Recent trends in parallel computing (vol1, issue2) by STM Journals Publication

eISSN 2393-8749

Recent Trends in

Parallel Computing (RTPC) May - August 2014

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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

Recent Trends in Parallel Computing

Contents

1. Coarse Grain to Mixed Grain Parallel Computing Tirumale Ramesh

2. Dynamic Data Flow based Spatial Sorting Method for GPUs: Software Based Autonomous Parallelization Ankush Rai

3. Edge Split Mapping Parallel Algorithm for Tetrahedral Mesh Refinement on Distributed Memory Systems Ankush Rai 15

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 1970â&#x20AC;&#x2122;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

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

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