Name
Abstract | ||
|---|---|---|
Molecular dynamics (MD) has been widely used in today's scientific research across multiple domains including materials science, biochemistry, biophysics, and structural biology. MD simulations can produce extremely large amounts of data in that each simulation could involve a large number of atoms (up to trillions) for a large number of timesteps (up to hundreds of millions). In this paper, we perform an in-depth analysis of a number of MD simulation datasets and then develop an efficient error-bounded lossy compressor that can significantly improve the compression ratios. The contributions are fourfold. (1) We characterize a number of MD datasets and summarize two commonly-used execution models. (2) We develop an adaptive error-bounded lossy compression framework (called MDZ), which can optimize the compression for both execution models adaptively by taking advantage of their specific characteristics. (3) We compare our solution with six other state-of-the-art related works by using three MD simulation packages each with multiple configurations. Experiments show that our solution has up to 233 % higher compression ratios than the second-best lossy compressor in most cases. (4) We demonstrate that MDZ is fully capable of handing particle data beyond MD simulations. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICDE53745.2022.00007 | 2022 IEEE 38th International Conference on Data Engineering (ICDE) |
Keywords | DocType | ISSN |
lossy compression,trajectory compression,molecular dynamics | Conference | 1063-6382 |
ISBN | Citations | PageRank |
978-1-6654-0884-4 | 0 | 0.34 |
References | Authors | |
25 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kai Zhao | 1 | 0 | 0.34 |
| Sheng Di | 2 | 737 | 55.88 |
| Danny Perez | 3 | 0 | 0.34 |
| Xin Liang | 4 | 1 | 1.04 |
| Zizhong Chen | 5 | 924 | 69.93 |
| Franck Cappello | 6 | 3775 | 251.47 |