Workshop on Mapping Signal and Image Processing Algorithms onto Parallel Processors
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Workshop on Mapping Signal and Image Processing Algorithms onto Parallel Processors 4-5 July, 1994, University of Westminster, London

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Published by KFKI Research Institute for Measurement and Computing Techniques in Budapest .
Written in English


Book details:

Edition Notes

Statementedited by I. Erényi.
Classifications
LC ClassificationsIN PROCESS
The Physical Object
Pagination1 v. ;
ID Numbers
Open LibraryOL878377M
LC Control Number95165410

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  JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 8, () RESEARCH NOTE Mapping Signal Processing Algorithms on Parallel Architectures NIDAL M. SAMMUR AND MARTIN T. HAGAN Electrical and Computer Engineering Department, Oklahoma State University, Stillwater, Oklahoma The Burg filter is a signal processing algorithm which is Cited by: 4. Data-parallel Digital Signal Processors: Algorithm Mapping, Architecture Scaling and Workload Adaptation by Sridhar Rajagopal Emerging applications such as high definition television (HDTV), streaming video, im-age processingin embedded applicationsand . Data-parallel Digital Signal Processors: Algorithm Mapping, Architecture Scaling and Workload Adaptation streaming video, image processing in embedded applications and signal processing in high-speed wireless communications are driving a need for high performance digital signal processors (DSPs) with real-time processing. inter-cluster.   Abstract: The broad introduction of multi-core processors into computing has brought a great opportunity to deploy computationally demanding applications such as signal and image processing on parallel computing platforms. However it is not an easy task to decompose a computational problem into sub-problems to explore the massive parallelism provided by multi-core processors.

in parallel and pipeline modes. Data streams may split, merge, exchange or cross information. This is called multipipelined execution. It is, therefore, im-portant to represent signal and image processing algorithms in multipipelined form to be properly mapped onto the homogeneous computing structure. Abstract: Many digital signal and image processing algorithms can be speeded up by executing them in parallel on multiple processors. The speed of parallel execution is limited by the need for communication and synchronization between processors. In this paper, we present a paradigm for parallel processing that we call the block data flow paradigm (BDFP).   In this research we also tried to describe the role of parallel image processing in the field of medical imaging. algorithm with p processors and Ts (n,1) Digital signal processor.   gershausen, önfeld and , “Mapping complex image processing algorithms onto heterogeneous multi-processors regarding architecture dependent performance parameters”, Intnl. Workshop on Algorithms and Parallel VLSI Architectures, Leuven, Belgium, August Also in “Algorithms and Parallel VLSI Architectures III.

Most signal processing algorithms have built in recursivity and regularity and in for the case of parallel arrays, local connectivity. (2n-1) steps, on parallel computer with n processors it. Optimal mapping of sequences of data parallel tasks. Share on. Authors: Jaspal Subhlok. School of Computer Science, Carnegie Mellon University, Pittsburgh PA. School of Computer Science, Carnegie Mellon University, Pittsburgh PA. Search about this author, Gary Vondran. many image processing tasks. Unfortunately, simply porting a software algorithm onto an FPGA often gives disappointing results, because many image processing algorithms have been optimised for a serial processor. It is usually necessary to transform the algorithm to efficiently exploit the parallelism and resources available on an FPGA. Different software realizations of the dataflow in the algorithms can affect the performance of stream processors by greater than an order-of-magnitude. The thesis first presents the design of signal processing algorithms that map efficiently on stream processors by parallelizing the algorithms and by re-ordering the flow of data.