Parallel Computing

Recent Trends in

(RTPC)

May - August 2014

eISSN 2393-8749

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Recent Trends in Parallel Computing

(eISSN: 2393-8749)

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?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.

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Industrial Tribology Machine Dynamics & Maintenance

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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