Image Programming for Scientific Visualization by Cluster Computing

Advancement of computer usage in scientific work must be based on development of the e-science technologies. More generally e-science refers to the development of the next generation infrastructure to support computationally based science. This involves the effective utilization of distributed computing, network storage and scientific visualization resources. An enormous number of image processing software has been written for conventional desktop computers. These implement a wide range of operations, such as convolution, histogram equalization and template matching. Rendering and processing complex scientific visualization on desktop computers could ask for processing days. But the same part of job, or process if it is distributed on several nodes could be done much faster. The graphical applications usually have a tremendous potential for parallelism. In this paper distributed digital image programming visualization presented