2008 | Berk Hess, Carsten Kutzner, David van der Spoel, and Erik Lindahl
The article "GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation" by Berk Hess, Carsten Kutzner, David van der Spoel, and Erik Lindahl presents significant advancements in the GROMACS molecular simulation toolkit. The authors focus on achieving high performance on single processors through algorithmic optimizations and hand-coded routines, while also ensuring excellent scalability on parallel machines. Key contributions include:
1. **Domain Decomposition**: An eighth-shell domain decomposition method with minimal communication, dynamic load balancing, and efficient neighbor searching using charge groups.
2. **Dynamic Load Balancing**: A robust algorithm that balances the load by adjusting cell boundaries to avoid oscillations and instabilities, ensuring efficient and stable performance.
3. **Parallel Holonomic Constraints**: A non-iterative constraint algorithm (P-LINCS) that allows for holonomic constraints without iterative communication, enabling longer integration time steps.
4. **Optimizing Memory Access**: A sorting scheme that optimizes memory access order during force calculations, improving performance for large systems.
5. **Multiple-Program, Multiple-Data PME Parallelization**: A new approach to parallelize the Particle Mesh Ewald (PME) electrostatics algorithm, reducing communication and improving scaling.
The article also discusses other new features, such as support for coarse-grained simulations, user-defined interactions, and 2D periodic systems. Benchmarks demonstrate the scalability and performance of GROMACS 4, showing linear scaling for weak scaling and strong scaling for larger systems. The results highlight the effectiveness of the new algorithms in achieving high performance and scalability in molecular simulations.The article "GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation" by Berk Hess, Carsten Kutzner, David van der Spoel, and Erik Lindahl presents significant advancements in the GROMACS molecular simulation toolkit. The authors focus on achieving high performance on single processors through algorithmic optimizations and hand-coded routines, while also ensuring excellent scalability on parallel machines. Key contributions include:
1. **Domain Decomposition**: An eighth-shell domain decomposition method with minimal communication, dynamic load balancing, and efficient neighbor searching using charge groups.
2. **Dynamic Load Balancing**: A robust algorithm that balances the load by adjusting cell boundaries to avoid oscillations and instabilities, ensuring efficient and stable performance.
3. **Parallel Holonomic Constraints**: A non-iterative constraint algorithm (P-LINCS) that allows for holonomic constraints without iterative communication, enabling longer integration time steps.
4. **Optimizing Memory Access**: A sorting scheme that optimizes memory access order during force calculations, improving performance for large systems.
5. **Multiple-Program, Multiple-Data PME Parallelization**: A new approach to parallelize the Particle Mesh Ewald (PME) electrostatics algorithm, reducing communication and improving scaling.
The article also discusses other new features, such as support for coarse-grained simulations, user-defined interactions, and 2D periodic systems. Benchmarks demonstrate the scalability and performance of GROMACS 4, showing linear scaling for weak scaling and strong scaling for larger systems. The results highlight the effectiveness of the new algorithms in achieving high performance and scalability in molecular simulations.