2 resultados para Arquiteturas enterradas

em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo


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A multiplicação de experiências participativas visando ampliar o controle social sobre políticas e políticos tornou-se fenômeno relevante no Brasil e mundo afora. Neste artigo é apresentado um quadro analítico útil à comparação da diversidade da inovação democrática enquanto arquitetura da participação orientada para o controle social. Mediante a aplicação desse quadro, examinamos a configuração de duas arquiteturas contrastantes, originárias de dois contextos nacionais distintos: México e Brasil.

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The modern GPUs are well suited for intensive computational tasks and massive parallel computation. Sparse matrix multiplication and linear triangular solver are the most important and heavily used kernels in scientific computation, and several challenges in developing a high performance kernel with the two modules is investigated. The main interest it to solve linear systems derived from the elliptic equations with triangular elements. The resulting linear system has a symmetric positive definite matrix. The sparse matrix is stored in the compressed sparse row (CSR) format. It is proposed a CUDA algorithm to execute the matrix vector multiplication using directly the CSR format. A dependence tree algorithm is used to determine which variables the linear triangular solver can determine in parallel. To increase the number of the parallel threads, a coloring graph algorithm is implemented to reorder the mesh numbering in a pre-processing phase. The proposed method is compared with parallel and serial available libraries. The results show that the proposed method improves the computation cost of the matrix vector multiplication. The pre-processing associated with the triangular solver needs to be executed just once in the proposed method. The conjugate gradient method was implemented and showed similar convergence rate for all the compared methods. The proposed method showed significant smaller execution time.