We have designed, built, and analyzed a distributed parallel storage system that will supply image streams fast enough to permit multi-user, “real-time”, video-like applications in a wide-area ATM network-based Internet environment. We have based the implementation on user-level code in order to secure portability; we have characterized the performance bottlenecks arising from operating system and hardware issues, and based on this have optimized our design to make the best use of the available performance. Although at this time we have only operated with a few classes of data, the approach appears to be capable of providing a scalable, high-performance, and economical mechanism to provide a data storage system for several classes of data (including mixed multimedia streams), and for applications (clients) that operate in a high-speed network environment.

This work deals with the problem of finding efficient access plans for retrieving a set of pages from a multi-disk system with replicated data. This paper contains two results related to this problem: (a) We solve the problem of finding an optimal access path by transforming it into a network flow problem. We also indicate how our method may be employed in dynamic environments where some (or all) of the disks have a preexisting load, are heterogeneous, and reside on different servers. (b) We present a lower bound for the worst case response time of a request under all replication schemes, and also discuss the replication scheme that results in this lower bound. We then use simulation to show how this replication scheme can also greatly reduce the average case response time.

We describe the design and implementation of a distributed parallel storage system that uses high-speed ATM networks as a key element of the architecture. Other elements include a collection of network-based disk block servers, and an associated name server that provides some file system functionality. The implementation is based on user level software that runs on UNIX workstations. Both the architecture and the implementation are intended to provide for easy and economical scalability. This approach has yielded a data source that scales economically to very high speed. Target applications include online storage for both very large images and video sequences. This paper describes the architecture, and explores the performance issues of the current implementation.>

Describes two parallel algorithms for ranking the pixels on a curve in O (log N) time using either an EREW or CREW PRAM model. The algorithms accomplish this with N processors for a square root N* square root N image. After applying such an algorithm to an image, it is possible to move the pixels from a curve into processors having consecutive addresses. This is important because one can subsequently apply many algorithms to the curve (such as piecewise linear approximation algorithms or point in polygon tests) using segmented scan operations (i.e. parallel prefix operations). Scan operations can be executed in logarithmic time on many interconnection networks, such as hypercube, tree, butterfly, and shuffle exchange machines as well as on the EREW PRAM. The algorithms were implemented on the hypercube structured Connection Machine, and various performance tests were conducted.>

The problem of efficiently processing small data structures on massively parallel single-instruction multiple-data machines using replication methods is discussed. The problem stems from considerations of both multiresolution vision systems and focus of attention vision systems. A general framework for developing replicated algorithms, based on the four steps of embedding, distribution, decomposition, and collection, is described. A simple example is provided based on computing the histogram of a gray-level image. Replicated chain processing is discussed, and an efficient algorithm for ranking the elements in a chain in log (n) time on a concurrent write parallel random access machine is presented.>