Accelerating the Production of Synthetic Seismograms by a Multicore Processor Cluster with Multiple GPUs

In this work we propose two different parallel versions of the software package COMPSYN, devoted to the production of syntethic seismograms. The first version consists in the parallelization of the code to run on a cluster of multicore processors and is obtained by exploiting the MPI paradigm and OpenMP API to the end of maximizing the performance on multicore processors. The second version exploits the set of GPU associated to the multicore processor cluster and uses CUDA to take advantage of the GPU's computational power. We analyze the application performance of the two different implementations by using a real case study. In particular, we obtain for the GPU version a speedup of 10x over the parallelized version running on the cluster of multicore processors. Furthermore, we can estimate about at least 100x the speedup of the GPU version using a single node of the cluster with respect to the original sequential version

Accelerating the production of synthetic seismograms by a multicore processor cluster with multiple GPUs

In this work we propose two different parallel versions of the software package COMPSYN, devoted to the production of syntethic seismograms. The first version consists in the parallelization of the code to run on a cluster of multicore processors and is obtained by exploiting the MPI paradigm and OpenMP API to the end of maximizing the performance on multicore processors. The second version exploits the set of GPU associated to the multicore processor cluster and uses CUDA to take advantage of the GPU's computational power. We analyze the application performance of the two different implementations by using a real case study. In particular, we obtain for the GPU version a speedup of 10x over the parallelized version running on the cluster of multicore processors. Furthermore, we can estimate about at least 100x the speedup of the GPU version using a single node of the cluster with respect to the original sequential version. © 2012 IEEE

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal