134 research outputs found
Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS
GROMACS is a widely used package for biomolecular simulation, and over the
last two decades it has evolved from small-scale efficiency to advanced
heterogeneous acceleration and multi-level parallelism targeting some of the
largest supercomputers in the world. Here, we describe some of the ways we have
been able to realize this through the use of parallelization on all levels,
combined with a constant focus on absolute performance. Release 4.6 of GROMACS
uses SIMD acceleration on a wide range of architectures, GPU offloading
acceleration, and both OpenMP and MPI parallelism within and between nodes,
respectively. The recent work on acceleration made it necessary to revisit the
fundamental algorithms of molecular simulation, including the concept of
neighborsearching, and we discuss the present and future challenges we see for
exascale simulation - in particular a very fine-grained task parallelism. We
also discuss the software management, code peer review and continuous
integration testing required for a project of this complexity.Comment: EASC 2014 conference proceedin
Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes
Multiscale and inhomogeneous molecular systems are challenging topics in the
field of molecular simulation. In particular, modeling biological systems in
the context of multiscale simulations and exploring material properties are
driving a permanent development of new simulation methods and optimization
algorithms. In computational terms, those methods require parallelization
schemes that make a productive use of computational resources for each
simulation and from its genesis. Here, we introduce the heterogeneous domain
decomposition approach which is a combination of an heterogeneity sensitive
spatial domain decomposition with an \textit{a priori} rearrangement of
subdomain-walls. Within this approach, the theoretical modeling and
scaling-laws for the force computation time are proposed and studied as a
function of the number of particles and the spatial resolution ratio. We also
show the new approach capabilities, by comparing it to both static domain
decomposition algorithms and dynamic load balancing schemes. Specifically, two
representative molecular systems have been simulated and compared to the
heterogeneous domain decomposition proposed in this work. These two systems
comprise an adaptive resolution simulation of a biomolecule solvated in water
and a phase separated binary Lennard-Jones fluid.Comment: 14 pages, 12 figure
The conduction pathway of potassium channels is water free under physiological conditions.
Ion conduction through potassium channels is a fundamental process of life. On the basis of crystallographic data, it was originally proposed that potassium ions and water molecules are transported through the selectivity filter in an alternating arrangement, suggesting a "water-mediated" knock-on mechanism. Later on, this view was challenged by results from molecular dynamics simulations that revealed a "direct" knock-on mechanism where ions are in direct contact. Using solid-state nuclear magnetic resonance techniques tailored to characterize the interaction between water molecules and the ion channel, we show here that the selectivity filter of a potassium channel is free of water under physiological conditions. Our results are fully consistent with the direct knock-on mechanism of ion conduction but contradict the previously proposed water-mediated knock-on mechanism
Adaptive multi-stage integrators for optimal energy conservation in molecular simulations
We introduce a new Adaptive Integration Approach (AIA) to be used in a wide
range of molecular simulations. Given a simulation problem and a step size, the
method automatically chooses the optimal scheme out of an available family of
numerical integrators. Although we focus on two-stage splitting integrators,
the idea may be used with more general families. In each instance, the
system-specific integrating scheme identified by our approach is optimal in the
sense that it provides the best conservation of energy for harmonic forces. The
AIA method has been implemented in the BCAM-modified GROMACS software package.
