5,091 research outputs found
Efficient Parallelization of Short-Range Molecular Dynamics Simulations on Many-Core Systems
This article introduces a highly parallel algorithm for molecular dynamics
simulations with short-range forces on single node multi- and many-core
systems. The algorithm is designed to achieve high parallel speedups for
strongly inhomogeneous systems like nanodevices or nanostructured materials. In
the proposed scheme the calculation of the forces and the generation of
neighbor lists is divided into small tasks. The tasks are then executed by a
thread pool according to a dependent task schedule. This schedule is
constructed in such a way that a particle is never accessed by two threads at
the same time.Benchmark simulations on a typical 12 core machine show that the
described algorithm achieves excellent parallel efficiencies above 80 % for
different kinds of systems and all numbers of cores. For inhomogeneous systems
the speedups are strongly superior to those obtained with spatial
decomposition. Further benchmarks were performed on an Intel Xeon Phi
coprocessor. These simulations demonstrate that the algorithm scales well to
large numbers of cores.Comment: 12 pages, 8 figure
Towards Energy Efficiency in Heterogeneous Processors: Findings on Virtual Screening Methods
The integration of the latest breakthroughs in computational modeling and high performance computing (HPC) has leveraged advances in the fields of healthcare and drug discovery, among others. By integrating all these developments together, scientists are creating new exciting personal therapeutic strategies for living longer that were unimaginable not that long ago. However, we are witnessing the biggest revolution in HPC in the last decade. Several graphics processing unit architectures have established their niche in the HPC arena but at the expense of an excessive power and heat. A solution for this important problem is based on heterogeneity. In this paper, we analyze power consumption on heterogeneous systems, benchmarking a bioinformatics kernel within the framework of virtual screening methods. Cores and frequencies are tuned to further improve the performance or energy efficiency on those architectures. Our experimental results show that targeted low‐cost systems are the lowest power consumption platforms, although the most energy efficient platform and the best suited for performance improvement is the Kepler GK110 graphics processing unit from Nvidia by using compute unified device architecture. Finally, the open computing language version of virtual screening shows a remarkable performance penalty compared with its compute unified device architecture counterpart.Ingeniería, Industria y Construcció
QuantumATK: An integrated platform of electronic and atomic-scale modelling tools
QuantumATK is an integrated set of atomic-scale modelling tools developed
since 2003 by professional software engineers in collaboration with academic
researchers. While different aspects and individual modules of the platform
have been previously presented, the purpose of this paper is to give a general
overview of the platform. The QuantumATK simulation engines enable
electronic-structure calculations using density functional theory or
tight-binding model Hamiltonians, and also offers bonded or reactive empirical
force fields in many different parametrizations. Density functional theory is
implemented using either a plane-wave basis or expansion of electronic states
in a linear combination of atomic orbitals. The platform includes a long list
of advanced modules, including Green's-function methods for electron transport
simulations and surface calculations, first-principles electron-phonon and
electron-photon couplings, simulation of atomic-scale heat transport, ion
dynamics, spintronics, optical properties of materials, static polarization,
and more. Seamless integration of the different simulation engines into a
common platform allows for easy combination of different simulation methods
into complex workflows. Besides giving a general overview and presenting a
number of implementation details not previously published, we also present four
different application examples. These are calculations of the phonon-limited
mobility of Cu, Ag and Au, electron transport in a gated 2D device, multi-model
simulation of lithium ion drift through a battery cathode in an external
electric field, and electronic-structure calculations of the
composition-dependent band gap of SiGe alloys.Comment: Submitted to Journal of Physics: Condensed Matte
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
TensorMD: Scalable Tensor-Diagram based Machine Learning Interatomic Potential on Heterogeneous Many-Core Processors
Molecular dynamics simulations have emerged as a potent tool for
investigating the physical properties and kinetic behaviors of materials at the
atomic scale, particularly in extreme conditions. Ab initio accuracy is now
achievable with machine learning based interatomic potentials. With recent
advancements in high-performance computing, highly accurate and large-scale
simulations become feasible. This study introduces TensorMD, a new machine
learning interatomic potential (MLIP) model that integrates physical principles
and tensor diagrams. The tensor formalism provides a more efficient computation
and greater flexibility for use with other scientific codes. Additionally, we
proposed several portable optimization strategies and developed a highly
optimized version for the new Sunway supercomputer. Our optimized TensorMD can
achieve unprecedented performance on the new Sunway, enabling simulations of up
to 52 billion atoms with a time-to-solution of 31 ps/step/atom, setting new
records for HPC + AI + MD
Roadmap on Electronic Structure Codes in the Exascale Era
Electronic structure calculations have been instrumental in providing many
important insights into a range of physical and chemical properties of various
molecular and solid-state systems. Their importance to various fields,
including materials science, chemical sciences, computational chemistry and
device physics, is underscored by the large fraction of available public
supercomputing resources devoted to these calculations. As we enter the
exascale era, exciting new opportunities to increase simulation numbers, sizes,
and accuracies present themselves. In order to realize these promises, the
community of electronic structure software developers will however first have
to tackle a number of challenges pertaining to the efficient use of new
architectures that will rely heavily on massive parallelism and hardware
accelerators. This roadmap provides a broad overview of the state-of-the-art in
electronic structure calculations and of the various new directions being
pursued by the community. It covers 14 electronic structure codes, presenting
their current status, their development priorities over the next five years,
and their plans towards tackling the challenges and leveraging the
opportunities presented by the advent of exascale computing.Comment: Submitted as a roadmap article to Modelling and Simulation in
Materials Science and Engineering; Address any correspondence to Vikram
Gavini ([email protected]) and Danny Perez ([email protected]
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