The potential of exploiting coarse-grain task parallelism from sequential programs

Code Generation from Simulink Models with Task and Data Parallelism

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v21i.9004 ◽

2021 ◽

Vol 21 ◽

pp. 1-13

Author(s):

Pin Xu ◽

Masato Edahiro ◽

Kondo Masaki

Keyword(s):

Hierarchical Clustering ◽

Code Generation ◽

Heterogeneous Computing ◽

Parallel Programs ◽

Data Parallelism ◽

Task Parallelism ◽

Clustering Method ◽

Computing Environment ◽

Sequential Programs ◽

Data Parallel

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.

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OPUS: HETEROGENEOUS COMPUTING WITH DATA PARALLEL TASKS

Parallel Processing Letters ◽

10.1142/s0129626499000256 ◽

1999 ◽

Vol 09 (02) ◽

pp. 275-289 ◽

Cited By ~ 3

Author(s):

ERWIN LAURE ◽

PIYUSH MEHROTRA ◽

HANS ZIMA

Keyword(s):

High Performance ◽

Heterogeneous Computing ◽

Coarse Grain ◽

Tool Support ◽

Task Parallelism ◽

Coordination Language ◽

Data Parallel ◽

High Performance Fortran ◽

Parallel Tasks ◽

Object Based

The coordination language Opus is an object-based extension of High Performance Fortran (HPF) that supports the integration of coarse-grain task parallelism with HPF-style data parallelism. In this paper we discuss Opus in the Context of multidisciplinary applications (MDAs) which execute in a heterogencous environment. After outlining the major properties of such applications and a number of different approaches towards providing language and tool support for MDAs we describe the salientfeatures of Opus and its implementation, emphasizing the issues related to the coordination of data-parallel HPF programs in a heterogencous environment.

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