The UCSC Kestrel Parallel Processor
IEEE Transactions on Parallel and Distributed Systems
Optimizing neural networks on SIMD parallel computers
Parallel Computing
A low-cost mixed-mode parallel processor architecture for embedded systems
Proceedings of the 21st annual international conference on Supercomputing
Finding the Next Computational Model: Experience with the UCSC Kestrel
Journal of Signal Processing Systems
Optimization of N-queens solvers on graphics processors
APPT'11 Proceedings of the 9th international conference on Advanced parallel processing technologies
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This paper presents the SIMD Phase Programming Model, a simple approach to solving asynchronous, irregular problems on massively parallel SIMD computers. The novelty of this model consists of a simple, clear method on how to turn a general serial program into an explicitly parallel one for a SIMD machine, transferring a portion of the flow control into the single PEs. Three case studies (the Mandelbrot Set, the N-Queen problem, and a Hopfield neural network that approximates the maximum clique in a graph) will be presented, implemented on two different SIMD computers (the UCSC Kestrel and the MasPar MP-2). Our results so far show good performance with respect to conventional serial CPU computing time and in terms of the high parallel speedup and efficiency achieved.