A PARALLEL ALGORITHM FOR MINIMIZING ESOP EXPRESSIONS

2014 ◽  
Vol 23 (01) ◽  
pp. 1450015 ◽  
Author(s):  
GEORGE PAPAKONSTANTINOU
Keyword(s):  
Boolean Function ◽  
Parallel Algorithms ◽  
Parallel Algorithm ◽  
Minimization Problem ◽  
Processing Time ◽  
Symmetric Functions ◽  
Sum Of Products ◽  
Speed Up ◽  
Exclusive Or

Two parallel algorithms are proposed in this paper for solving the problem of finding exact exclusive-or sum of products (ESOP) expressions for an arbitrary Boolean function. This minimization problem is a very difficult one and solutions have been proposed only for up to seven variables. The processing time for some symmetric functions of seven variables is of the order of weeks. The proposed algorithm is a hybrid one (OpenMP, MPI) and a speed-up of more than nine could be achieved, for a cluster of three nodes with four cores each.

scholarly journals Parallel Algorithms for Spatial Rainfall Distribution

Jurnal INKOM ◽  
2014 ◽  
Vol 8 (1) ◽  
pp. 29 ◽  
Author(s):  
Arnida Lailatul Latifah ◽  
Adi Nurhadiyatna
Keyword(s):  
Parallel Algorithms ◽  
Computation Time ◽  
Computational Time ◽  
Rainfall Distribution ◽  
Flood Modelling ◽  
Computation Efficiency ◽  
Distance Weighting ◽  
Speed Up ◽  
Important Input ◽  
Serial Algorithms

This paper proposes parallel algorithms for precipitation of flood modelling, especially applied in spatial rainfall distribution. As an important input in flood modelling, spatial distribution of rainfall is always needed as a pre-conditioned model. In this paper two interpolation methods, Inverse distance weighting (IDW) and Ordinary kriging (OK) are discussed. Both are developed in parallel algorithms in order to reduce the computational time. To measure the computation efficiency, the performance of the parallel algorithms are compared to the serial algorithms for both methods. Findings indicate that: (1) the computation time of OK algorithm is up to 23% longer than IDW; (2) the computation time of OK and IDW algorithms is linearly increasing with the number of cells/ points; (3) the computation time of the parallel algorithms for both methods is exponentially decaying with the number of processors. The parallel algorithm of IDW gives a decay factor of 0.52, while OK gives 0.53; (4) The parallel algorithms perform near ideal speed-up.

scholarly journals SEQUENTIAL AND PARALLEL ALGORITHMS FOR CAUSAL EXPLANATION WITH BACKGROUND KNOWLEDGE

2004 ◽  
Vol 12 (supp02) ◽  
pp. 101-122
Author(s):  
BHASKARA REDDY MOOLE ◽  
MARCO VALTORTA
Keyword(s):  
Parallel Algorithms ◽  
Parallel Algorithm ◽  
Causal Explanation ◽  
Background Knowledge ◽  
Sequential Algorithm ◽  
Dependency Model

This paper presents a new sequential algorithm to answer the question about the existence of a causal explanation for a set of independence statements (a dependency model), which is consistent with a given set of background knowledge. Emphasis is placed on generality, efficiency and ease of parallelization of the algorithm. From this sequential algorithm, an efficient, scalable, and easy to implement parallel algorithm with very little inter-processor communication is derived.

scholarly journals Retraction: Parallel Algorithm for Reduction of Data Processing Time in Big Data (Journal of Physics: Conference Series 1432 012095)

2020 ◽  
Vol 1432 ◽  
pp. 012110
Author(s):  
Jesús Silva ◽  
Hugo Hernández Palma ◽  
William Niebles Núñez ◽  
David Ovallos-Gazabon ◽  
Noel Varela
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Parallel Algorithm ◽  
Processing Time ◽  
Conference Series

AN IMPROVED PARALLEL PREFIX ALGORITHM ON OTIS-MESH

2006 ◽  
Vol 16 (04) ◽  
pp. 429-440 ◽  
Author(s):  
PRASANTA K. JANA ◽  
BHABANI P. SINHA
Keyword(s):  
Parallel Algorithms ◽  
Parallel Algorithm ◽  
Time Complexity ◽  
Data Distribution ◽  
General Theme ◽  
Parallel Prefix

Wang and Sahni [4] reported two parallel algorithms for N-point prefix computation on an N-processor OTIS-Mesh optoelectronic computer. The overall time complexity for both SIMS and MIMD models of their first algorithm was shown to be (8 N1/4 - 1) electronic moves and 2 OTIS moves. This was further reduced to (7 N1/4 - 1) electronic moves and 2 OTIS moves in their second algorithm. We present here an improved parallel algorithm for N-point prefix computation on an N-processor OTIS-Mesh, which needs (5.5 N1/4 + 3) electronic moves and 2 OTIS moves. Our algorithm is based on the general theme of parallel prefix algorithm proposed in [4] but following the data distribution and local prefix computation similar to that of [1].

scholarly journals Disjointed sum of products by a novel technique of orthogonalizing ORing

2018 ◽  
Vol 16 (1) ◽  
pp. 392-406
Author(s):  
Yavuz Can
Keyword(s):  
Boolean Function ◽  
Differential Calculus ◽  
Novel Technique ◽  
Sum Of Products ◽  
One Step ◽  
Set Up ◽  
Orthogonal Form

