Biological neural networks as model systems for designing future parallel processing computers

One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence

Continuum computer architecture for exaflops computation

The ultimate computers in our long-term future will deliver exaflops-scale performance (or greater) and will look very different from today’s micro-processors and massively parallel computers. Ironically, however, their alien structures and operational behavior can be inferred from the same technology trends driving development of today’s conventional computing systems

Concurrent Viola Jones classifiers on a portable Beowulf cluster : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Mechatronics at Massey University

Real-time Computer Vision is an interesting application for supercomputing, real-time applications (vision processing in particular) employ special purpose hardware such as DSPs to achieve high performance. This thesis explores parallel computers particularly commodity general purpose hardware. We also build a prototype to better understand the economics of supercomputing, specifically related to mobile computing - low power, rugged design by building a mobile computer. A new communication layer is built, where by the nature of the locality of the nodes allows one to optimise the protocols to reduce the latency comparably. Finally a study and in depth results of the algorithm, the Viola Jones Object detector in parallel are presented followed by reflection and future work based on the current results and platform

NAS Requirements Checklist for Job Queuing/Scheduling Software

The increasing reliability of parallel systems and clusters of computers has resulted in these systems becoming more attractive for true production workloads. Today, the primary obstacle to production use of clusters of computers is the lack of a functional and robust Job Management System for parallel applications. This document provides a checklist of NAS requirements for job queuing and scheduling in order to make most efficient use of parallel systems and clusters for parallel applications. Future requirements are also identified to assist software vendors with design planning

Self-assembly of parallel atomic wires and periodic clusters of silicon on a vicinal Si(111) surface

Silicon self-assembly at step edges in the initial stage of homoepitaxial growth on a vicinal Si(111) surface is studied by scanning tunneling microscopy (STM). The resulting atomic structures change dramatically from a parallel array of 0.7 nm wide wires to one dimensionally aligned periodic clusters of the diameter ~ 2 nm and periodicity 2.7 nm in the very narrow range of growth temperatures between 400 and 300 C. These nanostructures are expected to play an important role in future development of silicon quantum computers. Mechanisms leading to such distinct structures are discussed.Comment: Accepted for publication in Phys. Rev. Lett. Numbers of pages and figures are 13 and 3, respectivel

R/parallel – speeding up bioinformatics analysis with R

Background: R is the preferred tool for statistical analysis of many bioinformaticians due in part to the increasing number of freely available analytical methods. Such methods can be quickly reused and adapted to each particular experiment. However, in experiments where large amounts of data are generated, for example using high-throughput screening devices, the processing time required to analyze data is often quite long. A solution to reduce the processing time is the use of parallel computing technologies. Because R does not support parallel computations, several tools have been developed to enable such technologies. However, these tools require multiple modications to the way R programs are usually written or run. Although these tools can finally speed up the calculations, the time, skills and additional resources required to use them are an obstacle for most bioinformaticians. Results: We have designed and implemented an R add-on package, R/parallel, that extends R by adding user-friendly parallel computing capabilities. With R/parallel any bioinformatician can now easily automate the parallel execution of loops and benefit from the multicore processor power of today's desktop computers. Using a single and simple function, R/parallel can be integrated directly with other existing R packages. With no need to change the implemented algorithms, the processing time can be approximately reduced N-fold, N being the number of available processor cores. Conclusion: R/parallel saves bioinformaticians time in their daily tasks of analyzing experimental data. It achieves this objective on two fronts: first, by reducing development time of parallel programs by avoiding reimplementation of existing methods and second, by reducing processing time by speeding up computations on current desktop computers. Future work is focused on extending the envelope of R/parallel by interconnecting and aggregating the power of several computers, both existing office computers and computing clusters.

Performance of a parallel code for the Euler equations on hypercube computers

The performance of hypercubes were evaluated on a computational fluid dynamics problem and the parallel environment issues were considered that must be addressed, such as algorithm changes, implementation choices, programming effort, and programming environment. The evaluation focuses on a widely used fluid dynamics code, FLO52, which solves the two dimensional steady Euler equations describing flow around the airfoil. The code development experience is described, including interacting with the operating system, utilizing the message-passing communication system, and code modifications necessary to increase parallel efficiency. Results from two hypercube parallel computers (a 16-node iPSC/2, and a 512-node NCUBE/ten) are discussed and compared. In addition, a mathematical model of the execution time was developed as a function of several machine and algorithm parameters. This model accurately predicts the actual run times obtained and is used to explore the performance of the code in interesting but yet physically realizable regions of the parameter space. Based on this model, predictions about future hypercubes are made

Parallel Computers and Complex Systems

We present an overview of the state of the art and future trends in high performance parallel and distributed computing, and discuss techniques for using such computers in the simulation of complex problems in computational science. The use of high performance parallel computers can help improve our understanding of complex systems, and the converse is also true --- we can apply techniques used for the study of complex systems to improve our understanding of parallel computing. We consider parallel computing as the mapping of one complex system --- typically a model of the world --- into another complex system --- the parallel computer. We study static, dynamic, spatial and temporal properties of both the complex systems and the map between them. The result is a better understanding of which computer architectures are good for which problems, and of software structure, automatic partitioning of data, and the performance of parallel machines

Report from the MPP Working Group to the NASA Associate Administrator for Space Science and Applications

NASA's Office of Space Science and Applications (OSSA) gave a select group of scientists the opportunity to test and implement their computational algorithms on the Massively Parallel Processor (MPP) located at Goddard Space Flight Center, beginning in late 1985. One year later, the Working Group presented its report, which addressed the following: algorithms, programming languages, architecture, programming environments, the way theory relates, and performance measured. The findings point to a number of demonstrated computational techniques for which the MPP architecture is ideally suited. For example, besides executing much faster on the MPP than on conventional computers, systolic VLSI simulation (where distances are short), lattice simulation, neural network simulation, and image problems were found to be easier to program on the MPP's architecture than on a CYBER 205 or even a VAX. The report also makes technical recommendations covering all aspects of MPP use, and recommendations concerning the future of the MPP and machines based on similar architectures, expansion of the Working Group, and study of the role of future parallel processors for space station, EOS, and the Great Observatories era