Towards multi-GPU support for visualization

John D. Owens
SciDAC Institute for Ultrascale Visualization / Department of Electrical and Computer, Engineering, University of California at Davis, One Shields Avenue, Davis CA 95616 USA
J. Phys.: Conf. Ser., Vol. 78, No. 1. (2007), 012055


   title={Towards multi-GPU support for visualization},

   author={Owens, J.D.},

   booktitle={Journal of Physics: Conference Series},




   organization={IOP Publishing}


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At the Institute for Ultrascale Visualization, we are tackling the broad problem of building visualization solutions for the petascale age and beyond. As computing transitions into a new age where scalar solutions no longer improve in performance, and parallel solutions are the vehicle for future performance gains, one key challenge in our effort is to harness the power of emerging commodity data-parallel processors. One of the most promising new parallel processors is the graphics processing unit (GPU), whose recent gains in programmability and enormous arithmetic and memory bandwidth substantially outpace its CPU counterpart. The GPU’s performance and programmability, together with its graphics capabilities, make it particularly well suited for demanding nextgeneration visualization applications. We face a number of important challenges in making the GPU a first-class citizen for computation in these visualization applications. In this paper, we describe how we are addressing these challenges: mapping GPUs to general-purpose tasks, fundamental algorithms for GPUs, visualization applications that use the GPU for general-purpose computation, and extending our applications to support multiple GPUs. We also describe trends in upcoming GPU designs and how they will impact our research moving forward.
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