z-logo
open-access-imgOpen Access
Code Generation from Simulink Models with Task and Data Parallelism
Author(s) -
P. X. Xu,
Masato Edahiro,
Masaki Kondo
Publication year - 2021
Publication title -
international journal of computer and technology
Language(s) - English
Resource type - Journals
ISSN - 2277-3061
DOI - 10.24297/ijct.v21i.9004
Subject(s) - computer science , data parallelism , parallel computing , task parallelism , parallelism (grammar) , speedup , task (project management) , code (set theory) , instruction level parallelism , code generation , scripting language , cluster analysis , computation , programming language , artificial intelligence , operating system , management , set (abstract data type) , key (lock) , economics
In this paper, we propose a method to automatically generate parallelized code from Simulink models, while exploiting both task and data parallelism. Building on previous research, we propose a model-based parallelizer (MBP) that exploits task parallelism and assigns tasks to CPU cores using a hierarchical clustering method. We also propose amethod in which data-parallel SYCL code is generated from Simulink models; computations with data parallelism are expressed in the form of S-Function Builder blocks and are executed in a heterogeneous computing environment. Most parts of the procedure can be automated with scripts, and the two methods can be applied together. In the evaluation, the data-parallel programs generated using our proposed method achieved a maximum speedup of approximately 547 times, compared to sequential programs, without observable differences in the computed results. In addition, the programs generated while exploiting both task and data parallelism were confirmed to have achieved better performance than those exploiting either one of the two.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here