QUALITY AND COMPLEXITY BOUNDS OF LOAD BALANCING ALGORITHMS FOR PARALLEL IMAGE PROCESSING

2000 ◽  
Vol 14 (04) ◽  
pp. 463-476 ◽  
Author(s):  
SERGE MIGUET ◽  
JEAN-MARC PIERSON
Keyword(s):  
Image Processing ◽  
Parallel Implementation ◽  
Data Distribution ◽  
Complexity Bounds ◽  
Distribution Strategy ◽  
Optimal Partitions ◽  
Parallel Image Processing ◽  
Parallel Image ◽  
The Family

The parallel implementation of image processing algorithms implies an important choice of data distribution strategy. In order to handle the specific constraints associated with images, data distribution must take into account not only the locality of the data and its geometrical regularity but also the possible irregular computation costs associated with different image elements. A widely studied field to tackle this problem is the family of methods related to rectilinear partitioning. We introduce two fully parallel heuristics that compute suboptimal partitions, with a better complexity than the best known algorithms that compute optimal partitions. In this paper, we compare our heuristics to an optimal partitioning, both in terms of execution time and accuracy of the partition. We give some theoretical bounds on the quality of these heuristics that are corroborated by results of random numerical experiments and real applications.

Computational Benefits, Limitations and Techniques of Parallel Image Processing

2020 ◽  
pp. 1-9
Author(s):  
Maha Heng Li ◽  
Marinka Yu Zhang
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Parallel Computing ◽  
Comparative Evaluation ◽  
Parallel Imaging ◽  
Feature Classification ◽  
Parallel Image Processing ◽  
Parallel Image

The main purpose of digitalized image processing is to enhance the quality of images and subsequently to facilitate the process of feature classification and extraction. This process is effectively utilized in healthcare imaging, computer visioning, astronomy, meteorology, and remote sensing among other essential fields. The main issue is that this technique takes a lot of time even though it provides convenient and efficient means of addressing the prevailing issue. In this paper, we shall provide a comparative evaluation of the present contributions adding to the application of parallel image processing including their limitations and benefits. Another key segment of research in this paper is to evaluate parallel computing, presently available techniques, tools and architecture in different image processing applications. As such, this paper is purposed to critically evaluate the role of parallel imaging in the field of healthcare imaging.

GPU processing for parallel image processing and real-time object recognition

2014 ◽  
Author(s):  
Kevin Vincent ◽  
Damien Nguyen ◽  
Brian Walker ◽  
Thomas Lu ◽  
Tien-Hsin Chao
Keyword(s):  
Image Processing ◽  
Object Recognition ◽  
Real Time ◽  
Parallel Image Processing ◽  
Parallel Image ◽  
Gpu Processing

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.

Sign in / Sign up

Export Citation Format

Share Document