Multiscale computing for science and engineering in the era of exascale performance

Hoekstra, Alfons G.; Chopard, Bastien; Coster, D.; Zwart, Simon Portegies; Coveney, Peter V.

doi:10.1098/rsta.2018.0144

Cited by 23 publications

(28 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of the MD codes used today have been designed or adapted to run on parallel computer systems. NAMD [136], for example, designed for high-performance simulation of large biomolecular systems, has been used to simulate systems consisting of tens of millions atoms [137], although any MD codes with long-range interactions, which are communication bound, will not scale effectively in a strong sense to reach required time scales [138]. OpenMM [139] and ACEMD (the latter now uses the OpenMM kernels) [140], designed and optimized for GPUs, are among the fastest MD codes in terms of single GPU board performance.…”

Section: Software Approachesmentioning

confidence: 99%

Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

et al. 2020

Self Cite

View full text Add to dashboard Cite

A central quantity of interest in molecular biology and medicine is the free energy of binding of a molecule to a target biomacromolecule. Until recently, the accurate prediction of binding affinity had been widely regarded as out of reach of theoretical methods owing to the lack of reproducibility of the available methods, not to mention their complexity, computational cost and time-consuming procedures. The lack of reproducibility stems primarily from the chaotic nature of classical molecular dynamics (MD) and the associated extreme sensitivity of trajectories to their initial conditions. Here, we review computational approaches for both relative and absolute binding free energy calculations, and illustrate their application to a diverse set of ligands bound to a range of proteins with immediate relevance in a number of medical domains. We focus on ensemble-based methods which are essential in order to compute statistically robust results, including two we have recently developed, namely thermodynamic integration with enhanced sampling and enhanced sampling of MD with an approximation of continuum solvent. Together, these form a set of rapid, accurate, precise and reproducible free energy methods. They can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine. These applications show that individual binding affinities equipped with uncertainty quantification may be computed in a few hours on a massive scale given access to suitable high-end computing resources and workflow automation. A high level of accuracy can be achieved using these approaches.

show abstract

Section: Software Approachesmentioning

confidence: 99%

Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Mechanisms are provided in HMS to dynamically evaluate a sub‐model from another model, to extract relevant data from completed sub‐model evaluations, and to handle common errors encountered during sub‐model evaluation. The HMS approach to multiscale model development is an example of the concept of Distributed Multiscale Computing (DMC), where multiscale simulations involve the orchestrated execution of individual sub‐model components . DMC has the potential to take full advantage of emerging exascale high performance computers, but challenges remain to manage these highly‐dynamic computations on computers that have traditionally been used for monolithic codes employing domain decomposition.…”

Section: Resultsmentioning

confidence: 99%

“…Through a variety of multiscale approaches, researchers seek to bypass limits faced by discrete particle simulations [48][49][50][51][52][53][54]. This is critically important for energetic materials so that continuum simulations can incorporate the effects of microscale or mesoscale structural features in materials, while using information that is ultimately sourced from quantum mechanical simulations.…”

Section: Introductionmentioning

confidence: 99%

Forging of Hierarchical Multiscale Capabilities for Simulation of Energetic Materials

Barnes

Leiter

Larentzos

et al. 2019

Propellants Explo Pyrotec

View full text Add to dashboard Cite

We present new capabilities for investigation of microstructure in energetic material response for both explicit large‐scale and multiscale simulations. We demonstrate the computational capabilities by studying the effect of porosity on the reactive shock response of a coarse‐grain (CG) model of the energetic material cyclotrimethylene trinitramine (RDX), the non‐reactive equation of state for a porous representative volume element (RVE) of CG RDX, and utilization of available supercomputing resources for speculative sampling to accelerate hierarchical multiscale simulations. Small amounts of porosity (up to 4 %) are shown to have significant effect on the initiation of reactive CG RDX using large‐scale reactive dissipative particle dynamics simulations. Non‐reactive RVEs are shown to undergo a porosity‐dependent pore collapse at hydrostatic conditions, and an existing automation framework is shown to be easily modified for the incorporation of microstructure while retaining reliable convergence properties. A novel predictive sampling method based on use of kernel density estimators is shown to effectively accelerate time‐to‐solution in a multiscale simulation, scaling with free CPU cores, while making no assumptions about the underlying physics for the data being analyzed. These multidisciplinary studies of distinct yet connected problems combine to provide methodological insights for high‐fidelity modeling of reactive systems with microstructure.

show abstract

“…After the completion of the research described here, the MPI Forum adopted the revised model of MPI transfers [23]. This large-count model supports numbers of items to be transferred that are MPI_COUNT in size (normally 63-bit signed integers on 64-bit architectures) versus 2 31 in MPI-3.1, plus it supports all the corollary changes needed to make these work for large transfer support of point-to-point, collective, one-sided, datatype and I/O operations. A second API is provided in both C and Fortran that supports these new, BigCount modes.…”

Section: Standardization In Mpi-4mentioning

confidence: 99%

“…Hoekstra et al [31] observe that the lattice Boltzmann method possesses an algorithmic structure that will enable it to continue its scaling performance on larger supercomputers in the transition to exascale platforms. In part, this is due to the fact that, unlike some algorithms, the lattice Boltzmann royalsocietypublishing.org/journal/rsfs Interface Focus 11: 20190119 method does not possess a hard limit on scalability that inhibits performance at large scale.…”

Section: Extreme Scale Performancementioning

confidence: 99%

Towards blood flow in the virtual human: efficient self-coupling of HemeLB

et al. 2020

Self Cite

View full text Add to dashboard Cite

Many scientific and medical researchers are working towards the creation of a virtual human—a personalized digital copy of an individual—that will assist in a patient’s diagnosis, treatment and recovery. The complex nature of living systems means that the development of this remains a major challenge. We describe progress in enabling the HemeLB lattice Boltzmann code to simulate 3D macroscopic blood flow on a full human scale. Significant developments in memory management and load balancing allow near linear scaling performance of the code on hundreds of thousands of computer cores. Integral to the construction of a virtual human, we also outline the implementation of a self-coupling strategy for HemeLB. This allows simultaneous simulation of arterial and venous vascular trees based on human-specific geometries.

show abstract

Multiscale computing for science and engineering in the era of exascale performance

Cited by 23 publications

References 53 publications

Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

Forging of Hierarchical Multiscale Capabilities for Simulation of Energetic Materials

Towards blood flow in the virtual human: efficient self-coupling of HemeLB

Contact Info

Product

Resources

About