The Parallel Computational Environment for Imaging Science, PiCEIS is an image processing code designed for efficient execution on massively parallel computers. Through effective use of the aggregate resources of such computers PiCEIS enables much larger and more accurate production processing using existing off the shelf hardware. A goal of PiCEIS is to decrease the difficulty of writing scalable parallel programs and reduce the time to add new functionalities. In part this is accomplished by the PiCEIS architecture, its ability to easily add additional modules, and also through the use of a non-uniform memory access (NUMA) programming model based upon one-sided access to distributed shared memory. In this paper we briefly describe the PiCEIS architecture, our NUMA programming tools, and examine some typical techniques and algorithms.
A framework for parallel visualization at Pacific Northwest National Laboratory (PNNL) is being developed that utilizes the IBM Scaleable Graphics Engine (SGE) and IBM SP parallel computers. Parallel visualization resources are discussed, including display technologies, data handling, rendering, and interactivity. Several of these resources have been developed, while others are under development. These framework resources will be utilized by programmers in custom parallel visualization applications.