DATA ALLOCATION STRATEGIES FOR PARALLEL IMAGE PROCESSING ALGORITHMS

1995 ◽  
Vol 09 (04) ◽  
pp. 615-634 ◽  
Author(s):  
VIRGINIE MARION-POTY ◽  
SERGE MIGUET
Keyword(s):  
Parallel Implementation ◽  
Image Data ◽  
Data Allocation ◽  
Load Imbalance ◽  
Parallel Image Processing ◽  
Parallel Image ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Recursive Operators ◽  
Allocation Strategies

This paper discusses several data allocation strategies used for the parallel implementation of basic imaging operators. It shows that depending on the operator (sequential or parallel, with regular or irregular execution time), the image data must be partitioned in very different manners: The square sub-domains are best adapted for minimizing the communication volume, but rectangles can perform better when we take into account the time for constructing messages. Block allocations are well adapted for inherently parallel operators since they minimize interprocessor interactions, but in the case of recursive operators, they lead to nearly sequential executions. In this framework, we show the usefulness of block-cyclic allocations. Finally, we illustrate the fact that allocating the same amount of image data to each processor can lead to severe load imbalance in the case of some operators with data-dependant execution times.

Hybrid Reconfigurable PC Add-on Card for Parallel Image Processing

2011 ◽  
Vol 110-116 ◽  
pp. 5057-5062
Author(s):  
Aadithya Ravi ◽  
Easwara E.A. Moorthy ◽  
D. Vidya ◽  
G.Mahesh Kumar
Keyword(s):  
Image Processing ◽  
Real Time ◽  
The Real ◽  
Parallel Image Processing ◽  
Parallel Image ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Specific hardware solutions are always faster than programmable architectures. But dedicated architectures have the inherent disadvantage of inflexibility. Changes in the algorithm or extensions of the application are handled easily by programmable architectures. The approach discussed here involves a hardware-software co-design to optimize on performance and programmability. The architecture houses two SHARC processors to aid in parallelizing the image processing algorithms, and a reconfigurable FPGA which may be configured on the fly to execute any of the real-time algorithms as desired. The functional memory would consist of pre-designs (FPGA based) of certain objects, each of which could be used to configure an FPGA to perform a particular function.

scholarly journals ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 333
Author(s):  
David Legland ◽  
Marie-Françoise Devaux
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
User Interface ◽  
Image Data ◽  
Image Visualization ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Spatial Calibration ◽  
User Friendly ◽  
Efficient Software

Modern imaging devices provide a wealth of data often organized as images with many dimensions, such as 2D/3D, time and channel. Matlab is an efficient software solution for image processing, but it lacks many features facilitating the interactive interpretation of image data, such as a user-friendly image visualization, or the management of image meta-data (e.g. spatial calibration), thus limiting its application to bio-image analysis. The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab environment. It provides a user-friendly visualization of multi-dimensional images, a collection of image processing algorithms and methods for analysis of images, the management of spatial calibration, and facilities for the analysis of multi-variate images. ImageM can also be run on the open source alternative software to Matlab, Octave. ImageM is freely distributed on GitHub: https://github.com/mattools/ImageM.

scholarly journals MULTI-AGENT PARALLEL IMPLEMENTATION OF PHOTOMASK SIMULATION IN PHOTOLITHOGRAPHY

2014 ◽  
pp. 45-54
Author(s):  
Syarhei M. Avakaw ◽  
Alexander A. Doudkin ◽  
Alexander V. Inyutin ◽  
Aleksey V. Otwagin ◽  
Vladislav A. Rusetsky
Keyword(s):  
Parallel Implementation ◽  
Graph Model ◽  
Computing System ◽  
Aerial Image ◽  
Image Simulation ◽  
Time Dynamic ◽  
System A ◽  
Parallel Image Processing ◽  
Parallel Image ◽  
Multi Agent

A framework for paralleling aerial image simulation in photolithography is proposed. Initial data for the simulation representing photomask are considered as a data stream that is processed by a multi-agent computing system. A parallel image processing is based on a graph model of a parallel algorithm. The algorithm is constructed from individual computing operations in a special visual editor. Then the visual representation is converted into XML, which is interpreted by the multi-agent system based on MPI. The system performs run- time dynamic optimization of calculations using an algorithm of virtual associative network. The proposed framework gives a possibility to design and analyze parallel algorithms and to adapt them to architecture of the computing cluster.

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