Block-based programming abstractions for explicit parallel computing

2015 ◽  
Author(s):  
Annette Feng ◽  
Eli Tilevich ◽  
Wu-chun Feng
Keyword(s):  
Parallel Computing ◽  
Programming Abstractions ◽  
Block Based

Parallel Computing in Optimization

1998 ◽  
Vol 49 (7) ◽  
pp. 770-771
Author(s):  
V J Rayward-Smith
Keyword(s):  
Parallel Computing

A Structure Learning Method for Block-Based Neural Networks

2013 ◽  
Vol 133 (10) ◽  
pp. 1976-1982 ◽  
Author(s):  
Hidetaka Watanabe ◽  
Seiichi Koakutsu ◽  
Takashi Okamoto ◽  
Hironori Hirata
Keyword(s):  
Neural Networks ◽  
Structure Learning ◽  
Learning Method ◽  
Block Based

Block-Based Incremental Caching for Information Centric Networking

2016 ◽  
Vol E99.B (12) ◽  
pp. 2550-2558
Author(s):  
Sung-Hwa LIM ◽  
Yeo-Hoon YOON ◽  
Young-Bae KO ◽  
Huhnkuk LIM
Keyword(s):  
Information Centric Networking ◽  
Block Based

Matlab and Parallel Computing

2012 ◽  
Vol 17 (4) ◽  
pp. 207-216 ◽  
Author(s):  
Magdalena Szymczyk ◽  
Piotr Szymczyk
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Parallel Computing ◽  
Distributed Computing ◽  
Control Systems ◽  
High Performance ◽  
Parallel Applications ◽  
Process Simulations ◽  
Key Features ◽  
Financial Process

Abstract The MATLAB is a technical computing language used in a variety of fields, such as control systems, image and signal processing, visualization, financial process simulations in an easy-to-use environment. MATLAB offers "toolboxes" which are specialized libraries for variety scientific domains, and a simplified interface to high-performance libraries (LAPACK, BLAS, FFTW too). Now MATLAB is enriched by the possibility of parallel computing with the Parallel Computing ToolboxTM and MATLAB Distributed Computing ServerTM. In this article we present some of the key features of MATLAB parallel applications focused on using GPU processors for image processing.

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