recent trends in parallel computing (vol1, issue2)
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Parallel Computing
Recent Trends in
(RTPC)
May - August 2014
eISSN 2393-8749
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Recent Trends in Parallel Computing
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I take the privilege to present the print version for the Volume 1 Issue (2) 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. Coarse Grain to Mixed Grain Parallel Computing Tirumale Ramesh 1
2. Dynamic Data Flow based Spatial Sorting Method for GPUs: Software Based Autonomous Parallelization Ankush Rai 11
3. Edge Split Mapping Parallel Algorithm for Tetrahedral Mesh Refinement on Distributed Memory SystemsAnkush Rai 15
ContentsRecent Trends in Parallel Computing
RTPC (2014)© STM Journals 2014. All Rights Reserved
Recent Trends in Parallel Computing ISSN: 2393-8749 (online)
Volume 1, Issue 2
www.stmjournals.com
Coarse Grain to Mixed Grain Parallel Computing
Tirumale Ramesh* Department of Computer Science and Engineering, Amrita University, Bengaluru Campus, India
Abstract Parallel computing interests are growing again as a result of multicore processors.
History of parallel computing dates back to early 1970’s when supercomputers supported high performance computing demand for scientific applications. Today, highly dense
Very Large Scale Integration (VLSI) chips have opened up new opportunities and challenges for parallel computing. In this article, we provide a short synopsis of an
evolutionary path of parallel computing from both hardware granularity and
programming point of view. The article also discusses the challenges of parallel computing to emerging big data analytics area.
Keywords: Parallel Computing, Coarse Grain, Fine Grain, Mixed-grain,
Multiprocessor, Multicore, Instruction Level Parallelism, SIMD, MIMD, Data Flow,
VLSI, Field Programmable Gate Arrays (FPGA), Multi-FPGA, Graphic Processor
Units (GPU), Big Data Analytics
RTPC (2014)© STM Journals 2014. All Rights Reserved
Recent Trends in Parallel Computing ISSN: 2393-8749 (online)
Volume 1, Issue 2
www.stmjournals.com
Dynamic Data Flow based Spatial Sorting Method for
GPUs: Software Based Autonomous Parallelization
Ankush Rai* Department of Applied Science, CRIAD Laboratories, Smiriti Nagar, Bhillai, Chhatisgarh, India
Abstract The direct communication between distant parts of a computer system for a mesh or grid
based scalable parallel system isn't cost effective. Thus, we have presented and evaluated
the performances of spatial sorting method for multi-core GPUs (Graphic Processing Unit) computing. It is a parallel sorting algorithm for autonomous threading of workload
which is considerably faster than Thrust Merge sorting, which is known to be the best
comparison based sorting algorithm for GPUs. The study shows that the proposed algorithm is more efficient than randomized sample sort, when the memory operations for
parallelization of stack units are taken into consideration. Additionally, the proposed algorithm provides an advantage over traditional sorting methods in way that it facilitate
dynamic data flow and therefore, its operating time is devoid of the input data dependent
fluctuations. Our technique proves its potential extensions of adaptability to more complex parallel algorithms.
Keywords: Autonomous parallelization, threading, sorting, GPUs, parallel
algorithm, dynamic data flow
RTPC (2014)© STM Journals 2014. All Rights Reserved
Recent Trends in Parallel Computing ISSN: 2393-8749 (online)
Volume 1, Issue 2
www.stmjournals.com
Edge Split Mapping Parallel Algorithm for Tetrahedral
Mesh Refinement on Distributed Memory Systems
Ankush Rai* Department of Applied Science, CRIAD Laboratories, Smiriti Nagar, Bhillai, Chhatisgarh, India
Abstract In this paper we have described a parallel mesh refining algorithm based on edge split
mapping parallel algorithm for on a distributed memory system; that aids the user in
auto-managing the assignment of processor workload and subdivision of the 3d-mesh. We discussed the design and performance of the algorithm which speeds up the processing of
the instructions over a multi-core processor.
Keywords: Parallel algorithm, Distributed memory, Mesh Refinement