Abstract
It has been clear for many years that parallelism is the cornerstone of future computing. What has been much less clear is how to achieve high degrees of parallelism in a practical way. In the standard formulation there is an inherent dilemma facing the designers and users of highly parallel systems. It is relatively easy and efficient to build loosely coupled systems where each processor works exclusively on local data. But this is exactly the kind of system that has proven most difficult to program, except in some special problems that naturally separate. Much of the current research effort in computer systems is concerned with ways of efficiently providing the illusion of shared memory in a distributed machine [TSF90].
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References
Alnaes, K., Gustavson, D., James, D., and Kristiansen, E., “Scalable Coherent Interface,” CompEuro 90, Tel Aviv, Isael, May 7–9, 1990.
Bilmes, J., and Kohn, P., “Ring Array Processor (RAP): Software Architecture,” Technical Report TR-90-050, International Computer Science Institute, Berkeley, CA, 1990.
Culler, D., Sah, A., Schauser, K., von Eicken, T., Wawrzynek, J., “Fine-grain Parallelism with Minimal Hardware Support: A Compiler-Controlled Threaded Abstract Machine,” EECS Dept., U., California at Berkeley, CA.
D’Autrechy, C.L., et al., “A general-purpose simulation environment for developing connectionist models,” Simulation, 51, 1, pp. 5–19.
Feldman, J.A., et al., “Computing with Structured Connectionist Networks,” CACM 31, 2, pp. 170–187.
Goddard, N., The Rochester Connectionist Simulator: User Manual, TR, U. Rochester.
Hammerstrom, D., “A VLSI Architecture for High-Performance, Low-Cost, On-chip Learning,” Proc. of IJCNN, San Diego, June.
Hermansky, H., “Perceptual Linear Predictive (PLP) Analysis of Speech,” J. Acoust. Soc. Am. 87 (4), April.
Holler, M., Tarn, S., Castro, H., Benson, R., “An Electrically Trainable Artificial Neural Network (ETANN) with 10240 ‘Floating Gate’ Synapses,” Intel Corp., Technology Development, Novel Device Group, Santa Clara, CA, Proc. of the Int’l Annual Conf. on Neural Networks, 1989, pp. II-191-196.
Iwata, A., Yoshida, Y., Matsuda, S., Sato, Y., and Suzumura, N., “An Artificial Neural Network Accelerator using General Purpose Floating Point Digital Signal Processors,” Proc. JCNN 1989, pp. II-171-175.
Kurfess, F., “Unification with ICSIM,” Forthcoming 1991, Technical Report, International Computer Science Institute, Berkeley, CA.
Kung, S.Y., and Hwang, J.N., “A Unified Systolic Architecture for Artificial Neural Networks,” Journal of Parallel and Distributed Computing, Michael Arbib (ed.), April.
Le Cun, Y., Denker, J., Solla, S., Howard, R., and Jackel, L., “Optimal Brain Damage,” in Advances in Neural Information Processing Systems II, David Touretzky (ed.), Morgan-Kaufmann, San Mateo, 1990.
Lyon, R., and Mead, C, “An Analog Electronic Cochlea,” Transactions on Acoustics, Speech, and Signal Processing, Vol. 36, No. 7, July 1988, pp. 1119–1134, and in Artificial Neural Networks Electronic Implementations, Nelson Morgan (ed.), IEEE Computer Society Neural Networks Technology Series, 1990.
Morgan, N. (ed.), Artificial Neural Networks: Electronic Implementations, 1990.
Morgan, N., Beck, J., Kohn, P., Bilmes, J., Allman, E., and Beer, J., “The RAP: a Ring Array Processor for Layered Network Calculations,” Proc. of Intl. Conf. on Application Specific Array Processors, pp. 296–308, IEEE Computer Society Press, Princeton, NJ, 1990.
Morgan, N., Beck, J., Kohn, P., Bilmes, J., Allman, E., and Beerk, J., “The RAP: a Ring Array Procesor for Layered Network Calculations,” Proc. of Intl. Conf. on Application Specific Array Procesors, pp. 296–308. IEEE Computer Society Press, Princeton, N.J., 1990.
Morgan, N., and Bourlard, H., “Continuous Speech Recognition Using Multilayer Perceptrons with Hidden Markov Models,” Proc. IEEE Intl. Conf. on Acoustics, Speech, & Signal Processing, pp. 413–416, Albuquerque, New Mexico, 1990.
Morgan, N., Hermansky, H., Wooters, C., Kohn, P., and Bourlard, H., “Phonetically-based Speaker-Independent Continuous Speech Recognition Using PLP Analysis with Multilayer Perceptrons,” IEEE Intl. Conf. on Acoustics, Speech, & Signal Processing, Toronto, Canada, 1991, in Press.
Omohundro, S., “The Sather Language,” Technical Report (in draft form), International Computer Science Institute, Berkeley, CA.
Ramacher, U., and Raab., W., “Fine-grain System Architectures for Systolic Emulation of Neural Algorithm,” Proc. of Intl. Conf. on Application Specific Array Processors, pp. 554–566. IEEE Computer Society Press, Princeton, N.J.
Schmidt, H., “ICSIM: Initial design of an object-oriented net simulator,” Technical Report TR-90-055, International Computer Science Institute, Berkeley, CA, 1990.
Tarn, M., Smith., J., and Farber, D., “A Survey of Distributed Shared Memory Systems,” Dept., CIS, U. of Pennsylvania, Philadelphia, Pa.
Wilson, M.A., et al, Genesis: A system for simulating neural networks, Proc. of’ 89 NIPS conf., also TR: Pasadena: Cal. Inst. of Tech., 1989.
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Feldman, J.A. (1991). Conventional and Connectionist Parallel Computation. In: Schwärtzel, H. (eds) Angewandte Informatik und Software / Applied Computer Science and Software. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-93501-5_5
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DOI: https://doi.org/10.1007/978-3-642-93501-5_5
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