Section ◽  
Time Domain ◽  
Message Passing ◽  
High Speed ◽  
Large Scale ◽  
Message Passing Interface ◽  
Shift Operator ◽  
Plasma Sheath ◽  
Blunt Cone

The plasma sheath is known as a popular topic of computational electromagnetics, and the plasma case is more resource-intensive than the non-plasma case. In this paper, a parallel shift-operator discontinuous Galerkin time-domain method using the MPI (Message Passing Interface) library is proposed to solve the large-scale plasma problems. To demonstrate our algorithm, a plasma sheath model of the high-speed blunt cone was established based on the results of the multiphysics software, and our algorithm was used to extract the radar cross-section (RCS) versus different incident angles of the model.

scholarly journals Model Order Reduction of Large-Scale Finite Element Systems in an MPI Parallelized Environment for Usage in Multibody Simulation

2016 ◽  
Vol 63 (4) ◽  
pp. 475-494 ◽  
Author(s):  
Thomas Volzer ◽  
Peter Eberhard
Keyword(s):  
Finite Element ◽  
Model Reduction ◽  
Message Passing ◽  
Large Scale ◽  
Message Passing Interface ◽  
Block Size ◽  
Reduction Process ◽  
Element Model ◽  
Multibody Simulation ◽  
Elastic Bodies

Abstract The use of elastic bodies within a multibody simulation became more and more important within the last years. To include the elastic bodies, described as a finite element model in multibody simulations, the dimension of the system of ordinary differential equations must be reduced by projection. For this purpose, in this work, the modal reduction method, a component mode synthesis based method and a moment-matching method are used. Due to the always increasing size of the non-reduced systems, the calculation of the projection matrix leads to a large demand of computational resources and cannot be done on usual serial computers with available memory. In this paper, the model reduction software Morembs++ is presented using a parallelization concept based on the message passing interface to satisfy the need of memory and reduce the runtime of the model reduction process. Additionally, the behaviour of the Block-Krylov-Schur eigensolver, implemented in the Anasazi package of the Trilinos project, is analysed with regard to the choice of the size of the Krylov base, the block size and the number of blocks. Besides, an iterative solver is considered within the CMS-based method.

scholarly journals Parallel Implementation of Non-slicing Floorplans with MPI and OpenMP

2021 ◽  
Author(s):  
Oluvaseun Owojaiye
Keyword(s):  
Message Passing ◽  
Large Scale ◽  
Message Passing Interface ◽  
Parallel Implementation ◽  
Computation Time ◽  
Sequential Algorithm ◽  
Design Stage ◽  
Single Chip ◽  
Solution Quality ◽  
Early Design Stage

Advancement in technology has brought considerable improvement to processor design and now manufacturers design multiple processors on a single chip. Supercomputers today consists of cluster of interconnected nodes that collaborate together to solve complex and advanced computation problems. Message Passing Interface and Open Multiprocessing are the popularly used programming models to optimize sequential codes by parallelizing them on the different multiprocessor architecture that exist today. In this thesis, we parallelize the non-slicing floorplan algorithm based on Multilevel Floorplanning/placement of large scale modules using B*tree (MB*tree) with MPI and OpenMP on distributed and shared memory architectures respectively. In VLSI (Very Large Scale Integration) design automation, floorplanning is an initial and vital task performed in the early design stage. Experimental results using MCNC benchmark circuits show that our parallel algorithm produced better results than the corresponding sequential algorithm; we were able to speed up the algorithm up to 4 times, hence reducing computation time and maintaining floorplan solution quality. On the other hand, we compared both parallel versions; and the OpenMP results gave slightly better than the corresponding MPI results.

scholarly journals A Simulator for Large-Scale Parallel Computer Architectures

2010 ◽  
Vol 1 (2) ◽  
pp. 57-73 ◽  
Author(s):  
Curtis L. Janssen ◽  
Helgi Adalsteinsson ◽  
Scott Cranford ◽  
Joseph P. Kenny ◽  
Ali Pinar ◽  
...  
Keyword(s):  
Message Passing ◽  
Large Scale ◽  
Message Passing Interface ◽  
Parallel Execution ◽  
Parallel Computer ◽  
Coarse Grained ◽  
Efficient Design ◽  
Standard Format ◽  
Trace File ◽  
Macro Scale

Efficient design of hardware and software for large-scale parallel execution requires detailed understanding of the interactions between the application, computer, and network. The authors have developed a macro-scale simulator (SST/macro) that permits the coarse-grained study of distributed-memory applications. In the presented work, applications using the Message Passing Interface (MPI) are simulated; however, the simulator is designed to allow inclusion of other programming models. The simulator is driven from either a trace file or a skeleton application. Trace files can be either a standard format (Open Trace Format) or a more detailed custom format (DUMPI). The simulator architecture is modular, allowing it to easily be extended with additional network models, trace file formats, and more detailed processor models. This paper describes the design of the simulator, provides performance results, and presents studies showing how application performance is affected by machine characteristics.

