108 research outputs found
Parallel backpropagation neural networks forTask allocation by means of PVM
Features such as fast response, storage efficiency, fault tolerance and graceful degradation in face of scarce or spurious inputs make neural networks appropriate tools for Intelligent Computer Systems. A neural network is, by itself, an inherently parallel system where many, extremely simple, processing units work simultaneously in the same problem building up a computational device which possess adaptation (learning) and generalisation (recognition) abilities. Implementation of neural networks roughly involve at least three stages; design, training and testing. The second, being CPU intensive, is the one requiring most of the processing resources and depending on size and structure complexity the learning process can be extremely long. Thus, great effort has been done to develop parallel implementations intended for a reduction of learning time.
Pattern partitioning is an approach to parallelise neural networks where the whole net is replicated in different processors and the weight changes owing to diverse training patterns are parallelised. This approach is the most suitable for a distributed architecture such as the one considered here. Incoming task allocation, as a previous step, is a fundamental service aiming for improving distributed system performance facilitating further dynamic load balancing. A Neural Network Device inserted into the kernel of a distributed system as an intelligent tool, allows to achieve automatic allocation of execution requests under some predefined performance criteria based on resource availability and incoming process requirements.
This paper being, a twofold proposal, shows firstly, some design and implementation insights to build a system where decision support for load distribution is based on a neural network device and secondly a distributed implementation to provide parallel learning of neural networks using a pattern partitioning approach.
In the latter case, some performance results of the parallelised approach for learning of backpropagation neural networks, are shown. This include a comparison of recall and generalisation abilities and speed-up when using a socket interface or PVM.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI
Submicron Systems Architecture Project: Semiannual Technial Report
No abstract available
Submicron Systems Architecture Project : Semiannual Technical Report
The Mosaic C is an experimental fine-grain multicomputer
based on single-chip nodes. The Mosaic C chip includes 64KB of fast dynamic RAM,
processor, packet interface, ROM for bootstrap and self-test, and a two-dimensional selftimed
router. The chip architecture provides low-overhead and low-latency handling of
message packets, and high memory and network bandwidth. Sixty-four Mosaic chips are
packaged by tape-automated bonding (TAB) in an 8 x 8 array on circuit boards that can, in
turn, be arrayed in two dimensions to build arbitrarily large machines. These 8 x 8 boards are
now in prototype production under a subcontract with Hewlett-Packard. We are planning
to construct a 16K-node Mosaic C system from 256 of these boards. The suite of Mosaic
C hardware also includes host-interface boards and high-speed communication cables. The
hardware developments and activities of the past eight months are described in section 2.1.
The programming system that we are developing for the Mosaic C is based on the
same message-passing, reactive-process, computational model that we have used with earlier
multicomputers, but the model is implemented for the Mosaic in a way that supports finegrain
concurrency. A process executes only in response to receiving a message, and may in
execution send messages, create new processes, and modify its persistent variables before
it either exits or becomes dormant in preparation for receiving another message. These
computations are expressed in an object-oriented programming notation, a derivative of
C++ called C+-. The computational model and the C+- programming notation are
described in section 2.2. The Mosaic C runtime system, which is written in C+-, provides
automatic process placement and highly distributed management of system resources. The
Mosaic C runtime system is described in section 2.3
Parallel backpropagation neural networks forTask allocation by means of PVM
Features such as fast response, storage efficiency, fault tolerance and graceful degradation in face of scarce or spurious inputs make neural networks appropriate tools for Intelligent Computer Systems. A neural network is, by itself, an inherently parallel system where many, extremely simple, processing units work simultaneously in the same problem building up a computational device which possess adaptation (learning) and generalisation (recognition) abilities. Implementation of neural networks roughly involve at least three stages; design, training and testing. The second, being CPU intensive, is the one requiring most of the processing resources and depending on size and structure complexity the learning process can be extremely long. Thus, great effort has been done to develop parallel implementations intended for a reduction of learning time.
Pattern partitioning is an approach to parallelise neural networks where the whole net is replicated in different processors and the weight changes owing to diverse training patterns are parallelised. This approach is the most suitable for a distributed architecture such as the one considered here. Incoming task allocation, as a previous step, is a fundamental service aiming for improving distributed system performance facilitating further dynamic load balancing. A Neural Network Device inserted into the kernel of a distributed system as an intelligent tool, allows to achieve automatic allocation of execution requests under some predefined performance criteria based on resource availability and incoming process requirements.
This paper being, a twofold proposal, shows firstly, some design and implementation insights to build a system where decision support for load distribution is based on a neural network device and secondly a distributed implementation to provide parallel learning of neural networks using a pattern partitioning approach.
In the latter case, some performance results of the parallelised approach for learning of backpropagation neural networks, are shown. This include a comparison of recall and generalisation abilities and speed-up when using a socket interface or PVM.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI
Parallel mapping and circuit partitioning heuristics based on mean field annealing
Ankara : Department of Computer Engineering and Information Science and the Institute of Engineering and Science of Bilkent University, 1992.Thesis (Master's) -- Bilkent University, 1992.Includes bibliographical references.Moan Field Annealinp; (MFA) aJgoritlim, receñí,ly proposc'd for solving com
binatorial optimization problems, combines the characteristics of nenral networks
and simulated annealing. In this thesis, MFA is formulated for tlie
mapping i)roblcm and the circuit partitioning problem. EHicient implementation
schemes, which decrease the complexity of the proposed algorithms by
asymptotical factors, are also given. Perlormances of the proposed MFA algorithms
are evaluated in comparison with two well-known heuristics: simulated
annealing and Kernighan-Lin. Results of the experiments indicate that MFA
can be used as an alternative heuristic for the mapping problem and the circuit
partitioning problem. Inherent parallelism of the MFA is exploited by
designing efficient parallel algorithms for the proposed MFA heuristics. Parallel
MFA algorithms proposed for solving the circuit partitioning problem are
implemented on an iPS(J/2’ hypercube multicompute.r. Experimental results
show that the proposed heuristics can be efficiently parallelized, which is crucial
for algorithms that solve such computationally hard problems.Bultan, TevfikM.S
An Application Perspective on High-Performance Computing and Communications
We review possible and probable industrial applications of HPCC focusing on the software and hardware issues. Thirty-three separate categories are illustrated by detailed descriptions of five areas -- computational chemistry; Monte Carlo methods from physics to economics; manufacturing; and computational fluid dynamics; command and control; or crisis management; and multimedia services to client computers and settop boxes. The hardware varies from tightly-coupled parallel supercomputers to heterogeneous distributed systems. The software models span HPF and data parallelism, to distributed information systems and object/data flow parallelism on the Web. We find that in each case, it is reasonably clear that HPCC works in principle, and postulate that this knowledge can be used in a new generation of software infrastructure based on the WebWindows approach, and discussed in an accompanying paper
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