6,965 research outputs found
Developing performance-portable molecular dynamics kernels in Open CL
This paper investigates the development of a molecular dynamics code that is highly portable between architectures. Using OpenCL, we develop an implementation of Sandiaâs miniMD benchmark that achieves good levels of performance across a wide range of hardware: CPUs, discrete GPUs and integrated GPUs.
We demonstrate that the performance bottlenecks of miniMDâs short-range force calculation kernel are the same across these architectures, and detail a number of platform- agnostic optimisations that improve its performance by at least 2x on all hardware considered. Our complete code is shown to be 1.7x faster than the original miniMD, and at most 2x slower than implementations individually hand-tuned for a specific architecture
Molecular dynamics recipes for genome research
Molecular dynamics (MD) simulation allows one to predict the time evolution of a system of interacting particles. It is widely used in physics, chemistry and biology to address specific questions about the structural properties and dynamical mechanisms of model systems. MD earned a great success in genome research, as it proved to be beneficial in sorting pathogenic from neutral genomic mutations. Considering their computational requirements, simulations are commonly performed on HPC computing devices, which are generally expensive and hard to administer. However, variables like the software tool used for modeling and simulation or the size of the molecule under investigation might make one hardware type or configuration more advantageous than another or even make the commodity hardware definitely suitable for MD studies. This work aims to shed lights on this aspect
Multi-Architecture Monte-Carlo (MC) Simulation of Soft Coarse-Grained Polymeric Materials: SOft coarse grained Monte-carlo Acceleration (SOMA)
Multi-component polymer systems are important for the development of new
materials because of their ability to phase-separate or self-assemble into
nano-structures. The Single-Chain-in-Mean-Field (SCMF) algorithm in conjunction
with a soft, coarse-grained polymer model is an established technique to
investigate these soft-matter systems. Here we present an im- plementation of
this method: SOft coarse grained Monte-carlo Accelera- tion (SOMA). It is
suitable to simulate large system sizes with up to billions of particles, yet
versatile enough to study properties of different kinds of molecular
architectures and interactions. We achieve efficiency of the simulations
commissioning accelerators like GPUs on both workstations as well as
supercomputers. The implementa- tion remains flexible and maintainable because
of the implementation of the scientific programming language enhanced by
OpenACC pragmas for the accelerators. We present implementation details and
features of the program package, investigate the scalability of our
implementation SOMA, and discuss two applications, which cover system sizes
that are difficult to reach with other, common particle-based simulation
methods
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
Highly accelerated simulations of glassy dynamics using GPUs: caveats on limited floating-point precision
Modern graphics processing units (GPUs) provide impressive computing
resources, which can be accessed conveniently through the CUDA programming
interface. We describe how GPUs can be used to considerably speed up molecular
dynamics (MD) simulations for system sizes ranging up to about 1 million
particles. Particular emphasis is put on the numerical long-time stability in
terms of energy and momentum conservation, and caveats on limited
floating-point precision are issued. Strict energy conservation over 10^8 MD
steps is obtained by double-single emulation of the floating-point arithmetic
in accuracy-critical parts of the algorithm. For the slow dynamics of a
supercooled binary Lennard-Jones mixture, we demonstrate that the use of
single-floating point precision may result in quantitatively and even
physically wrong results. For simulations of a Lennard-Jones fluid, the
described implementation shows speedup factors of up to 80 compared to a serial
implementation for the CPU, and a single GPU was found to compare with a
parallelised MD simulation using 64 distributed cores.Comment: 12 pages, 7 figures, to appear in Comp. Phys. Comm., HALMD package
licensed under the GPL, see http://research.colberg.org/projects/halm
A GPU-accelerated Direct-sum Boundary Integral Poisson-Boltzmann Solver
In this paper, we present a GPU-accelerated direct-sum boundary integral
method to solve the linear Poisson-Boltzmann (PB) equation. In our method, a
well-posed boundary integral formulation is used to ensure the fast convergence
of Krylov subspace based linear algebraic solver such as the GMRES. The
molecular surfaces are discretized with flat triangles and centroid
collocation. To speed up our method, we take advantage of the parallel nature
of the boundary integral formulation and parallelize the schemes within CUDA
shared memory architecture on GPU. The schemes use only
size-of-double device memory for a biomolecule with triangular surface
elements and partial charges. Numerical tests of these schemes show
well-maintained accuracy and fast convergence. The GPU implementation using one
GPU card (Nvidia Tesla M2070) achieves 120-150X speed-up to the implementation
using one CPU (Intel L5640 2.27GHz). With our approach, solving PB equations on
well-discretized molecular surfaces with up to 300,000 boundary elements will
take less than about 10 minutes, hence our approach is particularly suitable
for fast electrostatics computations on small to medium biomolecules
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