This paper deals with the solution of an inverse time fractional diffusion equation described by a Caputo fractional derivative. Numerical simulations, involving large domains, give rise to a huge practical problem. Hence, by starting from an accurate meshless localized collocation method using radial basis functions (RBFs), here we propose a fast algorithm which exploits the GPU-CUDA capabilities. More in detail, we first developed a C code which uses the well-known numerical library LAPACK to perform basic linear algebra operations in order to implement an efficient sequential algorithm. Then we propose a GPU software based on ad hoc parallel CUDA-kernels and efficient usage of parallel numerical libraries available for GPUs. Performance analysis will show the reliability and the efficiency of the proposed parallel implementation.

A GPU-CUDA framework for solving a two-dimensional inverse anomalous diffusion problem

Galletti A.;Marcellino L.;
2020-01-01

Abstract

This paper deals with the solution of an inverse time fractional diffusion equation described by a Caputo fractional derivative. Numerical simulations, involving large domains, give rise to a huge practical problem. Hence, by starting from an accurate meshless localized collocation method using radial basis functions (RBFs), here we propose a fast algorithm which exploits the GPU-CUDA capabilities. More in detail, we first developed a C code which uses the well-known numerical library LAPACK to perform basic linear algebra operations in order to implement an efficient sequential algorithm. Then we propose a GPU software based on ad hoc parallel CUDA-kernels and efficient usage of parallel numerical libraries available for GPUs. Performance analysis will show the reliability and the efficiency of the proposed parallel implementation.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/83407
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