Design and analysis of parallel algorithms

  • How do we analyze parallel algorithm?

    An algorithm is a sequence of steps that take inputs from the user and after some computation, produces an output.
    A parallel algorithm is an algorithm that can execute several instructions simultaneously on different processing devices and then combine all the individual outputs to produce the final result..

  • What are steps to develop parallel algorithm?

    In parallel algorithms (task parallelism), A big task is divided into two or more sub tasks and each sub task is executed by one processing element (PE) parallely.
    All sub tasks are depended on each other..

  • What is a parallel algorithm in design and analysis of algorithms?

    A parallel algorithm is cost optimal when its cost matches the run time of the best known sequential algorithm Ts for the same problem.
    The speed up S offered by a parallel algorithm is simply the ratio of the run time of the best known sequential algorithm to that of the parallel algorithm..

  • What is methodological design of parallel algorithm?

    Methodological Approach to Parallel Algorithm Design: .

    1. Partitioning
      2. . .
    2. Communication
      3. . .
    3. Agglomeration
      4. . .
    4. Mapping

  • What is the analysis of parallel programming?

    In computer science, the analysis of parallel algorithms is the process of finding the computational complexity of algorithms executed in parallel – the amount of time, storage, or other resources needed to execute them..

  • Identifying portions of the work that can be performed concurrently.
    Mapping the concurrent pieces of work onto multiple processes running in parallel.
    Distributing the input, output, and intermediate data associated with the program.
    Managing accesses to data shared by multiple processors.
  • The performance of a parallel algorithm is determined by calculating its speedup.
    Speedup is defined as the ratio of the worst-case execution time of the fastest known sequential algorithm for a particular problem to the worst-case execution time of the parallel algorithm.
Selim G. The design and analysls of parallel algorithms / by Sellm G. Akl. p. cm. Bibliography: p.
The design and analysls of parallel algorithms / by Sellm G. Akl. p. cm. Bibliography: p. Includes Index. ISBN 0-13-200056- 

Can a parallel algorithm solve a list-ranking problem?

There are, however, several work-efficient parallel solutions to both of these problems

The following parallel algorithm uses the technique of random sampling to construct a pointer from each node to the end of a list of n nodes in a work-efficient fashion

The algorithm is easily generalized to solve the list-ranking problem

What is a cost optimal in a parallel algorithm?

One measure used for the analysis of parallel algorithms is the cost, defined to be the parallel running time multiplied by the number of processors employed by the algorithm

When the cost is proportional to a lower bound on the number of operations for a general sequential solution to the problem, the parallel method is called “cost optimal

”

What is the design and analysis of parallel algorithms?

The subject of this chapter is the design and analysis of parallel algorithms

Most of today’s algorithms are sequential, that is, they specify a sequence of steps in which each step consists of a single operation

These algorithms are well suited to today’s computers, which basically perform operations in a sequential fashion

The process of designing a parallel algorithm consists of four steps:

  • □ decomposition of a computational problem into tasks that can be executed simultaneously, and development of sequential algorithms for individual tasks;

Parallel Algorithm - Design Techniques

  • Divide and Conquer Method In the divide and conquer approach, the problem is divided into several small sub-problems. Then the sub-problems are solved recursively and combined to get the solution of the original problem. ...
The task/channel model encourages parallel algorithm designs that maximize local computations and minimize communications The algorithm designer typically partitions the computation, identifies communications among primitive tasks, agglomerates primitive tasks into larger tasks, and decides how to map tasks to processors

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