GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

GROMACS: высокопроизводительные молекулярные моделирования посредством многоуровневого параллелизма от ноутбуков до суперкомпьютеров
M Abraham, Teemu J. Murtola, Roland Schulz, Szilárd Páll, Jeremy C. Smith, Berk Hess, Erik Lindahl
2015-07-16

CPU-GPU accelerationGROMACSSIMDmolecular dynamicsmulti-level parallelism
GROMACS is one of the most widely used open-source and free software codes in chemistry, used primarily for dynamical simulations of biomolecules. It provides a rich set of calculation types, preparation and analysis tools. Several advanced techniques for free-energy calculations are supported. In version 5, it reaches new performance heights, through several new and enhanced parallelization algorithms. These work on every level; SIMD registers inside cores, multithreading, heterogeneous CPU–GPU acceleration, state-of-the-art 3D domain decomposition, and ensemble-level parallelization through built-in replica exchange and the separate Copernicus framework. The latest best-in-class compressed trajectory storage format is supported.
1
Ensemble-level parallelization is provided via built-in replica exchange and the separate Copernicus framework.
2
GROMACS is a widely used open-source molecular simulation software for biomolecular dynamics with extensive preparation and analysis tools.
3
Multi-level parallelism is implemented across SIMD registers, multithreading, heterogeneous CPU-GPU acceleration, and 3D domain decomposition.
4
Support added for a best-in-class compressed trajectory storage format.
5
Version 5 achieves significant performance improvements via several new and enhanced parallelization algorithms.

GROMACS molecular simulation software

High-performance multi-level parallelism and related performance features for dynamical biomolecular simulations (SIMD, multithreading, CPU–GPU acceleration, 3D domain decomposition, ensemble-level parallelization, compressed trajectory storage)

Publication Details
Publication Date
2015-07-16
Journal
Publisher
ISSN
Access Type
Author Information
Authors
M Abraham
Teemu J. Murtola
Roland Schulz
Szilárd Páll
Jeremy C. Smith
Berk Hess
Erik Lindahl
Explore further
Open the scid.ai AI chat with a ready-made request: it will find papers on a similar topic and help build a literature review.
Find similar papers in the chat
Make a presentation
100%