一个VLIW体系结构的单片多处理机

本介绍一个采用ＶＬＩＷ超长指令字体系结构的高性能单片多处理机，在这个体系结构中采用流水寄存器堆来消除循环程序内的数据相关，从而使程序能够在指令级以极高的并行度并行运行。模拟实验结果表明这个体系结构具有很高的运算速度和很好的性能价格比。     

一个支持多分支循环最优执行的VLIW体系结构

本首先提出一个能够支持多分支循环程序最优执行的ＶＬＩＷ体系结构模型，然后在这个模型的基础上设计了一个新的主要用于数字信号处理及图象处理应用领域的单片体系结构－ＵＲＰＲ－２。在这个体系结构中，属于不同路径和不同循环体的多个分支操作可以在一个节拍内同时被执行，因此可以在更大范围内开发指讼级并行性，同时还提出了一个种叫作流水控制黑板的机制来支持条件分支操作。ＵＲＰＲ－２不仅能够以很高的速度执行只含有基     

A Tale of Two Classifier Systems

This paper describes two classifier systems that learn. These are rule-based systems that use genetic algorithms, which are based on an analogy with natural selection and genetics, as their principal learning mechanism, and an economic model as their principal mechanism for apportioning credit. CFS-C is a domain-independent learning system that has been widely tested on serial computers. CFS is a parallel implementation of CFS-C that makes full use of the inherent parallelism of classifier systems and genetic algorithms, and that allows the exploration of large-scale tasks that were formerly impractical. As with other approaches to learning, classifier systems in their current form work well for moderately-sized tasks but break down for larger tasks. In order to shed light on this issue, we present several empirical studies of known issues in classifier systems, including the effects of population size, the actual contribution of genetic algorithms, the use of rule chaining in solving higher-order tasks, and issues of task representation and dynamic population convergence. We conclude with a discussion of some major unresolved issues in learning classifier systems and some possible approaches to making them more effective on complex tasks.     

Experiments in the Parallel Computation of 3D Convex Hulls

Two parallel implementations of a 3D convex hull algorithm are reported. The paper considers a MIMD distributed memory architecture and the implementations are carried out on the Meiko Computing Surface using T800 transputers and the programming languages Occam and C. The first method uses a simple parallel geometric decomposition strategy and produces encouraging results. With the second approach a parallel generic Divide-and-Conquer kernel is incorporated. This is an example of the algorithmic skeleton approach to parallel programming and involves run-time, dynamic allocation of work to processors. The resulting performances for both methods are measured and compared.     

Data management scheme for parallel radiosity

The radiosity method is a very demanding process in terms of computing and memory resources. To cope with these problems, parallel solutions have been proposed in the literature. A new parallel solution, based on the use of a shared virtual memory, is proposed. It will be shown that this concept of SVM greatly simplifies the implementation of a parallel algorithm since distributed data are managed by the operating system. This new parallel radiosity algorithm has been implemented on an iPSC/2 hypercube using the SVM. The first results obtained with this algorithm are encouraging since the calculated efficiency curve is nearly linear.     

Image processing with VLSI

The development and implementation of systems for the more complex realtime image processing and scene understanding tasks, such as robot vision and remote surveillance, calls for faster computation than that possible using the traditional serial computer. The advent of VLSI has made feasible the consideration of more specialized processing architectures, designed to support these datarates, while keeping systems compact and relatively cheap. Two approaches are discussed: the use of a programmable processor array, and the customizing of image processing algorithms in silicon. This paper examines designs based upon each approach in the light of the techniques and constraints of VLSI. In particular we describe in some detail an example of a VLSI parallel array processor, the Grid (GEC rectangular image and data processor), and a number of special-purpose CMOS/SOS chips based on systolic design techniques.     

Three solutions for a robot arm controller using Pascal-Plus,occam and edison

Three currently available concurrent language systems, Pascal-Plus, occam and Edison, are used to implement a controller for a robot arm. The robot arm allows real parallelism of operation within the movements of the arm. The feasibility and restrictions placed upon the resultant solution for each of the language systems is then analysed and discussed. A Petri-net solution is also presented for the generalized problem and it is shown that each of the solutions is a different folding of the general net.     

Parallel context-sensitive compilation

The searching power of massively parallel associative computers is an under used and under investigated capability that can be used to facilitate software development. This paper describes the development of a context sensitive compiler for pattern-matching languages using that searching power. The described compiler was implemented on the STARAN parallel computer and the compiled OPS5 programs were also executed on the STARAN obtaining an estimated throughput of 6000 rules per second. The described compilation of production rules into equivalent procedural rules is completely data parallel, with the degree of parallelism depending on the number of tokens in the program being compiled. During any one step of the context-sensitive analysis, the entire program is processed in constant time.     

Observer-invariant histopathology using genetics-based machine learning

Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types—a key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique——based on an incremental genetics-based rule learner. exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented operations. Results demonstrate that is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations as efficient and powerful tools for biomedical image processing.