Distributed Virtual System (DIVIRS) Project

As outlined in our continuation proposal 92-ISI-50R (revised) on contract NCC 2-539, we are (1) developing software, including a system manager and a job manager, that will manage available resources and that will enable programmers to program parallel applications in terms of a virtual configuration of processors, hiding the mapping to physical nodes; (2) developing communications routines that support the abstractions implemented in item one; (3) continuing the development of file and information systems based on the virtual system model; and (4) incorporating appropriate security measures to allow the mechanisms developed in items 1 through 3 to be used on an open network. The goal throughout our work is to provide a uniform model that can be applied to both parallel and distributed systems. We believe that multiprocessor systems should exist in the context of distributed systems, allowing them to be more easily shared by those that need them. Our work provides the mechanisms through which nodes on multiprocessors are allocated to jobs running within the distributed system and the mechanisms through which files needed by those jobs can be located and accessed

DIstributed VIRtual System (DIVIRS) Project

The development of Prospero moved from the University of Washington to ISI and several new versions of the software were released from ISI during the contract period. Changes in the first release from ISI included bug fixes and extensions to support the needs of specific users. Among these changes was a new option to directory queries that allows attributes to be returned for all files in a directory together with the directory listing. This change greatly improves the performance of their server and reduces the number of packets sent across their trans-pacific connection to the rest of the internet. Several new access method were added to the Prospero file method. The Prospero Data Access Protocol was designed, to support secure retrieval of data from systems running Prospero

Utilising the grid for augmented reality

Traditionally registration and tracking within Augmented Reality (AR) applications have been built around specific markers which have been added into the user’s viewpoint and allow for their position to be tracked and their orientation to be estimated in real-time. All attempts to implement AR without specific markers have increased the computational requirements and some information about the environment is still needed in order to match the registration between the real world and the virtual artifacts. This thesis describes a novel method that not only provides a generic platform for AR but also seamlessly deploys High Performance Computing (HPC) resources to deal with the additional computational load, as part of the distributed High Performance Visualization (HPV) pipeline used to render the virtual artifacts. The developed AR framework is then applied to a real world application of a marker-less AR interface for Transcranial Magnetic Stimulation (TMS), named BART (Bangor Augmented Reality for TMS). Three prototypes of BART are presented, along with a discussion of the subsequent limitations and solutions of each. First by using a proprietary tracking system it is possible to achieve accurate tracking, but with the limitations of having to use bold markers and being unable to render the virtual artifacts in real time. Second, BART v2 implements a novel tracking system using computer vision techniques. Repeatable feature points are extracted from the users view point to build a description of the object or plane that the virtual artifact is aligned with. Then as each frame is updated we use the changing position of the feature points to estimate how the object has moved. Third, the e-Viz framework is used to autonomously deploy HPV resources to ensure that the virtual objects are rendered in real-time. e-Viz also enables the allocation of remote High Performance Computing (HPC) resources to handle the computational requirements of the object tracking and pose estimation