Numerical tests in molecular dynamics and hybrid Monte Carlo simulations of
constrained and unconstrained physical systems show that the method
successfully realises the fail-safe strategy. In all experiments, and for each
of the criteria employed, the AIA is at least as good as, and often
significantly outperforms the standard Verlet scheme, as well as fixed
parameter, optimized two-stage integrators. In particular, the sampling
efficiency found in simulations using the AIA is up to 5 times better than the
one achieved with other tested schemes
Distributed Computing in a Pandemic
The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Much of this COVID-19 research depends on distributed computing. In this article, I review distributed architectures -- various types of clusters, grids and clouds -- that can be leveraged to perform these tasks at scale, at high-throughput, with a high degree of parallelism, and which can also be used to work collaboratively. High-performance computing (HPC) clusters will be used to carry out much of this work. Several bigdata processing tasks used in reducing the spread of SARS-CoV-2 require high-throughput approaches, and a variety of tools, which Hadoop and Spark offer, even using commodity hardware. Extremely large-scale COVID-19 research has also utilised some of the world's fastest supercomputers, such as IBM's SUMMIT -- for ensemble docking high-throughput screening against SARS-CoV-2 targets for drug-repurposing, and high-throughput gene analysis -- and Sentinel, an XPE-Cray based system used to explore natural products. Grid computing has facilitated the formation of the world's first Exascale grid computer. This has accelerated COVID-19 research in molecular dynamics simulations of SARS-CoV-2 spike protein interactions through massively-parallel computation and was performed with over 1 million volunteer computing devices using the Folding@home platform. Grids and clouds both can also be used for international collaboration by enabling access to important datasets and providing services that allow researchers to focus on research rather than on time-consuming data-management tasks
GPU fast multipole method with lambda-dynamics features
A significant and computationally most demanding part of molecular dynamics simulations is the calculation of long-range electrostatic interactions. Such interactions can be evaluated directly by the naĂŻve pairwise summation algorithm, which is a ubiquitous showcase example for the compute power of graphics processing units (GPUS). However, the pairwise summation has O(N^2) computational complexity for N interacting particles; thus, an approximation method with a better scaling is required. Today, the prevalent method for such approximation in the field is particle mesh Ewald (PME). PME takes advantage of fast Fourier transforms (FFTS) to approximate the solution efficiently. However, as the underlying FFTS require all-to-all communication between ranks, PME runs into a communication bottleneck. Such communication overhead is negligible only for a moderate parallelization. With increased parallelization, as needed for high-performance applications, the usage of PME becomes unprofitable. Another PME drawback is its inability to perform constant pH simulations efficiently. In such simulations, the protonation states of a protein are allowed to change dynamically during the simulation. The description of this process requires a separate evaluation of the energies for each protonation state. This can not be calculated efficiently with PME as the algorithm requires a repeated FFT for each state, which leads to a linear overhead with respect to the number of states. For a fast approximation of pairwise Coulombic interactions, which does not suffer from PME drawbacks, the Fast Multipole Method (FMM) has been implemented and fully parallelized with CUDA. To assure the optimal FMM performance for diverse MD systems multiple parallelization strategies have been developed. The algorithm has been efficiently incorporated into GROMACS and subsequently tested to determine the optimal FMM parameter set for MD simulations. Finally, the FMM has been incorporated into GROMACS to allow for out-of-the-box electrostatic calculations. The performance of the single-GPU FMM implementation, tested in GROMACS 2019, achieves about a third of highly optimized CUDA PME performance when simulating systems with uniform particle distributions. However, the FMM is expected to outperform PME at high parallelization because the FMM global communication overhead is minimal compared to that of PME. Further, the FMM has been enhanced to provide the energies of an arbitrary number of titratable sites as needed in the constant-pH method. The extension is not fully optimized yet, but the first results show the strength of the FMM for constant pH simulations. For a relatively large system with half a million particles and more than a hundred titratable sites, a straightforward approach to compute alternative energies requires the repetition of a simulation for each state of the sites. The FMM calculates all energy terms only a factor 1.5 slower than a single simulation step. Further improvements of the GPU implementation are expected to yield even more speedup compared to the actual implementation.2021-11-1
Reaction Path Averaging: Characterizing the Structural Response of the DNA Double Helix to Electron Transfer
A polarizable environment, prominently the solvent, responds to electronic
changes in biomolecules rapidly. The knowledge of conformational relaxation of
the biomolecule itself, however, may be scarce or missing. In this work, we
describe in detail the structural changes in DNA undergoing electron transfer
between two adjacent nucleobases. We employ an approach based on averaging of
tens to hundreds of thousands of nonequilibrium trajectories generated with
molecular dynamics simulation, and a reduction of dimensionality suitable for
DNA. We show that the conformational response of the DNA proceeds along a
single collective coordinate that represents the relative orientation of two
consecutive base pairs, namely, a combination of helical parameters shift and
tilt. The structure of DNA relaxes on time scales reaching nanoseconds,
contributing marginally to the relaxation of energies, which is dominated by
the modes of motion of the aqueous solvent. The concept of reaction path
averaging (RPA), conveniently exploited in this context, makes it possible to
filter out any undesirable noise from the nonequilibrium data, and is
applicable to any chemical process in general.Comment: 45 pages, 20 figures, published, added Supplementary informatio
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