parallel computers architecture and programming v rajaraman pdf

Parallel computers architecture and programming v rajaraman pdf

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Parallel Computers Architecture And Programming V Rajaraman Free Books

Parallel Computers: Architecture and Programming, 2nd Edition

Parallel Computers: Architecture and Programming by V. Rajaraman, C. Siva Ram Murthy

Mengenai Saya

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Parallel Computers Architecture And Programming V Rajaraman Free Books

To browse Academia. Skip to main content. By using our site, you agree to our collection of information through the use of cookies. To learn more, view our Privacy Policy. Log In Sign Up. Download Free PDF. Rajnish Dashora. Prof Narayanmoorthy. Garrison Prinslow. Download PDF. A short summary of this paper. Narayana Moorthi. The running time of any complex algorithm or applications can be improved by running more than one task at the same time with multiple processors concurrently.

Here the performance improvement is measured in terms of increase in the number of cores per machine and is analyzed for better Energy Efficient optimal work load balance. In this paper we investigate the review and literature survey of parallel computers for future improvements. We aim to present the theoretical and technical terminologies for parallel computing.

The focus here is the process of comparative analysis of single core and multicore systems to run an application program for faster execution time and optimize the scheduler for better performance. Now days everyone is having desktop and laptop with multiple processor [4] [5] [6] [7] [8] cores. So it is necessary for us to understand the various concepts and methods involved in parallel programming and parallel computing to make use of next generation computers more effectively.

The processing capability of Microprocessor is increasing in terms of clock speed and inclusion of more and more execution units with pipeline support in a chip to support parallelism. The parallel computer is defined as the collection of more than one processing element or cores to solve a large size problem in fastest rate. Processing the task in parallel [9] is the computing challenge. The way we measure the performance is the running time or execution time of the program or application.

The method to reduce the running time of a program is through high speed computing which can be done through parallel programming and parallel computing machines. Balancing the workload among threads or cores or processors is critical to application performance.

The key objective for load balancing is to minimize idle time on threads and share the workload equally among all cores and threads with minimal work sharing overheads. Multi-core Architecture Overview Computer [10] [11] [12] [13] [14] [15] [16] is an electronic digital machine which takes instructions to solve the given problem or application. Any computer machine consists of Input devices like keyboard, mouse and output device like monitor and printer.

It also has many types and sizes of memory for instruction and data storage. The fundamental computer machine is identified as Von-Neumann Architecture. The program is executed sequentially. To improve the run time of program parallelism is introduced with different levels.

The Multicore Processor Architectures [17] [18] [19] [20] [21] [22] consist of one or more processor cores to provide higher levels of parallelism. The parallel programming approach is practiced to get high performance within the system.

A multi-core processor is composed of two or more independent cores. Manufacturers typically integrate the cores onto a single integrated circuit die known as a chip multiprocessor or CMP , or onto multiple dies in a single chip package.

Speedup: The measure of speedup is as follows. The speedup increases with increase in number of processor cores. The quality of an algorithm [23] either sequential or parallel is measured using various metrics like the running time, space, speedup, efficiency , memory access time, hit ratio, Floating point operations per second, etc.

Complexity Analysis: An algorithm is a finite sequence of steps well defined to solve a given problem [19]. In many case there will be more than one method or algorithm to solve the given problem. Here the choice or selection of an algorithm depends on efficiency or complexity of an algorithm. The complexity of an algorithm measures the running time and or storage space required for the algorithm.

The following notations are used to measure the complexity of an algorithm. The parallel algorithm runs on many cores or processors. The two measures of complexity of an algorithm are as follows. Worst case Maximum time or space and Average case Expected Value plays an important role to select the algorithm. It has the collection of compiler directives, library functions and environment variables.

When the program starts the single thread named as master thread is active. This active thread executes the sequential portion of the program. When parallel operations ae required the master thread creates additional threads which are known as fork. In the parallel region of the code the master and additional threads executes. At the end of parallel region the additional threads are destroyed and only master thread continues this is known as join.

