Name
Abstract | ||
|---|---|---|
Molecular modeling is a field that traditionally has large computational costs. Until recently, most simulation techniques relied on long trajectories, which inherently have poor scalability. A new class of methods is proposed that requires only a large number of short calculations, and for which minimal communication between computer nodes is required. We considered one of the more accurate variants called Accelerated Weighted Ensemble Dynamics (AWE) and for which distributed computing can be made efficient. We implemented AWE using the Work Queue framework for task management and applied it to an all atom protein model (Fip35 WW domain). We can run with excellent scalability by simultaneously utilizing heterogeneous resources from multiple computing platforms such as clouds (Amazon EC2, Microsoft Azure), dedicated clusters, grids, on multiple architectures (CPU/GPU, 32/64bit), and in a dynamic environment in which processes are regularly added or removed from the pool. This has allowed us to achieve an aggregate sampling rate of over 500 ns/hour. As a comparison, a single process typically achieves 0.1 ns/hour. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/eScience.2012.6404429 | eScience |
Keywords | DocType | Citations |
poor scalability,folding protein,large computational cost,Fip35 WW domain,Work Queue,Amazon EC2,multiple architecture,large number,Weighted Ensemble Dynamics,Microsoft Azure,multiple computing platform,excellent scalability | Conference | 1 |
PageRank | References | Authors |
0.38 | 5 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dinesh Rajan | 1 | 191 | 33.05 |
| Haoyun Feng | 2 | 4 | 1.41 |
| Li Yu | 3 | 57 | 4.45 |
| Badi' Abdul-Wahid | 4 | 2 | 0.73 |
| Douglas Thain | 5 | 1530 | 127.00 |
| Eric Darve | 6 | 440 | 44.79 |
| Jesus A. Izaguirre | 7 | 61 | 16.21 |