By sacrificing completeness it increases efficiency. Useful in solving tough problems which could not be solved any other way. Otherwise repeat from 1. This method is basically a depth first search as complete solutions must be created before testing.
In this model a single process can have multiple, concurrent execution paths. The main program is scheduled to run by the native operating system. It loads and acquires all the necessary softwares and user resources to activate the process.
Any thread can execute any one subroutine and at the same time it can execute other subroutine. Threads communicate with each other through global memory. This requires Synchronization constructs to insure that more than one thread is not updating the same global address at any time.
Threads can be created and destroyed, but the main program remains live to provide the necessary shared resources until the application has completed.
Threads are commonly associated with shared memory architectures and operating systems. In the data parallel model, most of the parallel work focuses on performing operations on a data set. The data set is typically organised into a common structure, such as an array or a cube.
A set of tasks work collectively on the same data structure with each task working on a different portion of the same data structure. In shared memory architectures, all tasks may have access to the data structure through the global memory.
A single program is executed by all tasks simultaneously. SPMD programs usually have the necessary logic programmed into them to allow different tasks to branch or conditionally execute only those parts of the program they are designed to execute. That is, tasks do not necessarily have to execute the entire program, they may execute only a portion of it.
In this model, different tasks may use different data.
There are certain parallel overheads associated with parallel computing. The parallel overhead is the amount of time required to coordinate parallel tasks, as opposed to doing useful work.
The performance metrics are not able to achieve a linear curve in comparison to the increase in number of processors in the parallel computer.
The reason for the above is the presence of overheads in the parallel computer which may degrade the performance. The well-known sources of overheads in a parallel computer are: In the parallel computer, the problem is split into sub-problems and is assigned for computation to various processors.
But sometimes the sub-problems are not distributed in a fair manner to various sets of processors which causes imbalance of load between various processors.
This event causes degradation of overall performance of parallel computers.
As the data is assigned to multiple processors in a parallel computer while executing a parallel algorithm, the processors might be required to interact with other processes thus requiring inter-processor communication.
Therefore, there is a cost involved in transferring data between processors which incurs an overhead. In order to execute a parallel algorithm on a parallel computer, K number of processors are required. It may be noted that the given input is assigned to the various processors of the parallel computer.
As we already know, execution time decreases with increase in number of processors. However, when input size is fixed and we keep on increasing the number of processors, in such a situation after some point the execution time starts increasing. This is because of overheads encounteed in the parallel system.
Multiple processors require synchronization with each other while executing a parallel algorithm.
That is, the task running on processor X might have to wait for the result of a task executing on processor Y. Therefore, a delay is involved in completing the whole task distributed among K number of processors. Analysing the performance of parallel algorithms: A generic algorithm is mainly analysed on the basis of the following parameters: Usually we give much more importance to time complexity in comparison with space complexity.
The subsequent section highlights the criteria of analysing the complexity of parallel algorithms. The fundamental parameters required for the analysis of parallel algorithms are as follow: The Total Number of Processors Required 3.
The parallel architectures have been designed for improving the computation power of the various algorithms. Thus, the major concern of evaluating an algorithm is the determination of the amount of time required to execute.
Usually, the time complexity is calculated on the basis of the total number of steps executed to accomplish the desired output. The time complexity of an algorithm varies depending upon the instance of the input for a given problem.IGNOU MCA Study Material. July 15, January 21, MCSE Parallel Computing: MCSL Lab (C and Assembly Language Programming) For attend IGNOU MCA TEE Examination, it is necessary to first submit your Assignments to IGNOU and it is possible from IGNOU Study Material.
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