Architectures for reasoning in parallel

The research conducted has dealt with rule-based expert systems. The algorithms that may lead to effective parallelization of them were investigated. Both the forward and backward chained control paradigms were investigated in the course of this work. The best computer architecture for the developed and investigated algorithms has been researched. Two experimental vehicles were developed to facilitate this research. They are Backpac, a parallel backward chained rule-based reasoning system and Datapac, a parallel forward chained rule-based reasoning system. Both systems have been written in Multilisp, a version of Lisp which contains the parallel construct, future. Applying the future function to a function causes the function to become a task parallel to the spawning task. Additionally, Backpac and Datapac have been run on several disparate parallel processors. The machines are an Encore Multimax with 10 processors, the Concert Multiprocessor with 64 processors, and a 32 processor BBN GP1000. Both the Concert and the GP1000 are switch-based machines. The Multimax has all its processors hung off a common bus. All are shared memory machines, but have different schemes for sharing the memory and different locales for the shared memory. The main results of the investigations come from experiments on the 10 processor Encore and the Concert with partitions of 32 or less processors. Additionally, experiments have been run with a stripped down version of EMYCIN

Parallel processing and expert systems

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 1990s cannot enjoy an increased level of autonomy without the efficient implementation of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real-time demands are met for larger systems. Speedup via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial laboratories in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems is surveyed. The survey discusses multiprocessors for expert systems, parallel languages for symbolic computations, and mapping expert systems to multiprocessors. Results to date indicate that the parallelism achieved for these systems is small. The main reasons are (1) the body of knowledge applicable in any given situation and the amount of computation executed by each rule firing are small, (2) dividing the problem solving process into relatively independent partitions is difficult, and (3) implementation decisions that enable expert systems to be incrementally refined hamper compile-time optimization. In order to obtain greater speedups, data parallelism and application parallelism must be exploited

Parallel processing and expert systems

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 90's cannot enjoy an increased level of autonomy without the efficient use of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real time demands are met for large expert systems. Speed-up via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial labs in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems was surveyed. The survey is divided into three major sections: (1) multiprocessors for parallel expert systems; (2) parallel languages for symbolic computations; and (3) measurements of parallelism of expert system. Results to date indicate that the parallelism achieved for these systems is small. In order to obtain greater speed-ups, data parallelism and application parallelism must be exploited

Ada as an implementation language for knowledge based systems

Debates about the selection of programming languages often produce cultural collisions that are not easily resolved. This is especially true in the case of Ada and knowledge based programming. The construction of programming tools provides a desirable alternative for resolving the conflict

Spaceborne VHSIC multiprocessor system for AI applications

A multiprocessor system, under design for space-station applications, makes use of the latest generation symbolic processor and packaging technology. The result will be a compact, space-qualified system two to three orders of magnitude more powerful than present-day symbolic processing systems

Computer aided design

technical reportThe report is based on the proposal submitted to the National Science Foundation in September 1981, as part of the Coordinated Experimental Computer Science Research Program. The sections covering the budget and biographical data on the senior research personnel have not been included. Also, the section describing the department facilities at the time of the proposal submission is not included, because it would be only of historical interest

MARS: aRISC-based architecture for Lisp

[[abstract]]A RISC-based chip set architecture for Lisp is presented in this paper. This architecture contains an instruction fetch unit (IFU) and three processing units—integer processing unit (IPU), floating-point processing unit (FPU), and list processing unit (LPU). The IFU feeds instructions to the processing units and supports fast procedure call/return and branch, the IPU and FPU execute operations of different data type, and the LPU handles the Lisp runtime environment, dynamic type checking, and fast list access. In this architecture, the critical path of complex register file access and ALU operation is distributed into the LPU and IPU, and the tracing of a list can be done quickly by the non-delayed car or cdr instructions of the LPU. Performance simulation shows that this architecture would be about 6.2 times faster than SPUR and about 2.2 times faster than MIPS-X.[[booktype]]紙本[[booktype]]電子

Rediflow architecture prospectus

Journal ArticleRediflow is intended as a multi-function (symbolic and numeric) multiprocessor, demonstrating techniques for achieving speedup for Lisp-coded problems through the use of advanced programming concepts, high-speed communication, and dynamic load-distribution, in a manner suitable for scaling to upwards of 10,000 processors. An initial physical realization is proposed employing 16 nodes (initially in a hypercube topology), with processor, memory, and intelligent switch at each node