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 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.

scholarly journals Interaction with the User in the SAPFOR System

2021 ◽  
Vol 24 (1) ◽  
pp. 157-183
Author(s):  
Никита Андреевич Катаев
Keyword(s):  
Parallel Programming ◽  
Program Transformation ◽  
Heterogeneous Computing ◽  
Programming Model ◽  
Parallel Programs ◽  
Parallel Program ◽  
Program Parallelization ◽  
Parallel Programming Model ◽  
The One ◽  
High Level

Automation of parallel programming is important at any stage of parallel program development. These stages include profiling of the original program, program transformation, which allows us to achieve higher performance after program parallelization, and, finally, construction and optimization of the parallel program. It is also important to choose a suitable parallel programming model to express parallelism available in a program. On the one hand, the parallel programming model should be capable to map the parallel program to a variety of existing hardware resources. On the other hand, it should simplify the development of the assistant tools and it should allow the user to explore the parallel program the assistant tools generate in a semi-automatic way. The SAPFOR (System FOR Automated Parallelization) system combines various approaches to automation of parallel programming. Moreover, it allows the user to guide the parallelization if necessary. SAPFOR produces parallel programs according to the high-level DVMH parallel programming model which simplify the development of efficient parallel programs for heterogeneous computing clusters. This paper focuses on the approach to semi-automatic parallel programming, which SAPFOR implements. We discuss the architecture of the system and present the interactive subsystem which is useful to guide the SAPFOR through program parallelization. We used the interactive subsystem to parallelize programs from the NAS Parallel Benchmarks in a semi-automatic way. Finally, we compare the performance of manually written parallel programs with programs the SAPFOR system builds.

Research on Data Mining Optimization and Security Based on MapReduce

2014 ◽  
Vol 631-632 ◽  
pp. 1053-1056
Author(s):  
Hui Xia
Keyword(s):  
Data Mining ◽  
Execution Time ◽  
Cluster Computing ◽  
Limited Resource ◽  
Experimental Results ◽  
Computing Environment ◽  
Cluster Systems ◽  
National Education ◽  
Distributed Cluster ◽  
Data Optimization

The paper addressed the issues of limited resource for data optimization for efficiency, reliability, scalability and security of data in distributed, cluster systems with huge datasets. The study’s experimental results predicted that the MapReduce tool developed improved data optimization. The system exhibits undesired speedup with smaller datasets, but reasonable speedup is achieved with a larger enough datasets that complements the number of computing nodes reducing the execution time by 30% as compared to normal data mining and processing. The MapReduce tool is able to handle data growth trendily, especially with larger number of computing nodes. Scaleup gracefully grows as data and number of computing nodes increases. Security of data is guaranteed at all computing nodes since data is replicated at various nodes on the cluster system hence reliable. Our implementation of the MapReduce runs on distributed cluster computing environment of a national education web portal and is highly scalable.

Architectural Pattern for Scheduling Multi-Component Applications in Distributed Systems

2016 ◽  
Vol 8 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Absalom El-Shamir Ezugwu ◽  
Marc Eduard Frincu ◽  
Sahalu Balarabe Junaidu
Keyword(s):  
Design Pattern ◽  
Heterogeneous Computing ◽  
Systems Design ◽  
General Purpose ◽  
Area Network ◽  
Challenging Problem ◽  
Wide Area Network ◽  
Computing Environment ◽  
Scheduling System ◽  
Design Goal

This paper presents a conceptual perspective on scheduling systems' design pattern for several classes of multi-component applications. The authors consider this scheduling problem in a wide-area network of heterogeneous computing environment. The heterogeneity in both the user application and distributed resource environments make this a challenging problem. In addition, the authors propose a component-based reference architectural model, which describes the design of a general purpose scheduling system targeted at the scheduling of multi-component applications. The design goal is to identify and map out the necessary ingredients required to effectively perform the scheduling of multi-component applications.

A new heuristic for task scheduling in heterogeneous computing environment

2008 ◽  
Vol 9 (12) ◽  
pp. 1715-1723 ◽  
Author(s):  
Ehsan Ullah Munir ◽  
Jian-zhong Li ◽  
Sheng-fei Shi ◽  
Zhao-nian Zou ◽  
Qaisar Rasool
Keyword(s):  
Task Scheduling ◽  
Heterogeneous Computing ◽  
Computing Environment
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