scholarly journals Language Constructs for Data Partitioning and Distribution

1995 ◽  
Vol 4 (2) ◽  
pp. 59-85 ◽  
Author(s):  
P. Crooks ◽  
R. H. Perrott
Keyword(s):  
Shared Memory ◽  
Distributed Memory ◽  
Data Partitioning ◽  
Interactive Methods ◽  
Multiprocessor Systems ◽  
Programming Paradigm ◽  
Target Architecture ◽  
Explicit Processes ◽  
Language Constructs ◽  
Communication Programs

This article presents a survey of language features for distributed memory multiprocessor systems (DMMs), in particular, systems that provide features for data partitioning and distribution. In these systems the programmer is freed from consideration of the low-level details of the target architecture in that there is no need to program explicit processes or specify interprocess communication. Programs are written according to the shared memory programming paradigm but the programmer is required to specify, by means of directives, additional syntax or interactive methods, how the data of the program are decomposed and distributed.

scholarly journals Programming in Vienna Fortran

1992 ◽  
Vol 1 (1) ◽  
pp. 31-50 ◽  
Author(s):  
Barbara Chapman ◽  
Piyush Mehrotra ◽  
Hans Zima
Keyword(s):  
Shared Memory ◽  
Data Structures ◽  
Distributed Memory ◽  
Data Distribution ◽  
Performance Potential ◽  
Distributed Memory Machines ◽  
Programming Paradigm ◽  
Language Extension ◽  
Wide Range ◽  
Global Data

Exploiting the full performance potential of distributed memory machines requires a careful distribution of data across the processors. Vienna Fortran is a language extension of Fortran which provides the user with a wide range of facilities for such mapping of data structures. In contrast to current programming practice, programs in Vienna Fortran are written using global data references. Thus, the user has the advantages of a shared memory programming paradigm while explicitly controlling the data distribution. In this paper, we present the language features of Vienna Fortran for FORTRAN 77, together with examples illustrating the use of these features.

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.

scholarly journals An O(log2N) Fully-Balanced Resampling Algorithm for Particle Filters on Distributed Memory Architectures

Algorithms ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 342
Author(s):  
Alessandro Varsi ◽  
Simon Maskell ◽  
Paul G. Spirakis
Keyword(s):  
Parallel Computing ◽  
Shared Memory ◽  
Time Complexity ◽  
Distributed Memory ◽  
Particle Filters ◽  
Dynamic Models ◽  
State Of The Art ◽  
Novel Approach ◽  
Non Gaussian ◽  
Memory Architectures

Resampling is a well-known statistical algorithm that is commonly applied in the context of Particle Filters (PFs) in order to perform state estimation for non-linear non-Gaussian dynamic models. As the models become more complex and accurate, the run-time of PF applications becomes increasingly slow. Parallel computing can help to address this. However, resampling (and, hence, PFs as well) necessarily involves a bottleneck, the redistribution step, which is notoriously challenging to parallelize if using textbook parallel computing techniques. A state-of-the-art redistribution takes O((log2N)2) computations on Distributed Memory (DM) architectures, which most supercomputers adopt, whereas redistribution can be performed in O(log2N) on Shared Memory (SM) architectures, such as GPU or mainstream CPUs. In this paper, we propose a novel parallel redistribution for DM that achieves an O(log2N) time complexity. We also present empirical results that indicate that our novel approach outperforms the O((log2N)2) approach.

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