| | 2 | by N.Nakasato (Journal of Computational Science) |
| | 3 | == abstract == |
| | 4 | The kd-tree is a fundamental tool in computer science. |
| | 5 | Among other applications, the application of kd-tree search (by the |
| | 6 | tree method) to the fast evaluation of particle interactions and |
| | 7 | neighbor search is highly important, since the computational |
| | 8 | complexity of these problems is reduced from O(N^2^) for a brute |
| | 9 | force method to O(N log N) for the tree method, where N is |
| | 10 | the number of particles. |
| | 11 | In this paper, we present a parallel |
| | 12 | implementation of the tree method running on a graphics processing |
| | 13 | unit (GPU). We present a detailed description of how we have |
| | 14 | implemented the tree method on a Cypress GPU. |
| | 15 | An optimization that we found important is localized particle ordering to |
| | 16 | effectively utilize cache memory. |
| | 17 | We present a number of test results |
| | 18 | and performance measurements. |
| | 19 | Our results show that the execution of the tree traversal |
| | 20 | in a force calculation on a GPU is practical and efficient. |
| | 21 | |
| | 22 | == Link == |
| | 23 | http://dx.doi.org/10.1016/j.jocs.2011.01.006 |
| | 24 | |
| | 25 | == Recent Update (201112) == |