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Current high performance clusters are equipped with high bandwidth/low latency networks, lots of processors and nodes, very fast storage systems, etc. However, due to economical and/or power related constraints, in general it is not feasible to provide an accelerating co-processor –such as a graphics processor (GPU)– per node. To overcome this, in this paper we present a GPU virtualization middleware, which makes remote CUDA-compatible GPUs available to all the cluster nodes. The software is implemented on top of the sockets application programming interface, ensuring portability over commodity networks, but it can also be easily adapted to high performance networks. [ABSTRACT FROM AUTHOR]

We investigate the performance of the routines in LAPACK and the Successive Band Reduction (SBR) toolbox for the reduction of a dense matrix to tridiagonal form, a crucial preprocessing stage in the solution of the symmetric eigenvalue problem, on general-purpose multi-core processors. In response to the advances of hardware accelerators, we also modify the code in SBR to accelerate the computation by off-loading a significant part of the operations to a graphics processor (GPU). Performance results illustrate the parallelism and scalability of these algorithms on current high-performance multi-core architectures. [ABSTRACT FROM AUTHOR]

We present several algorithms to compute the solution of a linear system of equations on a GPU, as well as general techniques to improve their performance, such as padding and hybrid GPU-CPU computation. We also show how iterative refinement with mixed-precision can be used to regain full accuracy in the solution of linear systems. Experimental results on a G80 using CUBLAS 1.0, the implementation of BLAS for NVIDIA® GPUs with unified architecture, illustrate the performance of the different algorithms and techniques proposed. [ABSTRACT FROM AUTHOR]

The increase in performance of the last generations of graphics processors (GPUs) has made this class of hardware a coprocessing platform of remarkable success in certain types of operations. In this paper we evaluate the performance of linear algebra and image processing routines, both on classical and unified GPU architectures and traditional processors (CPUs). From this study, we gain insights on the properties that make an algorithm likely to deliver high performance on a GPU. [ABSTRACT FROM AUTHOR]