Comparative study of data-parallel and message-passing technologies on distributed-memory parallel computers

1995 ◽  
Author(s):  
Michel Gazaix
Keyword(s):  
Comparative Study ◽  
Message Passing ◽  
Distributed Memory ◽  
Parallel Computers ◽  
Data Parallel

scholarly journals Teaching tools for parallel processing

2005 ◽  
Vol 18 (2) ◽  
pp. 219-224
Author(s):  
Emina Milovanovic ◽  
Natalija Stojanovic
Keyword(s):  
Parallel Computing ◽  
Parallel Processing ◽  
Shared Memory ◽  
Message Passing ◽  
Distributed Memory ◽  
Cost Effective ◽  
Parallel Computers ◽  
Free Software ◽  
Teaching Tools ◽  
Network Of Workstations

Because many universities lack the funds to purchase expensive parallel computers, cost effective alternatives are needed to teach students about parallel processing. Free software is available to support the three major paradigms of parallel computing. Parallaxis is a sophisticated SIMD simulator which runs on a variety of platforms.jBACI shared memory simulator supports the MIMD model of computing with a common shared memory. PVM and MPI allow students to treat a network of workstations as a message passing MIMD multicomputer with distributed memory. Each of this software tools can be used in a variety of courses to give students experience with parallel algorithms.

scholarly journals Practical Wavelet Tree Construction

2021 ◽  
Vol 26 ◽  
pp. 1-67
Author(s):  
Patrick Dinklage ◽  
Jonas Ellert ◽  
Johannes Fischer ◽  
Florian Kurpicz ◽  
Marvin Löbel
Keyword(s):  
Parallel Algorithms ◽  
Shared Memory ◽  
Distributed Memory ◽  
Auxiliary Information ◽  
Parallel Computers ◽  
External Memory ◽  
Sequential Algorithms ◽  
Bottom Up ◽  
Memory Efficiency ◽  
Tree Construction

We present new sequential and parallel algorithms for wavelet tree construction based on a new bottom-up technique. This technique makes use of the structure of the wavelet trees—refining the characters represented in a node of the tree with increasing depth—in an opposite way, by first computing the leaves (most refined), and then propagating this information upwards to the root of the tree. We first describe new sequential algorithms, both in RAM and external memory. Based on these results, we adapt these algorithms to parallel computers, where we address both shared memory and distributed memory settings. In practice, all our algorithms outperform previous ones in both time and memory efficiency, because we can compute all auxiliary information solely based on the information we obtained from computing the leaves. Most of our algorithms are also adapted to the wavelet matrix , a variant that is particularly suited for large alphabets.

scholarly journals Studies of Electron-Molecule Collisions on Distributed-memory Parallel Computers

1992 ◽  
Vol 45 (3) ◽  
pp. 325 ◽  
Author(s):  
Carl Winstead ◽  
Qiyan Sun ◽  
Paul G Hipes ◽  
Marco AP Lima ◽  
Vincent McKoy
Keyword(s):  
Distributed Memory ◽  
Recent Progress ◽  
Cost Effective ◽  
Parallel Computers ◽  
Polyatomic Molecules ◽  
Low Energy ◽  
Materials Processing ◽  
Challenging Problem ◽  
Molecular Systems ◽  
Critical Data

We review recent progress in the study of low-energy collisions between electrons and polyatomic molecules which has resulted from the application of distributed-memory parallel computing to this challenging problem. Recent studies of electronically elastic and inelastic scattering from several molecular systems, including ethene, propene, cyclopropane, and disilane, are presented. We also discuss the potential of ab initio methods combined with cost-effective parallel computation to provide critical data for the modeling of materials-processing plasmas.

scholarly journals Parallel Object-Oriented Computation Applied to a Finite Element Problem

1993 ◽  
Vol 2 (4) ◽  
pp. 133-144 ◽  
Author(s):  
Jon B. Weissman ◽  
Andrew S. Grimshaw ◽  
R.D. Ferraro
Keyword(s):  
Finite Element ◽  
Message Passing ◽  
Large Scale ◽  
Processing System ◽  
Object Oriented ◽  
Data Parallel ◽  
Programming Tools ◽  
Comparable Performance ◽  
Oriented Parallel ◽  
Performance Results

The conventional wisdom in the scientific computing community is that the best way to solve large-scale numerically intensive scientific problems on today's parallel MIMD computers is to use Fortran or C programmed in a data-parallel style using low-level message-passing primitives. This approach inevitably leads to nonportable codes and extensive development time, and restricts parallel programming to the domain of the expert programmer. We believe that these problems are not inherent to parallel computing but are the result of the programming tools used. We will show that comparable performance can be achieved with little effort if better tools that present higher level abstractions are used. The vehicle for our demonstration is a 2D electromagnetic finite element scattering code we have implemented in Mentat, an object-oriented parallel processing system. We briefly describe the application. Mentat, the implementation, and present performance results for both a Mentat and a hand-coded parallel Fortran version.

Sign in / Sign up

Export Citation Format

Share Document