X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/kahina_paper2.git/blobdiff_plain/f9bf26cc810e585f03a82f4a0a5e8c64b96842ca..2aba789f5e740bc30d29b14c1761f917c8f83108:/paper.tex

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@@ -565,8 +565,6 @@ Algorithm~\ref{alg2-cuda} shows a sketch of the Ehrlich-Aberth method using CUDA
 \section{The EA algorithm on Multi-GPU}
 
 \subsection{MGPU (OpenMP-CUDA)approach}
-Before beginning the calculation, our implementation parallel with OpenMP and CUDA shares the input data between threads OpenMP, these input data sotn Z: the vector solution, P: the polynomial to solve,
-
 Before starting computations, our parallel implementation shared input data of the root finding polynomial between OpenMP threads. From Algorithm 1, the input data are the solution vector $Z$, the polynomial to solve $P$. Let number of OpenMP threads is equal to the number of GPUs, each threads OpenMP ( T-omp) checks one GPU,  and control a part of the shared memory, that is a part of the vector Z  like: $(n/Nbr_gpu)$ roots, n: the polynomial's degrees, $Nbr_gpu$ the number of GPUs. Then every GPU will have a grid of computation organized with its performances and the size of data of which it checks. In principle a grid is set by two parameter DimGrid, the number of block per grid, DimBloc: the number of threads per block. The following schema  shows the architecture of (CUDA,OpenMP).