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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Dr.Kai-Cheung LeungDoctor of Philosophy in Computer
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Prof.Maurizio PalesiAssociate professor, information
office of the university ING-INF/05 scientific field of
information processing.
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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.