scholarly journals The Efficiency of Linda for General Purpose Scientific Programming

1994 ◽  
Vol 3 (1) ◽  
pp. 61-71 ◽  
Author(s):  
Timothy G. Mattson
Keyword(s):  
Shared Memory ◽  
Programming Language ◽  
Programming Model ◽  
Parallel Computers ◽  
General Purpose ◽  
Registered Trademark ◽  
Multiple Data ◽  
Virtual Shared Memory ◽  
Hybrid Language ◽  
Complete Set

Linda (Linda is a registered trademark of Scientific Computing Associates, Inc.) is a programming language for coordinating the execution and interaction of processes. When combined with a language for computation (such as C or Fortran), the resulting hybrid language can be used to write portable programs for parallel and distributed multiple instruction multiple data (MIMD) computers. The Linda programming model is based on operations that read, write, and erase a virtual shared memory. It is easy to use, and lets the programmer code in a very expressive, uncoupled programming style. These benefits, however, are of little value unless Linda programs execute efficiently. The goal of this article is to demonstrate that Linda programs are efficient making Linda an effective general purpose tool for programming MIMD parallel computers. Two arguments for Linda's efficiency are given; the first is based on Linda's implementation and the second on a range of case studies spanning a complete set of parallel algorithm classes.

scholarly journals The Semi-Automatic Parallelisation of Scientific Application Codes Using a Computer Aided Parallelisation Toolkit

2001 ◽  
Vol 9 (2-3) ◽  
pp. 163-173 ◽  
Author(s):  
C.S. Ierotheou ◽  
S.P. Johnson ◽  
P.F. Leggett ◽  
M. Cross ◽  
E.W. Evans ◽  
...  
Keyword(s):  
Shared Memory ◽  
Message Passing ◽  
Programming Model ◽  
Parallel Computers ◽  
Parallel Programs ◽  
Scientific Application ◽  
Real World Application ◽  
Interprocedural Analysis ◽  
Computer Aided ◽  
Application Codes

The shared-memory programming model can be an effective way to achieve parallelism on shared memory parallel computers. Historically however, the lack of a programming standard using directives and the limited scalability have affected its take-up. Recent advances in hardware and software technologies have resulted in improvements to both the performance of parallel programs with compiler directives and the issue of portability with the introduction of OpenMP. In this study, the Computer Aided Parallelisation Toolkit has been extended to automatically generate OpenMP-based parallel programs with nominal user assistance. We categorize the different loop types and show how efficient directives can be placed using the toolkit's in-depth interprocedural analysis. Examples are taken from the NAS parallel benchmarks and a number of real-world application codes. This demonstrates the great potential of using the toolkit to quickly parallelise serial programs as well as the good performance achievable on up to 300 processors for hybrid message passing-directive parallelisations.

MIMD, Multiple Instruction, Multiple Data

2004 ◽  
Author(s):  
Wesley Petersen ◽  
Peter Arbenz
Keyword(s):  
Shared Memory ◽  
Message Passing ◽  
Distributed Memory ◽  
Programming Model ◽  
Data Access ◽  
File Server ◽  
Distributed Memory Machines ◽  
Shared Data ◽  
Multiple Data ◽  
Common Memory

The Multiple instruction, multiple data (MIMD) programming model usually refers to computing on distributed memory machines with multiple independent processors. Although processors may run independent instruction streams, we are interested in streams that are always portions of a single program. Between processors which share a coherent memory view (within a node), data access is immediate, whereas between nodes data access is effected by message passing. In this book, we use MPI for such message passing. MPI has emerged as a more/less standard message passing system used on both shared memory and distributed memory machines. It is often the case that although the system consists of multiple independent instruction streams, the programming model is not too different from SIMD. Namely, the totality of a program is logically split into many independent tasks each processed by a group (see Appendix D) of processes—but the overall program is effectively single threaded at the beginning, and likewise at the end. The MIMD model, however, is extremely flexible in that no one process is always master and the other processes slaves. A communicator group of processes performs certain tasks, usually with an arbitrary master/slave relationship. One process may be assigned to be master (or root) and coordinates the tasks of others in the group. We emphasize that the assignments of which is root is arbitrary—any processor may be chosen. Frequently, however, this choice is one of convenience—a file server node, for example. Processors and memory are connected by a network, for example, Figure 5.1. In this form, each processor has its own local memory. This is not always the case: The Cray X1, and NEC SX-6 through SX-8 series machines, have common memory within nodes. Within a node, memory coherency is maintained within local caches. Between nodes, it remains the programmer’s responsibility to assure a proper read–update relationship in the shared data. Data updated by one set of processes should not be clobbered by another set until the data are properly used.

