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Horizons of parallel computation
Journal of Parallel and Distributed Computing, 1995This paper considers the ultimate impact of fundamental physical limitations—notably, speed of light and device size—on parallel computing machines. Although we fully expect an innovative and very gradual evolution to the limiting situation, we take here the provocative view of exploring the consequences of the accomplished attainment of the physical ...
Franco P. Preparata, Gianfranco Bilardi
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SIAM Journal on Computing, 1987
Fundamental discussion of fast parallel computation of a b mod m, for n- bit integers a,b,m, in time (log n)\({}^{O(1)}\), and of related questions, including arithmetic and boolean circuits, extension to polynomials.
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Fundamental discussion of fast parallel computation of a b mod m, for n- bit integers a,b,m, in time (log n)\({}^{O(1)}\), and of related questions, including arithmetic and boolean circuits, extension to polynomials.
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Scheduling Parallel Computations
Journal of the ACM, 1968A model for parallel computations is given as a directed graph in which nodes represent elementary operations, and branches, data channels. The problem considered is the determination of an admissible schedule for such a computation; i.e. for each node determine a sequence of times at which the node initiates its operation.
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ACM SIGGRAPH 2008 classes, 2008
Users always care about performance. Although often it's just a matter of making sure the software is doing only what it should, there are many cases where it is vital to get down to the metal and leverage the fundamental characteristics of the processor.Until recently, performance improvement was not difficult.
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Users always care about performance. Although often it's just a matter of making sure the software is doing only what it should, there are many cases where it is vital to get down to the metal and leverage the fundamental characteristics of the processor.Until recently, performance improvement was not difficult.
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Parallel multischeme computation
Journal of Scientific Computing, 1988zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yi-ling F. Chiang +3 more
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Parallel computing on personal computers
Proceedings of the 1986 ACM SIGSMALL/PC symposium on Small systems - SIGSMALL '86, 1986A set of Personal Computers is connected to form a ring-structured parallel system. Each computer has access to its local memory and can communicate with its two neighbours in the ring.This network of asynchronous processors is used to solve in parallel combinatorial optimization problems that are too time- and space-consuming to be handled on a single
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Computational biology on parallel computers
2003 European Control Conference (ECC), 2003Several applications in computational biology have large runtime and memory requirements either because of large data sizes or the inherent time and memory complexity of the underlying algorithms. Parallel computing is an effective way to address both these concerns — run-time can be reduced by the use of multiple processors to solve the same problem ...
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Journal of the ACM, 1966
Various classes of machines incorporating parallelism are considered. A general class of large-scale multiprocessors is outlined, and some problems of hardware and software implementation for computers of this class are discussed.
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Various classes of machines incorporating parallelism are considered. A general class of large-scale multiprocessors is outlined, and some problems of hardware and software implementation for computers of this class are discussed.
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2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS), 2010
NVIDIA's CUDA architecture provides a powerful platform for writing highly parallel programs. By providing simple abstractions for hierarchical thread organization, memories, and synchronization, the CUDA programming model allows programmers to write scalable programs without the burden of learning a multitude of new programming constructs.
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NVIDIA's CUDA architecture provides a powerful platform for writing highly parallel programs. By providing simple abstractions for hierarchical thread organization, memories, and synchronization, the CUDA programming model allows programmers to write scalable programs without the burden of learning a multitude of new programming constructs.
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Parallelism in Seismic Computing
Surveys in Geophysics, 1989This paper describes a vectorized and parallelized implementation of a two-dimensional pseudo-spectral seismic elastic model and of a (frequency-domain) seismic migration algorithm on the (tightly-coupled) vector multiprocessor IBM 3090 VF. Performance data of alternative parallel implementations on an LCAP (loosely coupled system of array processors ...
M. Kindelan, P. Sguazzero, A. Kamel
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