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Parallel visual data restoration on multi-GPGPUs using stencil-reduce pattern
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DOI：10.1177/1094342014567907.png)
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Abstract
En 中文
In this paper, a highly effective parallel filter for visual data restoration is presented. The filter is designed following a skeletal approach, using a newly proposed stencil-reduce, and has been implemented by way of the FastFlow parallel programming library. As a result of its high-level design, it is possible to run the filter seamlessly on a multicore machine, on multi-GPGPUs, or on both. The design and implementation of the filter are discussed, and an experimental evaluation is presented.
Keywords:
Impulsive noise
Gaussian noise
image restoration
image filtering
GPGPUs
parallel patterns
skeletons
structured parallel programming
iterative stencil
stencil-reduce
MapReduce
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