scholarly journals A GPU-Based Gibbs Sampler for a Unidimensional IRT Model

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Yanyan Sheng ◽  
William S. Welling ◽  
Michelle M. Zhu
Keyword(s):  
Message Passing ◽  
Large Scale ◽  
Message Passing Interface ◽  
Cost Effective ◽  
Communication Overhead ◽  
Graphic Processing Units ◽  
Data Dependencies ◽  
High Data ◽  
Irt Models ◽  
Fully Bayesian

Item response theory (IRT) is a popular approach used for addressing large-scale statistical problems in psychometrics as well as in other fields. The fully Bayesian approach for estimating IRT models is usually memory and computationally expensive due to the large number of iterations. This limits the use of the procedure in many applications. In an effort to overcome such restraint, previous studies focused on utilizing the message passing interface (MPI) in a distributed memory-based Linux cluster to achieve certain speedups. However, given the high data dependencies in a single Markov chain for IRT models, the communication overhead rapidly grows as the number of cluster nodes increases. This makes it difficult to further improve the performance under such a parallel framework. This study aims to tackle the problem using massive core-based graphic processing units (GPU), which is practical, cost-effective, and convenient in actual applications. The performance comparisons among serial CPU, MPI, and compute unified device architecture (CUDA) programs demonstrate that the CUDA GPU approach has many advantages over the CPU-based approach and therefore is preferred.

Scaling modeling and simulation on high-performance computing clusters

SIMULATION ◽  
2019 ◽  
Vol 96 (2) ◽  
pp. 221-232
Author(s):  
Mike Mikailov ◽  
Junshan Qiu ◽  
Fu-Jyh Luo ◽  
Stephen Whitney ◽  
Nicholas Petrick
Keyword(s):  
Modeling And Simulation ◽  
High Performance Computing ◽  
Message Passing ◽  
High Performance ◽  
Large Scale ◽  
Message Passing Interface ◽  
The United States ◽  
United States Food ◽  
States Food ◽  
Performance Computing

Large-scale modeling and simulation (M&S) applications that do not require run-time inter-process communications can exhibit scaling problems when migrated to high-performance computing (HPC) clusters if traditional software parallelization techniques, such as POSIX multi-threading and the message passing interface, are used. A comprehensive approach for scaling M&S applications on HPC clusters has been developed and is called “computation segmentation.” The computation segmentation is based on the built-in array job facility of job schedulers. If used correctly for appropriate applications, the array job approach provides significant benefits that are not obtainable using other methods. The parallelization illustrated in this paper becomes quite complex in its own right when applied to extremely large M&S tasks, particularly due to the need for nested loops. At the United States Food and Drug Administration, the approach has provided unsurpassed efficiency, flexibility, and scalability for work that can be performed using embarrassingly parallel algorithms.

NoisePy: A New High-Performance Python Tool for Ambient-Noise Seismology

2020 ◽  
Vol 91 (3) ◽  
pp. 1853-1866 ◽  
Author(s):  
Chengxin Jiang ◽  
Marine A. Denolle
Keyword(s):  
Seismic Data ◽  
Message Passing ◽  
High Performance ◽  
Ambient Noise ◽  
Large Scale ◽  
Message Passing Interface ◽  
Dispersion Analysis ◽  
High Temporal Resolution ◽  
Data Format ◽  
The Cross

Abstract The fast-growing interests in high spatial resolution of seismic imaging and high temporal resolution of seismic monitoring pose great challenges for fast, efficient, and stable data processing in ambient-noise seismology. This coincides with the explosion of available seismic data in the last few years. However, the current computational landscape of ambient seismic field seismology remains highly heterogeneous, with individual researchers building their own homegrown codes. Here, we present NoisePy—a new high-performance python tool designed specifically for large-scale ambient-noise seismology. NoisePy provides most of the processing techniques for the ambient field data and the correlations found in the literature, along with parallel download routines, dispersion analysis, and monitoring functions. NoisePy takes advantage of adaptable seismic data format, a parallel input and output enabled HDF5 data format designed for seismology, for a structured organization of the cross-correlation data. The parallel computing of NoisePy is performed using Message Passing Interface and shows a strong scaling with the number of cores, which is well suited for embarrassingly parallel problems. NoisePy also uses a small memory overhead and stable memory usage. Benchmark comparisons with the latest version of MSNoise demonstrate about four-time improvement in compute time of the cross correlations, which is the slowest step of ambient-noise seismology. NoisePy is suitable for ambient-noise seismology of various data sizes, and it has been tested successfully at handling data of size ranging from a few GBs to several tens of TBs.

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