Parallel Computing

Recent Trends in

(RTPC)

Jan - April 2014

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Recent Trends in Parallel Computing

Focus and Scope Covers

† ILLIAC IV, Torus, PM2I, Butterfly, Mesh-of-tree

† Pyramid, Generalized Hypercube

† Twisted Cube, Folded Hypercube

† Cross-connected Cube

† Task and Data parallelism, Programming for performance

† GPU, Accelerators.

† Multi-core architectures.

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Industrial Tribology Machine Dynamics & Maintenance

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Indian Institute of Technology Delhi, India.

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Alternative Energy Technology Laboratory,

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Indian Institute of Technology, Chennai, India.

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Dr.Rashid AliAssistant Professor, College of

Computers and IT Taif University, Taif, Saudi Arabia.

Dr.Kai-Cheung LeungDoctor of Philosophy in Computer

Science, University of Otago.

Prof.Maurizio PalesiAssociate professor, information

office of the university ING-INF/05 scientific field of

information processing.

Dr.Tirumale K RameshProfessor at Amrita school of enginnering

Bangalore, India.

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Indian Institute of Technology, Guwahati.India.

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Gulnur Davletzhanovna ZhangissinaProfessor Head in Department

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K.I.Satpaev, Almaty, Kazakhstan.

I take the privilege to present the print version for the Volume 1 Issue (1) of Recent Trends in Parallel

Computing. The intension of RTPC is to create an atmosphere that stimulates creativeness, research

and growth in the area of parallel computing.

The development and growth of the mankind is the consequence of brilliant Research done by

eminent Scientists and Engineers in every field. RTPC provides an outlet for Research findings and

reviews in areas of parallel computing found to be relevant for National and International recent

developments & research initiative.

The aim and scope of the Journal is to provide an academic medium and an important reference for

the advancement and dissemination of Research results that support high level learning, teaching and

research in the domain of parallel computing.

Finally, I express my sincere gratitude and thanks to our Editorial/ Reviewer board and Authors for

their continued support and invaluable contributions and suggestions in the form of authoring write

ups/ reviewing and providing constructive comments for the advancement of the journals. With

regards to their due continuous support and co-operation, we have been able to publish quality

Research/Reviews findings for our customers base.

I hope you will enjoy reading this issue and we welcome your feedback on any aspect of the Journal.

Dr. Archana Mehrotra

Director

STM Journals

Director's Desk

STM JOURNALS

1. Parallelizing Apriori Algorithm using Open MP Nilesh S. Korde, Shailendra W. Shende 1

2. Ant Colony Optimization in Cloud for Load Balancing Mayuri R. Popalghat, Sonali S. Pandhare, Shweta U. Shinde, Sukanya S. Pathak 6

3. Static Load Balancing Scheduling for Multiprocessing SystemShahid Iqbal 11

ContentsRecent Trends in Parallel Computing

RTPC (2014)© STM Journals 2014. All Rights Reserved

Recent Trends in Parallel Computing

Volume 1, Issue 1

www.stmjournals.com

Parallelizing Apriori Algorithm using Open MP

Nilesh S. Korde*, Shailendra W. Shende Department of Information Technology,Yeshwantrao Chavan College of Engineering, Nagpur, India

Abstract An Association rule mining used for finding frequent item sets thus generating rules from

the frequent item sets. Finding frequent itemsets is more expensive in terms of CPU power and computing resources utilization. Apriori Algorithm is used for high

dimensionality on massively large data sets. Parallelism reduces the time required for serial processing. Parallel computing can be applied for mining of association rules.

Parallel apriori algorithm focus on parallelizing the process of frequent item set

discovery. The computation of frequent item sets mainly consists of creating the candidates and counting them. The parallel frequent itemsets mining algorithms

addresses the issue of distributing the candidates among processors such that their counting and creation is effectively parallelized. In this paper we have Implemented

Apriori Algorithm in serial and parallel on a quad core processor and compared their

performance with respect to time.

Keywords: Multi-core, parallelism, Apriori, data mining, open MP

RTPC (2014)© STM Journals 2014. All Rights Reserved

Recent Trends in Parallel Computing

Volume 1, Issue 1

www.stmjournals.com

Ant Colony Optimization in Cloud for Load Balancing

Mayuri R. Popalghat, Sonali S. Pandhare, Shweta U. Shinde*, Sukanya S. Pathak JSPM's Rajarshi Shahu College of Engg. Tathawade, Pune-411033, India

Abstract There is an increase in the demand of cloud these days, as more and more people are looking for virtual storage of data rather than investing in physical storage. Cloud has

many advantages like it focuses on maximizing the effectiveness of the shared resources. Cloud resources are shared by multiple users and these resources are dynamically

allocated as per the demand. One of the major issues faced by cloud computing these

days are that, demands for resources are continuously increasing as well as are irregularly distributed. To overcome this, it is very essential that there is a proper load

distribution. In our paper, we propose Ant Colony Optimization algorithm for

implementing load balancing amongst nodes. This is a modified algorithm over the existing ones as in this method, the ants continuously update a single global result set,

whereas in the previous ones, each ant had their own result set. Here we use the concept of foraging and trailing pheromones. This helps to create an efficient result set which

helps in load balancing.

Keywords: Ant colony optimization; load balancing; cloud computing; foraging and

trailing pheromones.

RTPC (2014)© STM Journals 2014. All Rights Reserved

Recent Trends in Parallel Computing

Volume 1, Issue 1

www.stmjournals.com

Static Load Balancing Scheduling for

Multiprocessing System

Shahid Iqbal* Birla Institute of Technology and Science, BITS-Pilani, Goa-403726, India

Abstracts Multiprocessing is the coordinated processing of programs by more than one computer processor. Multiprocessing simply means putting multiple processors in one system in

order to speed up computation. This paper talks about the static load balancing scheduling algorithm for the multiprocessing system. In this different tasks are statically

assigned to different processors for efficient utilization of each processor in the system.

In this a new term “Gap” has been added which is the basis of the performance of the

system. Our Simulation result shows the better and efficient utilization of computer

processors in a static manner.

Keywords: Multiprocessing system, scheduling algorithm, load balancing.