AbstractThis work presents a novel combining method called ‘orthogonalizing ORing $\bigcirc\!\!\!\!\!\!\vee $’ which enables the building of the union of two conjunctions whereby the result consists of disjointed conjunctions. The advantage of this novel technique is that the results are already presented in an orthogonal form which has a significant advantage for further calculations as the Boolean Differential Calculus. By orthogonalizing ORing two calculation steps - building the disjunction and the subsequent orthogonalization of two conjunctions - are performed in one step. Postulates, axioms and rules for this linking technique are also defined which have to be considered getting correct results. Additionally, a novel equation, based on orthogonalizing ORing, is set up for orthogonalization of every Boolean function of disjunctive form. Thus, disjointed Sum of Products can be easily calculated in a mathematical way by this equation.

A Parallel Algorithm for Mining Non-Redundant Recurrent Rules from a Sequence Database

2019 ◽  
Vol 23 (5) ◽  
pp. 956-961
Author(s):  
Seung-Yong Yoon ◽  
◽  
Hirohisa Seki
Keyword(s):  
Parallel Algorithm ◽  
Processing Time ◽  
Experimental Results ◽  
Task Parallelism ◽  
Sequence Database ◽  
Computation Cost ◽  
Software Specification ◽  
Specification And Verification

We propose a parallel algorithm for mining non-redundant recurrent rules from a sequence database. Recurrent rules, proposed by Lo et al. [1], can express “Whenever a series of precedent events occurs, eventually a series of consequent events occurs,” and they have shown the usefulness of recurrent rules in various domains, including software specification and verification. Although some algorithms such as NR3 have been proposed, mining non-redundant recurrent rules still requires considerable processing time. To reduce the computation cost, we present a parallel approach to mining non-redundant recurrent rules, which fully utilizes the task-parallelism in NR3. We also give some experimental results, which show the effectiveness of our proposed method.

A HYPERCUBE ALGORITHM FOR SLIDING WINDOW COMPRESSION

2000 ◽  
Vol 10 (04) ◽  
pp. 315-326
Author(s):  
CHRISTOS KAKLAMANIS ◽  
CHARALAMPOS KONSTANTOPOULOS ◽  
ANDREAS SVOLOS
Keyword(s):  
Parallel Algorithms ◽  
Parallel Algorithm ◽  
Interconnection Networks ◽  
Lossless Compression ◽  
Sliding Window ◽  
Efficient Implementation ◽  
Text Compression ◽  
Decoding Algorithm ◽  
Irregular Structure ◽  
Character String

Dictionary compression belongs to the class of lossless compression methods and is mainly used for compressing text files. The most known examples of this technique are the algorithms of the LZ coding family whose common feature is the use of an adaptive dictionary which is dynamically adjusting during the algorithm execution. In this paper, we present a parallel algorithm for one of these coding algorithms, namely the LZ77 coding algorithm also known as a sliding-window coding algorithm. We also present a parallel algorithm for the corresponding LZ77 decoding algorithm. Although there exist PRAM algorithms for various dictionary compression methods, their rather irregular structure has discouraged their implementation on practical interconnection networks such as the mesh and hypercube. However in the case of LZ77 coding/decoding, we show how to exploit the specific properties of the algorithm in order to achieve an efficient implementation on the hypercube. Specifically, we show how to encode a N-character string on a N-node hypercube in only O( log 2N) time. In contrast, a naive simulation of a PRAM algorithm of the LZ77 coding on the hypercube would have O( log 3N) complexity. In addition, we further enhance the performance of our parallel algorithms by using some known heuristics from the field of text compression.

Parallel 3-D pseudospectral simulation of seismic wave propagation

Geophysics ◽  
1998 ◽  
Vol 63 (1) ◽  
pp. 279-288 ◽  
Author(s):  
Takashi Furumura ◽  
B. L. N. Kennett ◽  
Hiroshi Takenaka
Keyword(s):  
Wave Propagation ◽  
Parallel Algorithm ◽  
Three Dimensional ◽  
Seismic Wave Propagation ◽  
Parallel Computer ◽  
Maximum Speed ◽  
Computational Domain ◽  
Concurrent Use ◽  
Speed Up ◽  
Digital Equipment

Three‐dimensional pseudospectral modeling for a realistic scale problem is still computationally very intensive, even when using current powerful computers. To overcome this, we have developed a parallel pseudospectral code for calculating the 3-D wavefield by concurrent use of a number of processors. The parallel algorithm is based on a partition of the computational domain, where the field quantities are distributed over a number of processors and the calculation is concurrently done in each subdomain with interprocessor communications. Experimental performance tests using three different styles of parallel computers achieved a fairly good speed up compared with conventional computation on a single processor: maximum speed‐up rate of 26 using 32 processors of a Thinking Machine CM-5 parallel computer, 1.6 using a Digital Equipment DEC‐Alpha two‐CPU workstation, and 4.6 using a cluster of eight Sun Microsystems SPARC-Station 10 (SPARC-10) workstations connected by an Ethernet. The result of this test agrees well with the performance theoretically predicted for each system. To demonstrate the feasibility of our parallel algorithm, we show three examples: 3-D acoustic and elastic modeling of fault‐zone trapped waves and the calculation of elastic wave propagation in a 3-D syncline model.

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