13,004 research outputs found
A GPU-accelerated package for simulation of flow in nanoporous source rocks with many-body dissipative particle dynamics
Mesoscopic simulations of hydrocarbon flow in source shales are challenging,
in part due to the heterogeneous shale pores with sizes ranging from a few
nanometers to a few micrometers. Additionally, the sub-continuum fluid-fluid
and fluid-solid interactions in nano- to micro-scale shale pores, which are
physically and chemically sophisticated, must be captured. To address those
challenges, we present a GPU-accelerated package for simulation of flow in
nano- to micro-pore networks with a many-body dissipative particle dynamics
(mDPD) mesoscale model. Based on a fully distributed parallel paradigm, the
code offloads all intensive workloads on GPUs. Other advancements, such as
smart particle packing and no-slip boundary condition in complex pore
geometries, are also implemented for the construction and the simulation of the
realistic shale pores from 3D nanometer-resolution stack images. Our code is
validated for accuracy and compared against the CPU counterpart for speedup. In
our benchmark tests, the code delivers nearly perfect strong scaling and weak
scaling (with up to 512 million particles) on up to 512 K20X GPUs on Oak Ridge
National Laboratory's (ORNL) Titan supercomputer. Moreover, a single-GPU
benchmark on ORNL's SummitDev and IBM's AC922 suggests that the host-to-device
NVLink can boost performance over PCIe by a remarkable 40\%. Lastly, we
demonstrate, through a flow simulation in realistic shale pores, that the CPU
counterpart requires 840 Power9 cores to rival the performance delivered by our
package with four V100 GPUs on ORNL's Summit architecture. This simulation
package enables quick-turnaround and high-throughput mesoscopic numerical
simulations for investigating complex flow phenomena in nano- to micro-porous
rocks with realistic pore geometries
Genetic Programming for Smart Phone Personalisation
Personalisation in smart phones requires adaptability to dynamic context
based on user mobility, application usage and sensor inputs. Current
personalisation approaches, which rely on static logic that is developed a
priori, do not provide sufficient adaptability to dynamic and unexpected
context. This paper proposes genetic programming (GP), which can evolve program
logic in realtime, as an online learning method to deal with the highly dynamic
context in smart phone personalisation. We introduce the concept of
collaborative smart phone personalisation through the GP Island Model, in order
to exploit shared context among co-located phone users and reduce convergence
time. We implement these concepts on real smartphones to demonstrate the
capability of personalisation through GP and to explore the benefits of the
Island Model. Our empirical evaluations on two example applications confirm
that the Island Model can reduce convergence time by up to two-thirds over
standalone GP personalisation.Comment: 43 pages, 11 figure
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
Time integration and steady-state continuation for 2d lubrication equations
Lubrication equations allow to describe many structurin processes of thin
liquid films. We develop and apply numerical tools suitable for their analysis
employing a dynamical systems approach. In particular, we present a time
integration algorithm based on exponential propagation and an algorithm for
steady-state continuation. In both algorithms a Cayley transform is employed to
overcome numerical problems resulting from scale separation in space and time.
An adaptive time-step allows to study the dynamics close to hetero- or
homoclinic connections. The developed framework is employed on the one hand to
analyse different phases of the dewetting of a liquid film on a horizontal
homogeneous substrate. On the other hand, we consider the depinning of drops
pinned by a wettability defect. Time-stepping and path-following are used in
both cases to analyse steady-state solutions and their bifurcations as well as
dynamic processes on short and long time-scales. Both examples are treated for
two- and three-dimensional physical settings and prove that the developed
algorithms are reliable and efficient for 1d and 2d lubrication equations,
respectively.Comment: 33 pages, 16 figure
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
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