223 research outputs found
Determining Free Energy Differences Through Variational Morphing
Free energy calculations based on atomistic Hamiltonians and sampling are key
to a first principles understanding of biomolecular processes, material
properties, and macromolecular chemistry. Here, we generalize the Free Energy
Perturbation method and derive non-linear Hamiltonian transformation sequences
for optimal sampling accuracy that differ markedly from established linear
transformations. We show that our sequences are also optimal for the Bennett
Acceptance Ratio (BAR) method, and our unifying framework generalizes BAR to
small sampling sizes and non-Gaussian error distributions. Simulations on a
Lennard-Jones gas show that an order of magnitude less sampling is required
compared to established methods.Comment: 7 pages, 5 figure
Variationally Derived Intermediates for Correlated Free Energy Estimates between Intermediate States
Free energy difference calculations based on atomistic simulations generally
improve in accuracy when sampling from a sequence of intermediate equilibrium
thermodynamic states that bridge the configuration space between two states of
interest. For reasons of efficiency, usually the same samples are used to
calculate the step-wise difference of such an intermediate to both adjacent
intermediates. However, this procedure violates the assumption of uncorrelated
estimates that is necessary to derive both the optimal sequence of intermediate
states and the widely used Bennett acceptance ratio (BAR) estimator. In this
work, via a variational approach, we derive the sequence of intermediate states
and the corresponding estimator with minimal mean squared error that account
for these correlations and assess its accuracy
More Bang for Your Buck: Improved use of GPU Nodes for GROMACS 2018
We identify hardware that is optimal to produce molecular dynamics
trajectories on Linux compute clusters with the GROMACS 2018 simulation
package. Therefore, we benchmark the GROMACS performance on a diverse set of
compute nodes and relate it to the costs of the nodes, which may include their
lifetime costs for energy and cooling. In agreement with our earlier
investigation using GROMACS 4.6 on hardware of 2014, the performance to price
ratio of consumer GPU nodes is considerably higher than that of CPU nodes.
However, with GROMACS 2018, the optimal CPU to GPU processing power balance has
shifted even more towards the GPU. Hence, nodes optimized for GROMACS 2018 and
later versions enable a significantly higher performance to price ratio than
nodes optimized for older GROMACS versions. Moreover, the shift towards GPU
processing allows to cheaply upgrade old nodes with recent GPUs, yielding
essentially the same performance as comparable brand-new hardware.Comment: 41 pages, 13 figures, 4 tables. This updated version includes the
following improvements: - most notably, added benchmarks for two coarse grain
MARTINI systems VES and BIG, resulting in a new Figure 13 - fixed typos -
made text clearer in some places - added two more benchmarks for MEM and RIB
systems (E3-1240v6 + RTX 2080 / 2080Ti
Best bang for your buck: GPU nodes for GROMACS biomolecular simulations
The molecular dynamics simulation package GROMACS runs efficiently on a wide
variety of hardware from commodity workstations to high performance computing
clusters. Hardware features are well exploited with a combination of SIMD,
multi-threading, and MPI-based SPMD/MPMD parallelism, while GPUs can be used as
accelerators to compute interactions offloaded from the CPU. Here we evaluate
which hardware produces trajectories with GROMACS 4.6 or 5.0 in the most
economical way. We have assembled and benchmarked compute nodes with various
CPU/GPU combinations to identify optimal compositions in terms of raw
trajectory production rate, performance-to-price ratio, energy efficiency, and
several other criteria. Though hardware prices are naturally subject to trends
and fluctuations, general tendencies are clearly visible. Adding any type of
GPU significantly boosts a node's simulation performance. For inexpensive
consumer-class GPUs this improvement equally reflects in the
performance-to-price ratio. Although memory issues in consumer-class GPUs could
pass unnoticed since these cards do not support ECC memory, unreliable GPUs can
be sorted out with memory checking tools. Apart from the obvious determinants
for cost-efficiency like hardware expenses and raw performance, the energy
consumption of a node is a major cost factor. Over the typical hardware
lifetime until replacement of a few years, the costs for electrical power and
cooling can become larger than the costs of the hardware itself. Taking that
into account, nodes with a well-balanced ratio of CPU and consumer-class GPU
resources produce the maximum amount of GROMACS trajectory over their lifetime
- …