The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
A flexible algorithm for calculating pair interactions on SIMD architectures
Open accessAbstract
Calculating interactions or correlations between pairs of particles is typically the most time-consuming task in particle simulation or correlation analysis. Straightforward implementations using a double loop over particle pairs have traditionally worked well, especially since compilers usually do a good job of unrolling the inner loop. In order to reach high performance on modern CPU and accelerator architectures, single-instruction multiple-data (SIMD) parallelization has become essential. Avoiding memory bottlenecks is also increasingly important and requires reducing the ratio of memory to arithmetic operations. Moreover, when pairs only interact within a certain cut-off distance, good SIMD utilization can only be achieved by reordering input and output data, which quickly becomes a limiting factor. Here we present an algorithm for SIMD parallelization based on grouping a fixed number of particles, e.g. 2, 4, or 8, into spatial clusters. Calculating all interactions between particles in a pair of such clusters improves data reuse compared to the traditional scheme and results in a more efficient SIMD parallelization. Adjusting the cluster size allows the algorithm to map to SIMD units of various widths. This flexibility not only enables fast and efficient implementation on current CPUs and accelerator architectures like GPUs or Intel MIC, but it also makes the algorithm future-proof. We present the algorithm with an application to molecular dynamics simulations, where we can also make use of the effective buffering the method introduces.
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Cited by
Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., & Lindahl, E. (2015) · SoftwareX · doi:10.1016/j.softx.2015.06.001 · Cited by 27,190
Self-citationMartini 3: A general purpose force field for coarse-grained molecular dynamics
Souza, P. C. T., Alessandri, R., Barnoud, J., Thallmair, S., Faustino, I., Grünewald, F., Patmanidis, I., Abdizadeh, H., Bruininks, B. M. H., Wassenaar, T. A., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01098-3 · Cited by 1,551
Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS
Páll, S., Zhmurov, A., Bauer, P., Abraham, M., Lundborg, M., Gray, A., Hess, B., & Lindahl, E. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0018516 · Cited by 940
Self-citationRNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview
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De novo design of potent and selective mimics of IL-2 and IL-15
Silva, D.-A., Yu, S., Ulge, U. Y., Spangler, J. B., Jude, K. M., Labão-Almeida, C., Ali, L. R., Quijano-Rubio, A., Ruterbusch, M., Leung, I., et al. (2019) · Nature · doi:10.1038/s41586-018-0830-7 · Cited by 531
Metadata
- Authors
- Páll, S., & Hess, B.
- Venue
- Computer Physics Communications
- Publisher
- Elsevier BV
- Published
- 2013-12
- Volume
- 184
- Issue
- 12
- Pages
- 2641-2650
- ISSN
- 0010-4655 (ISSN-L)
- OpenAlex
- W2064388657
- Cited by
- 709
- References
- 21
- FWCI
- 33.59
- Type
- journal article
- Language
- en
- Open access
- green
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