Progressive high-quality response surfaces for visually guided sensitivity analysis
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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Inversion of large-scale matrices appears in a few scientific applications like model reduction or optimal control. Matrix inversion requires an important computational effort and, therefore, the application of high performance computing techniques and architectures for matrices with dimension in the order of thousands. Following the recent up rise of graphics processors (GPUs), we present and evaluate high performance codes for matrix inversion, based on Gauss-Jordan elimination with partial pivoting, which off-load the main computational kernels to one or more GPUs while performing fine-grain operations on the general-purpose processor. The target architecture consists of a multi-core processor connected to several GPUs. Parallelism is extracted from parallel implementations of BLAS and from the concurrent execution of operations in the available computational units. Numerical experiments on a system with two Intel Quad Core processors and four NVIDIA c1060 GPUs illustrate the efficiency and the scalability of the different implementations, which deliver over 1.2脳1012 floating point operations per second.