Understanding Molecular Simulation
Understanding Molecular Simulation
A Guide to Monte Carlo Simulations in Statistical Physics
A Guide to Monte Carlo Simulations in Statistical Physics
Computing Distance Histograms Ef?ciently in Scientific Databases
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Performance analysis of a dual-tree algorithm for computing spatial distance histograms
The VLDB Journal — The International Journal on Very Large Data Bases
Distance histogram computation based on spatiotemporal uniformity in scientific data
Proceedings of the 15th International Conference on Extending Database Technology
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Analysis of large particle or molecular simulation data is integral part of the basic-science research community. It often involves computing functions such as point-to-point interactions of particles. Spatial distance histogram (SDH) is one such vital computation in scientific discovery. SDH is frequently used to compute Radial Distribution Function (RDF), and it takes quadratic time to compute using naive approach. Naive SDH computation is even more expensive as it is computed continuously over certain period of time to analyze simulation systems. In this paper we look at different tree-based SDH computation techniques and briefly discuss about their performance. We present different strategies to improve the performance of these techniques. Specifically, we study the density map (DM) based SDH computation techniques. A DM is essentially a grid dividing simulated space into cells (3D cubes) of equal size (volume), which can be easily implemented by augmenting a Quad-tree (or Oct-tree) index. DMs are used in various configurations to compute SDH continuously over snapshots of the simulation system. The performance improvements using some of these configurations is presented in this paper. We also discuss the effect of utilizing computation power of Graphics Processing Units (GPUs) in computing SDH.