A program written in Open MP executes as a master or single thread as same as sequential program. When the parallel construct is encountered, the master thread creates the number of threads and each thread computes the task simultaneously or concurrently.

The following tools and software is necessary for Open MP programming. The following steps illustrate the proposed work for Performance Study, Evaluation and analysis of single core and Multicore Machines.

Step1: Identify and define the large size problem to be solved Step2: Identify the sequential algorithm to solve the above problem Step3: Run this sequential algorithm code in a single core machine Step4. Measure the various performance parameters Like Total Execution time or Run time, Cache memory size, hit ratio, CPU utilization, etc Step5: Identify the concurrency in the above application code Independent sub tasks Step6: Write the parallel code using parallel programming languages Open MP Step7: Run the above code using multicore machines Step8: Measure the various performance parameters Like Total Execution time or Run time, Cache memory size, hit ratio, CPU utilization, etc Step9: Rerun the above code using various thread size or processors cores 2, 4, 8, 16, and 32, n and make a comparative analysis table.

The following bubble sort, selection sort, insertion sort and quick sort algorithms were implemented for sequential and parallel codes. Few observations on the experiment is as follows. It is observed that when the data is large the performance gain of parallel computing plays important role. As observed in all the algorithms the smaller the data sent the less efficient the parallel computing so parallel computing is more suitable for larger data elements.

Table 1 : Selection sort Number of elememts Sequential time Parallel time 10 0. We also analyzed a sample application code sorting algorithms operation on single core and multicore machines. If the data size is more we get the benefit of parallel computing by running multiple tasks continuously and concurrently with multiple cores. It has more benefits in terms of processing cores but it is also giving open challenge for the software developers to make use of multiple cores simultaneously for parallel computing.

So it is time for us to learn the parallel programming model to address the increasing processor cores in the next generation computers. Hennessy, D. David E. Michael J. Raj Jain Narayana Moorthi, P.

Mohan Kumar, Dr. Rajnish Dashora, Harsh P. Rajaraman, C. Narayana Moorthi, R. Related Papers. By Zaid Alyasseri. By Manuel Rodriguez and Mark A. Download pdf. Remember me on this computer. Enter the email address you signed up with and we'll email you a reset link.

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Parallel Computers: Architecture and Programming, 2nd Edition

The volume and variety of data being generated using computers is doubling every two years. This is called big data. It is possible to analyse such huge data collections with clusters of thousands of inexpensive computers to discover patterns in the data that have many applications. But analysing massive amounts of data available in the Internet has the potential of impinging on our privacy. Inappropriate analysis of big data can lead to misleading conclusions. In this article, we explain what is big data, how it is analysed, and give some case studies illustrating the potentials and pitfalls of big data analytics. This is a preview of subscription content, access via your institution.

Parallel Computers: Architecture and Programming by V. Rajaraman, C. Siva Ram Murthy

Save extra with 2 Offers. Rajaraman, C. A basic knowledge of the architecture of parallel computers and how to program them, is thus, essential for students of computer science and IT professionals.

REVIEW OF PARALLEL COMPUTING, PARALLEL PROGRAMMING AND ITS APPLICATIONS

Now a day individuals who Living in the era exactly where everything reachable by interact with the internet and the resources included can be true or not need people to be aware of each info they get. How many people to be smart in obtaining any information nowadays? Of course the reply is reading a book.

Mengenai Saya

Rajaraman and C. Siva Ram Murthy. All rights reserved. No part of this book may be reproduced in any form, by mimeograph or any other means, without permission in writing from the publisher.

The aim of digital image processing is to improve the quality of image and subsequently to perform features extraction and classification. It is effectively used in computer vision, medical imaging, meteorology, astronomy, remote sensing and other related field. The main problem is that it is generally time consuming process; Parallel Computing provides an efficient and convenient way to address this issue.

Rajaraman , Ph.

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