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Overview of Basic Numerical Methods and Parallel Computing
Published in Sourav Banerjee, Cara A.C. Leckey, Computational Nondestructive Evaluation Handbook, 2020
Sourav Banerjee, Cara A.C. Leckey
Automatic parallelization or simply called auto-parallelization refers to converting serial program into parallel program by employing multiple processors simultaneously in a shared-memory multiprocessor (SMP) system [15]. The objective of automatic parallelization, as the name implies, is to automate the process of transforming serial program to parallel such that the programmers do not have to go through the hectic and error-prone manual parallelization process [16]. Though the quality of automatic parallelization has improved in the past several decades, fully automatic parallelization of sequential programs by compilers is still far from being a standard norm. The auto-parallelization mostly focuses on loops since most of the execution time of a program takes place inside of loop.
Automatic Code Parallelization and Optimization
Published in David R. Martinez, Robert A. Bond, Vai M. Michael, High Performance Embedded Computing Handbook, 2018
It is not yet clear what the best approach to automatic parallelization is. Optimizing individual kernels in an application is certainly beneficial, particularly if the application is heavily dependent on a particular kernel. However, it does not help with the overall program optimization.
Study and evaluation of automatic offloading method in mixed offloading destination environment
Published in Cogent Engineering, 2022
In addition, the polyhedral model is one of the models used when performing automatic parallelization by a compiler, and performs dependency expression, analysis, scheduling, and parallelization based on algebra. However, in the polyhedral model, only the loop part that can accurately express the dependency is handled so there are some constraints when applying it, and only the simple loop parts such as matrix calculation can be handled.