Dealiased convolutions for pseudospectral simulations
نویسندگان
چکیده
منابع مشابه
Dealiased Convolutions for Pseudospectral Simulations
Efficient algorithms have recently been developed for calculating dealiased linear convolution sums without the expense of conventional zero-padding or phase-shift techniques. For one-dimensional in-place convolutions, the memory requirements are identical with the zero-padding technique, with the important distinction that the additional work memory need not be contiguous with the input data. ...
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Convolutions are used in data and image analysis and form the crux of the pseudospectral method for direct numerical simulations of fluids. Their calculation is a computationally expensive task that is facilitated by the use of FFTs at the expense of increased memory, which is required for the removal of aliased terms. Here, we present a multithreaded version of the method of implicit dealiasin...
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Implicit dealiasing is a method for computing in-place linear convolutions via fast Fourier transforms that decouples work memory from input data. It offers easier memory management and, for long one-dimensional input sequences, greater efficiency than conventional zero-padding. Furthermore, for convolutions of multidimensional data, the segregation of data and work buffers can be exploited to ...
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Algorithms are developed for calculating dealiased linear convolution sums without the expense of conventional zero-padding or phase-shift techniques. For one-dimensional in-place convolutions, the memory requirements are identical with the zero-padding technique, with the important distinction that the additional work memory need not be contiguous with the input data. This decoupling of data a...
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Implicitly dealiasing is a recently-developed technique which improves upon conventional zero padding to compute linear convolutions via fast Fourier transforms. For onedimensional inputs, the memory requirements and performance are similar to conventional zero-padded convolutions, but implicitly dealiased convolutions are faster and require less memory when the data is multi-dimensional. We sh...
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ژورنال
عنوان ژورنال: Journal of Physics: Conference Series
سال: 2011
ISSN: 1742-6596
DOI: 10.1088/1742-6596/318/7/072037