scholarly journals Code Generation from Simulink Models with Task and Data Parallelism

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.

EXTENDING OPENMP FOR TASK PARALLELISM

2003 ◽  
Vol 13 (03) ◽  
pp. 341-352 ◽  
Author(s):  
AMI MAROWKA
Keyword(s):  
Parallel Programs ◽  
Parallel Applications ◽  
Task Graph ◽  
Data Parallelism ◽  
Task Parallelism ◽  
Design Decision ◽  
Parallel Application ◽  
Additional Degree ◽  
Data Parallel ◽  
Precedence Relations

In a wide variety of scientific parallel applications, both task and data parallelism must be exploited to achieve the best possible performance on a multiprocessor machine. These applications induce task-graph parallelism with coarse-grain granularity. Nevertheless, using the available task-graph parallelism and combining it with data parallelism can increase the performance of parallel applications considerably since an additional degree of parallelism is exploited. The OpenMP standard supports data parallelism but does not support task-graph parallelism. In this paper we present an integration of task-graph parallelism in OpenMP by extending the parallel sections constructs to include task-index and precedence-relations matrix clauses. There are many ways in which task-graph parallelism can be supported in a programming environment. A fundamental design decision is whether the programmer has to write programs with explicit precedence relations, or if the responsibility of precedence relations generation is delegated to the compiler. One of the benefits provided by parallel programming models like OpenMP is that they liberate the programmer from dealing with the underlying details of communication and synchronization, which are cumbersome and error-prone tasks. If task-graph parallelism is to find acceptance, writing task-graph parallel programs must be no harder than writing data parallel programs, and therefore, in our design, precedence relations are described through simple programmer annotations, with implementation details handled by the system. This paper concludes with a description of several parallel application kernels that were developed to study the practical aspects of task-graph parallelism in OpenMP. The examples demonstrate that exploiting data and task parallelism in a single framework is the key to achieving good performance in a variety of applications.

scholarly journals Machine and Collection Abstractions for User-Implemented Data-Parallel Programming

2000 ◽  
Vol 8 (4) ◽  
pp. 231-246 ◽  
Author(s):  
Magne Haveraaen
Keyword(s):  
Data Structures ◽  
Commercial Application ◽  
Data Parallelism ◽  
Task Parallelism ◽  
Data Parallel ◽  
Full Control ◽  
High Level ◽  
Intensive Programs ◽  
Parallel Distribution ◽  
Sequential Implementation

Data parallelism has appeared as a fruitful approach to the parallelisation of compute-intensive programs. Data parallelism has the advantage of mimicking the sequential (and deterministic) structure of programs as opposed to task parallelism, where the explicit interaction of processes has to be programmed. In data parallelism data structures, typically collection classes in the form of large arrays, are distributed on the processors of the target parallel machine. Trying to extract distribution aspects from conventional code often runs into problems with a lack of uniformity in the use of the data structures and in the expression of data dependency patterns within the code. Here we propose a framework with two conceptual classes, Machine and Collection. The Machine class abstracts hardware communication and distribution properties. This gives a programmer high-level access to the important parts of the low-level architecture. The Machine class may readily be used in the implementation of a Collection class, giving the programmer full control of the parallel distribution of data, as well as allowing normal sequential implementation of this class. Any program using such a collection class will be parallelisable, without requiring any modification, by choosing between sequential and parallel versions at link time. Experiments with a commercial application, built using the Sophus library which uses this approach to parallelisation, show good parallel speed-ups, without any adaptation of the application program being needed.

OPUS: HETEROGENEOUS COMPUTING WITH DATA PARALLEL TASKS

1999 ◽  
Vol 09 (02) ◽  
pp. 275-289 ◽  
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.

scholarly journals Determining the Execution Time Distribution for a Data Parallel Program in a Heterogeneous Computing Environment

1997 ◽  
Vol 44 (1) ◽  
pp. 35-52 ◽  
Author(s):  
Yan Alexander Li ◽  
John K. Antonio ◽  
Howard Jay Siegel ◽  
Min Tan ◽  
Daniel W. Watson
Keyword(s):  
Execution Time ◽  
Time Distribution ◽  
Heterogeneous Computing ◽  
Parallel Program ◽  
Computing Environment ◽  
Data Parallel

scholarly journals A hierarchical clustering method for random intervals based on a similarity measure

2021 ◽  
Author(s):  
Ana Belén Ramos-Guajardo
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Real Life ◽  
Stopping Criterion ◽  
Clustering Method ◽  
Bootstrap Test ◽  
Empirical Performance ◽  
Random Intervals ◽  
Expected Values

AbstractA new clustering method for random intervals that are measured in the same units over the same group of individuals is provided. It takes into account the similarity degree between the expected values of the random intervals that can be analyzed by means of a two-sample similarity bootstrap test. Thus, the expectations of each pair of random intervals are compared through that test and a p-value matrix is finally obtained. The suggested clustering algorithm considers such a matrix where each p-value can be seen at the same time as a kind of similarity between the random intervals. The algorithm is iterative and includes an objective stopping criterion that leads to statistically similar clusters that are different from each other. Some simulations to show the empirical performance of the proposal are developed and the approach is applied to two real-life situations.

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