2010/11/16 by Michael Bäder, Michael Bader, Hans-Joachim Bungartz +9
Computer Science · Earth and Planetary Sciences · Medicine · #Distributed #FOS: Computer and information sciences #Graphics (cs.GR) #Medical Imaging Techniques and Applications #Parallel #Parallel Computing and Optimization Techniques #Performance (cs.PF) #Seismic Imaging and Inversion Techniques #and Cluster Computing (cs.DC) #cs.DC #cs.GR #cs.PF
paper · pdf · doi:10.48550/arxiv.1011.3583
arxiv created 2010/11/16 · openalex publication_date 2010/11/16 · arxiv updated 2010/11/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many high performance-computing algorithms are bandwidth limited, hence the need for optimal data rearrangement kernels as well as their easy integration into the rest of the application. In this work, we have built a CUDA library of fast kernels for a set of data rearrangement operations. In particular, we have built generic kernels for rearranging m dimensional data into n dimensions, including Permute, Reorder, Interlace/De-interlace, etc. We have also built kernels for generic Stencil computations on a two-dimensional data using templates and functors that allow application developers to rapidly build customized high performance kernels. All the kernels built achieve or surpass best-known performance in terms of bandwidth utilization.