Boosting Performance in Parallel Computing Models with a New Experimental Architecture

2021 ◽  
pp. 418-428
Author(s):  
Alberto Arteta Albert ◽  
Akshay Harshakumar ◽  
Luis Fernando de Mingo López ◽  
Nuria Gómez Blas
Keyword(s):  
Parallel Computing ◽  
Computing Models ◽  
Experimental Architecture

scholarly journals A Comparative Analysis of Distributed and Parallel Computing

2018 ◽  
pp. 60-67
Keyword(s):  
Parallel Computing ◽  
Comparative Analysis ◽  
Distributed Systems ◽  
Data Centers ◽  
Parallel Systems ◽  
The Other ◽  
Pros And Cons ◽  
Distributed And Parallel Computing ◽  
Better Than ◽  
Computing Models

In the age of emerging technologies, the amount of data is increasing very rapidly. Due to massive increase of data the level of computations are increasing. Computer executes instructions sequentially. But the time has now changed and innovation has been advanced. We are currently managing gigantic data centers that perform billions of executions on consistent schedule. Truth be- hold, if we dive deep into the processor engineering and mechanism, even a successive machine works parallel. Parallel computing is growing faster as a substitute of distributing computing. The performance to functionality ratio of parallel systems is high. Also, the I/O usage of parallel systems is lower because of ability to perform all operations simultaneously. On the other hand, the performance to functionality ratio of distributed systems is low. The I/O usage of distributed systems is higher because of incapability to perform all operations simultaneously. In this paper, an overview of distributed and parallel computing is described. The basic concept of these two computing is discussed. In addition to this, pros and cons of distributed and parallel computing models are described. Through many aspects, we can conclude that parallel systems are better than distributed systems.

Spiking Neural P Systems with Extended Channel Rules

2020 ◽  
Vol 31 (01) ◽  
pp. 2050049 ◽  
Author(s):  
Zeqiong Lv ◽  
Tingting Bao ◽  
Nan Zhou ◽  
Hong Peng ◽  
Xiangnian Huang ◽  
...  
Keyword(s):  
Parallel Computing ◽  
Control Mechanism ◽  
P Systems ◽  
Multiple Channels ◽  
Spiking Neural P Systems ◽  
Distributed Parallel Computing ◽  
New Variant ◽  
New Type ◽  
Turing Universality ◽  
Computing Models

This paper discusses a new variant of spiking neural P systems (in short, SNP systems), spiking neural P systems with extended channel rules (in short, SNP–ECR systems). SNP–ECR systems are a class of distributed parallel computing models. In SNP–ECR systems, a new type of spiking rule is introduced, called ECR. With an ECR, a neuron can send the different numbers of spikes to its subsequent neurons. Therefore, SNP–ECR systems can provide a stronger firing control mechanism compared with SNP systems and the variant with multiple channels. We discuss the Turing universality of SNP–ECR systems. It is proven that SNP–ECR systems as number generating/accepting devices are Turing universal. Moreover, we provide a small universal SNP–ECR system as function computing devices.

Performance prediction of parallel computing models to analyze cloud-based big data applications

2017 ◽  
Vol 21 (2) ◽  
pp. 1439-1454 ◽  
Author(s):  
Chao Shen ◽  
Weiqin Tong ◽  
Kim-Kwang Raymond Choo ◽  
Samina Kausar
Keyword(s):  
Big Data ◽  
Parallel Computing ◽  
Performance Prediction ◽  
Big Data Applications ◽  
Computing Models

scholarly journals STRUCTURAL-ALGORITHMIC MEAN OF THE AUTOMATED PLANNING OF CALCULABLE MODELS

2014 ◽  
pp. 36-44
Author(s):  
Anatoliy Gorbatyuk ◽  
Sergey Gorbatyuk
Keyword(s):  
Parallel Computing ◽  
Computer Systems ◽  
Automated Planning ◽  
Automated Design ◽  
Technical Solution ◽  
Reconstruction Method ◽  
Actual Problem ◽  
Design Technology ◽  
Calculation Time ◽  
Computing Models

The present paper is devoted to the scientific and technical solution of an actual problem of developing models and methods of the structural-algorithmic organization of parallel computing processes for enhancement of efficiency of computer systems. The method of algostructural designing of computing models has been proposed. It performs automated design of model by using library algostructures and connections among them. The algostructural models reconstruction method which provides structuring on the base of equal transformations has been improved. The calculation paralleling method in algostructures has been developed. It accounts for structure-algorithmic organization of the models and provides a calculation optimization and allows a calculation time to be decreased, available technological resources to be effectively used. The method of structural reconfiguration in problem-oriented algostructural construction is in progress development. The automated design technology of algostructural models is in progress development. The proposed technology provides effective designing of computing models.

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