MODELS OF THE TASK ASSIGNMENT PROBLEM IN DISTRIBUTED SYSTEMS Mario Lucertini Dipartirnento di Informatica e Sistemistica dell'Universiti di Roma e Istituto di Analisi dei Sistemi ed Informatica del C.N.R., Viale Manzoni 30, 00185, Roma The paper presents a model for optimum partitioning of tasks over a multiple-processor system. The minimization of the interprocessor. communications overhead and/or the message average delay are considered as a design criterion. The algorithmic approaches to the problem are briefly described and improuvements to the case of multiple copies of tasks are considered. A large set of references covering the area are included.

Algorithm Design for Computer System Design

Several different computer architectures have been suggested or built to overcome the Von Neumann bottleneck. Particularly in image processing, data structures and algorithm organization may benefit from a good match with the new architectures. Such a match may be defined as the degree of exploitation of the system resources (including time) to obtain the specific solution. A diagram that shows the existing match between classes of machines, data structures, and computational structures is given in this paper. A comparison of the performance of extreme architectures on a limited number of benchmarks, chosen from the most popular IP tasks, concludes this work.

Matching the task to an image processing architecture

It has been estimated that processor speeds on the order of 1 to 100 billion operations per second will be required to solve some of the current problems in computer vision. This paper overviews the use of parallel processing techniques for various vision tasks using a parallel processing computer architecture known as PASM (partitionable SIMD/MIMD machine). PASM is a large-scale multimicroprocessor system being designed for image processing and pattern recognition. It can be dynamically reconfigured to operate as one or more independent SIMD (single instruction stream-multiple data stream) and/or MIMD (multiple instruction stream-multiple data stream) machines. This paper begins with a discussion of the computational capabilities required for com-puter vision. It is then shown how parallel processing, and in particular PASM, can be used to meet these needs.© (1982) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Parallel processing for computer vision

This paper examines some computational issues in robotics. The concept of a robot-based manufacturing cell is outlined by viewing its behavior at three levels: machine level, cell integration level, and integration with higher level functions. Computational requirements associated with the first two of these three levels are discussed, and the need for very high computation rates at the machine level of a manufacturing cell, particularly for the real-time control of a robot arm and for robot vision is pointed out. The paper also explains the need to be able to manage multiple processes at the cell integration level. A unified solution to these computational needs which requires that the computation structure used to control and manage a manufacturing cell be designed as an object-based computer architecture is then proposed. The hardware /software boundary in the implementation of the objects comprising the overall architecture is transparent from a logical viewpoint but not from a timing viewpoint. Real-time constraints are met by implementing time-critical process objects directly in hardware. This "object level" design provides the hardware /software transparency necessary to formulate a unified system specification for these real-time embedded computer systems. Timing considerations can then be used determine the hardware /software boundary.

Hardware/Software Transparency In Robotics Through Object Level Design

This project examines some parallel architectures designed for image processing, and then addresses their applicability to the problem of image segmentation by texture analysis. Using this information, and research into the structure of the human visual system, an architecture for textural segmentation is proposed. The underlying premise is that textural segmentation can be achieved by recognizing local differences in texture elements (texels). This approach differs from most of the previous work where the differences in global, second-order statistics of the image points are used as the basis for segmentation. A realistic implementation of this approach requires a parallel computing architecture which consists of a hierarchy of functionally different nodes. First, simple features are extracted from the image. Second, these simple features are linked together to form more complex texels. Finally, local and more global differences in texels or their organization are enhanced and linked into boundaries.

A Connectionist Architecture For Computing Textural Segmentation

It is estimated that systems capable of executing 1 to 100 billion operations per second will be required to achieve near-real time performance in image analysis tasks. In this paper, we present three aspects of architecture for realizing economical solutions to this problem. At the Processor-Memory-Switch (PMS) level, we consider parallel and pipeline architectures using multiprocessor and array processor systems. At the Instruction-Set-Processor (ISP) level, we consider the firmware implementation of special instruction sets for image processing. At the processor design level, we consider the design of complex Arithmetic-Logic-Units (ALU) for execution of complex image operations. Given these alternative aspects, we present hardware and software design considerations affecting the overall architecture. Together, they appear to hold the promise of several orders of magnitude improvement in speed over general purpose systems.

R. W. Hon

Papers

Computer architectures for vision