scholarly journals CRITICAL PATH SCHEDULING PARALLEL PROGRAMS ON AN UNBOUNDED NUMBER OF PROCESSORS

2006 ◽  
Vol 17 (02) ◽  
pp. 287-301 ◽  
Author(s):  
MOURAD HAKEM ◽  
FRANCK BUTELLE
Keyword(s):  
Computational Complexity ◽  
Parallel Processing ◽  
Efficient Algorithm ◽  
Time Complexity ◽  
Distributed Memory ◽  
Critical Path ◽  
Parallel Programs ◽  
Solution Quality ◽  
Time Scheduling ◽  
Path Scheduling

In this paper we present an efficient algorithm for compile-time scheduling and clustering of parallel programs onto parallel processing systems with distributed memory, which is called The Dynamic Critical Path Scheduling DCPS. The DCPS is superior to several other algorithms from the literature in terms of computational complexity, processors consumption and solution quality. DCPS has a time complexity of O(e + v log v), as opposed to DSC algorithm O((e+v) log v) which is the best known algorithm. Experimental results demonstrate the superiority of DCPS over the DSC algorithm.

scholarly journals Fast 10-Point DFT Algorithm for Power System Harmonic Analysis

2021 ◽  
Vol 11 (15) ◽  
pp. 7007
Author(s):  
Janusz P. Paplinski ◽  
Aleksandr Cariow
Keyword(s):  
Computational Complexity ◽  
Power System ◽  
Harmonic Analysis ◽  
Efficient Algorithm ◽  
System Analysis ◽  
Spectral Leakage ◽  
Power System Analysis ◽  
Improve Accuracy ◽  
The Cost ◽  
Harmonic Power

This article presents an efficient algorithm for computing a 10-point DFT. The proposed algorithm reduces the number of multiplications at the cost of a slight increase in the number of additions in comparison with the known algorithms. Using a 10-point DFT for harmonic power system analysis can improve accuracy and reduce errors caused by spectral leakage. This paper compares the computational complexity for an L×10M-point DFT with a 2M-point DFT.

A STRATEGY FOR SCHEDULING PARTIALLY ORDERED PROGRAM GRAPHS ONTO MULTICOMPUTERS

1995 ◽  
Vol 05 (04) ◽  
pp. 575-586
Author(s):  
BEN LEE ◽  
ALI R. HURSON
Keyword(s):  
Parallel Processing ◽  
Message Passing ◽  
Massively Parallel ◽  
Communication Overhead ◽  
Simulation Studies ◽  
Global Approach ◽  
Partially Ordered ◽  
Massively Parallel Processing ◽  
Time Scheduling ◽  
Scheduling Heuristic

The issue of scalability is key to the success of massively parallel processing. Due to their distributed nature, message-passing multicomputers are appropriate for achieving scalar performance. However, the message-passing model lacks programmability due to difficulties encountered by the programmers to partition and schedule the computation over the processors and to establish efficient inter-processor communication in the user code. Therefore, this paper presents a compile-time scheduling heuristic, called BLS, that maps programs onto the processors of a message-passing multicomputer. In contrast to other methods proposed, BLS takes a more global approach in attempt to balance the tradeoff between exploiting parallelism and reducing communication overhead. To evaluate the effectiveness of BLS, simulation studies of scheduling SISAL programs are presented.

scholarly journals On Quantum Algorithm for Binary Search and Its Computational Complexity

2015 ◽  
Vol 22 (03) ◽  
pp. 1550019 ◽  
Author(s):  
S. Iriyama ◽  
M. Ohya ◽  
I.V. Volovich
Keyword(s):  
Computational Complexity ◽  
Time Complexity ◽  
Quantum Algorithm ◽  
Binary Search ◽  
Search Problem

A new quantum algorithm for the search problem and its computational complexity are discussed. Its essential part is the use of the so-called chaos amplifier, [8, 9, 10, 13]. It is shown that for the search problem containing [Formula: see text] objects time complexity of the method is polynomial in [Formula: see text].

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.

scholarly journals Efficient Algorithm For L(3,2,1)-Labeling of Cartesian Product Between Some Graphs

10.29007/x3qf ◽  
2019 ◽  
Author(s):  
Sumonta Ghosh ◽  
Prakhar Pogde ◽  
Narayan C. Debnath ◽  
Anita Pal
Keyword(s):  
Communication Network ◽  
Assignment Problem ◽  
Efficient Algorithm ◽  
Time Complexity ◽  
Cartesian Product ◽  
Frequency Assignment ◽  
Frequency Assignment Problem ◽  
The Difference

L(h,k) Labeling in graph came into existence as a solution to frequency assignment problem. To reduce interference a frequency in the form of non negative integers is assigned to each radio or TV transmitters located at various places. After L(h,k) labeling, L(h,k, j) labeling is introduced to reduce noise in the communication network. We investigated the graph obtained by Cartesian Product betweenCompleteBipartiteGraphwithPathandCycle,i. e.,Km,n×Pr andKm,n×Cr byapplying L(3,2,1)Labeling. The L(3,2,1) Labeling of a graph G is the difference between the highest and the lowest labels used in L(3,2,1) and is denoted by λ3,2,1(G) In this paper we have designed three suitable algorithms to label the graphs Km,n × Pr and Km,n × Cr . We have also analyzed the time complexity of each algorithm with illustration.

Parallel Processing on Distributed Memory Multiprocessors

1990 ◽  
pp. 15-34 ◽  
Author(s):  
Max Lemke ◽  
Anton Schüller ◽  
Karl Solchenbach ◽  
Ulrich Trottenberg
Keyword(s):  
Parallel Processing ◽  
Distributed Memory

Petersen-star networks modeled by optical transpose interconnection system

2021 ◽  
Vol 17 (11) ◽  
pp. 155014772110331
Author(s):  
Jung-hyun Seo ◽  
HyeongOk Lee
Keyword(s):  
Parallel Processing ◽  
Optical Communication ◽  
Time Complexity ◽  
High Performance ◽  
Electronic Communication ◽  
Interconnection Network ◽  
Processing System ◽  
Factor Graph ◽  
Star Network ◽  
Network Cost

One method to create a high-performance computer is to use parallel processing to connect multiple computers. The structure of the parallel processing system is represented as an interconnection network. Traditionally, the communication links that connect the nodes in the interconnection network use electricity. With the advent of optical communication, however, optical transpose interconnection system networks have emerged, which combine the advantages of electronic communication and optical communication. Optical transpose interconnection system networks use electronic communication for relatively short distances and optical communication for long distances. Regardless of whether the interconnection network uses electronic communication or optical communication, network cost is an important factor among the various measures used for the evaluation of networks. In this article, we first propose a novel optical transpose interconnection system–Petersen-star network with a small network cost and analyze its basic topological properties. Optical transpose interconnection system–Petersen-star network is an undirected graph where the factor graph is Petersen-star network. OTIS–PSN n has the number of nodes 102n, degree n+3, and diameter 6 n − 1. Second, we compare the network cost between optical transpose interconnection system–Petersen-star network and other optical transpose interconnection system networks. Finally, we propose a routing algorithm with a time complexity of 6 n − 1 and a one-to-all broadcasting algorithm with a time complexity of 2 n − 1.

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