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| Title | Potential Benefits of a Block-Space GPU Approach for Discrete Tetrahedral Domains |
| Authors | Cristobal Navarro, Benjamin Bustos, Nancy Hitschfeld |
| Publication date | 2016 |
| Abstract | The study of data-parallel domain re-organization and thread-mapping techniques are relevant topics as they can increase the efficiency of GPU computations on spatial discrete domains with non-box-shaped geometry. In this work we study the potential benefits of applying a succinct data re-organization of a tetrahedral data-parallel domain of size O(n^3) combined with an efficient block-space GPU map of the form g(l) : N -> N^3. Results from the analysis suggest that in theory the combination of these two optimizations produce significant performance improvement as block-based data reorganization allows a coalesced one-to-one correspondence at local thread-space while g(l) produces an efficient block-space spatial correspondence between groups of data and groups of threads, reducing the number of unnecessary threads from O(n^3) to O(n^2 * r^3) with r in O(1). From the analysis, we obtained that a block based succinct data re-organization can provide up to 2x improved performance over a linear data organization while the map can be up to 6x more efficient than a bounding box approach. The results from this work can serve as a useful guide for a more efficient GPU computation on tetrahedral domains found in spin lattice, finite element and special n-body problems, among others. |
| Downloaded | 6 times |
| Pages | 1-5 |
| Conference name | Latin American Conference on Informatics |
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