scholarly journals The Parallel C Preprocessor

1992 ◽  
Vol 1 (1) ◽  
pp. 79-89 ◽  
Author(s):  
Eugene D. Brooks III ◽  
Brent C. Gorda ◽  
Karen H. Warren
Keyword(s):  
Shared Memory ◽  
Programming Language ◽  
Programming Model ◽  
Parallel Machine ◽  
Fixed Number ◽  
Massively Parallel ◽  
Efficient Mechanism ◽  
C Programming Language ◽  
C Programming ◽  
C Preprocessor

We describe a parallel extension of the C programming language designed for multiprocessors that provide a facility for sharing memory between processors. The programming model was initially developed on conventional shared memory machines with small processor counts such as the Sequent Balance and Alliant FX/8, but has more recently been used on a scalable massively parallel machine, the BBN TC2000. The programming model issplit-joinrather thanfork-join. Concurrency is exploited to use a fixed number of processors more efficiently rather than to exploit more processors as in the fork-join model. Team splitting, a mechanism to split the team of processors executing a code into subteams to handle parallel subtasks, is used to provide an efficient mechanism to exploit nested concurrency. We have found the split-join programming model to have an inherent implementation advantage, compared to the fork-join model, when the number of processors in a machine becomes large.

Real-Time Simulation of Multibody Dynamics on Shared Memory Multiprocessors

1993 ◽  
Vol 115 (4) ◽  
pp. 627-637 ◽  
Author(s):  
Shu Chung ◽  
Edward J. Haug
Keyword(s):  
Real Time ◽  
Shared Memory ◽  
Multibody Systems ◽  
Variational Formulation ◽  
Parallel Computers ◽  
General Purpose ◽  
Ground Vehicles ◽  
Real Time Simulation ◽  
Static Scheduling ◽  
Time Simulation

This paper presents a recursive variational formulation for real-time simulation of multibody mechanical systems on shared memory parallel computers. Static scheduling algorithms are employed to evenly distribute computation on shared memory multi-processors. Based on the methods developed, a general-purpose dynamic simulation program is shown to simulate multibody systems faster than real-time, enabling operator-in-the-loop simulation of ground vehicles and robots.

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 Parallel programming technologies on computer complexes

2020 ◽  
Vol 30 (3) ◽  
pp. 28-33 ◽  
Author(s):  
S. A. Pryadko ◽  
A. Yu. Troshin ◽  
V. D. Kozlov ◽  
A. E. Ivanov
Keyword(s):  
Parallel Programming ◽  
Shared Memory ◽  
Programming Language ◽  
Operating Systems ◽  
Distributed Memory ◽  
Programming Models ◽  
Writing Programs ◽  
Advantages And Disadvantages ◽  
C Programming Language ◽  
C Programming

The article describes various options for speeding up calculations on computer systems. These features are closely related to the architecture of these complexes. The objective of this paper is to provide necessary information when selecting the capability for the speeding process of solving the computation problem. The main features implemented using the following models are described: programming in systems with shared memory, programming in systems with distributed memory, and programming on graphics accelerators (video cards). The basic concept, principles, advantages, and disadvantages of each of the considered programming models are described. All standards for writing programs described in the article can be used both on Linux and Windows operating systems. The required libraries are available and compatible with the C/C++ programming language. The article concludes with recommendations on the use of a particular technology, depending on the type of task to be solved.

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.

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