23,816 research outputs found
Expanded delta networks for very large parallel computers
In this paper we analyze a generalization of the traditional delta network, introduced by Patel [21], and dubbed Expanded Delta Network (EDN). These networks provide in general multiple paths that can be exploited to reduce contention in the network resulting in increased performance. The crossbar and traditional delta networks are limiting cases of this class of networks. However, the delta network does not provide the multiple paths that the more general expanded delta networks provide, and crossbars are to costly to use for large networks. The EDNs are analyzed with respect to their routing capabilities in the MIMD and SIMD models of computation.The concepts of capacity and clustering are also addressed. In massively parallel SIMD computers, it is the trend to put a larger number processors on a chip, but due to I/O constraints only a subset of the total number of processors may have access to the network. This is introduced as a Restricted Access Expanded Delta Network of which the MasPar MP-1 router network is an example
A Hybrid Decomposition Parallel Implementation of the Car-Parrinello Method
We have developed a flexible hybrid decomposition parallel implementation of
the first-principles molecular dynamics algorithm of Car and Parrinello. The
code allows the problem to be decomposed either spatially, over the electronic
orbitals, or any combination of the two. Performance statistics for 32, 64, 128
and 512 Si atom runs on the Touchstone Delta and Intel Paragon parallel
supercomputers and comparison with the performance of an optimized code running
the smaller systems on the Cray Y-MP and C90 are presented.Comment: Accepted by Computer Physics Communications, latex, 34 pages without
figures, 15 figures available in PostScript form via WWW at
http://www-theory.chem.washington.edu/~wiggs/hyb_figures.htm
New acceleration technique for the backpropagation algorithm
Artificial neural networks have been studied for many years in the hope of achieving human like performance in the area of pattern recognition, speech synthesis and higher level of cognitive process. In the connectionist model there are several interconnected processing elements called the neurons that have limited processing capability. Even though the rate of information transmitted between these elements is limited, the complex interconnection and the cooperative interaction between these elements results in a vastly increased computing power; The neural network models are specified by an organized network topology of interconnected neurons. These networks have to be trained in order them to be used for a specific purpose. Backpropagation is one of the popular methods of training the neural networks. There has been a lot of improvement over the speed of convergence of standard backpropagation algorithm in the recent past. Herein we have presented a new technique for accelerating the existing backpropagation without modifying it. We have used the fourth order interpolation method for the dominant eigen values, by using these we change the slope of the activation function. And by doing so we increase the speed of convergence of the backpropagation algorithm; Our experiments have shown significant improvement in the convergence time for problems widely used in benchmarKing Three to ten fold decrease in convergence time is achieved. Convergence time decreases as the complexity of the problem increases. The technique adjusts the energy state of the system so as to escape from local minima
Synchronization Landscapes in Small-World-Connected Computer Networks
Motivated by a synchronization problem in distributed computing we studied a
simple growth model on regular and small-world networks, embedded in one and
two-dimensions. We find that the synchronization landscape (corresponding to
the progress of the individual processors) exhibits Kardar-Parisi-Zhang-like
kinetic roughening on regular networks with short-range communication links.
Although the processors, on average, progress at a nonzero rate, their spread
(the width of the synchronization landscape) diverges with the number of nodes
(desynchronized state) hindering efficient data management. When random
communication links are added on top of the one and two-dimensional regular
networks (resulting in a small-world network), large fluctuations in the
synchronization landscape are suppressed and the width approaches a finite
value in the large system-size limit (synchronized state). In the resulting
synchronization scheme, the processors make close-to-uniform progress with a
nonzero rate without global intervention. We obtain our results by ``simulating
the simulations", based on the exact algorithmic rules, supported by
coarse-grained arguments.Comment: 20 pages, 22 figure
Managing the outsourcing of information security processes: the 'cloud' solution
Information security processes and systems are relevant for any organization and involve medium-to-high investment; however, the current economic downturn is causing a dramatic reduction in spending on Information Technology (IT). Cloud computing (i.e., externalization of one or more IT services) might be a solution for organizations keen to maintain a good level of security. In this paper we discuss whether cloud computing is a valid alternative to in-house security processes and systems drawing on four mini-case studies of higher education institutions in New England, US. Our findings show that the organizationās IT spending capacity affects the choice to move to the cloud; however, the perceived security of the cloud and the perceived in-house capacity to provide high quality IT (and security) services moderate this relationship. Moreover, other variables such as (low) quality of technical support, relatively incomplete contracts, poor defined Service License Agreements (SLA), and ambiguities over data ownership affect the choice to outsource IT (and security) using the cloud. We suggest that, while cloud computing could be a useful means of IT outsourcing, there needs to be a number of changes and improvements to how the service is currently delivered
A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines
Information in neural networks is represented as weighted connections, or
synapses, between neurons. This poses a problem as the primary computational
bottleneck for neural networks is the vector-matrix multiply when inputs are
multiplied by the neural network weights. Conventional processing architectures
are not well suited for simulating neural networks, often requiring large
amounts of energy and time. Additionally, synapses in biological neural
networks are not binary connections, but exhibit a nonlinear response function
as neurotransmitters are emitted and diffuse between neurons. Inspired by
neuroscience principles, we present a digital neuromorphic architecture, the
Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex
synaptic response functions without requiring additional hardware components.
We consider the paradigm of spiking neurons with temporally coded information
as opposed to non-spiking rate coded neurons used in most neural networks. In
this paradigm we examine liquid state machines applied to speech recognition
and show how a liquid state machine with temporal dynamics maps onto the
STPU-demonstrating the flexibility and efficiency of the STPU for instantiating
neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN
Automating security monitoring and analysis for Space Station Freedom's electric power system
Operating a large, space power system requires classifying the system's status and analyzing its security. Conventional algorithms are used by terrestrial electric utilities to provide such information to their dispatchers, but their application aboard Space Station Freedom will consume too much processing time. A new approach for monitoring and analysis using adaptive pattern techniques is presented. This approach yields an on-line security monitoring and analysis algorithm that is accurate and fast; and thus, it can free the Space Station Freedom's power control computers for other tasks
Deep Space Network information system architecture study
The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control
Datalog as a parallel general purpose programming language
The increasing available parallelism of computers demands new programming languages that make parallel programming dramatically easier and less error prone. It is proposed that datalog with negation and timestamps is a suitable basis for a general purpose programming language for sequential, parallel and distributed computers.
This paper develops a fully incremental bottom-up interpreter for datalog that supports a wide range of execution strategies, with trade-offs affecting efficiency, parallelism and control of resource usage. Examples show how the language can accept real-time external inputs and outputs, and mimic assignment, all without departing from its pure logical semantics
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