Survey of multiscale and multiphysics applications and communities

Multiscale and multiphysics applications are now commonplace, and many researchers focus on combining existing models to construct new multiscale models. This concise review of multiscale applications and their source communities in the EU and US outlines differences and commonalities among approaches and identifies areas in which collaboration between disciplines could be particularly beneficial. Because different communities adopt very different approaches to constructing multiscale simulations, and simulations on a length scale of a few meters and a time scale of a few hours can be found in many multiscale research domains, communities might receive additional benefit from sharing methods that are geared towards these scales. The Web extra is the full literature list mentioned in the article.This work has received funding from the MAPPER EC-FP7 project (grant no. RI-261507)

Putting the User at the Centre of the Grid: Simplifying Usability and Resource Selection for High Performance Computing

Computer simulation is finding a role in an increasing number of scientific disciplines, concomitant with the rise in available computing power. Realizing this inevitably re- quires access to computational power beyond the desktop, making use of clusters, supercomputers, data repositories, networks and distributed aggregations of these re- sources. Accessing one such resource entails a number of usability and security prob- lems; when multiple geographically distributed resources are involved, the difficulty is compounded. However, usability is an all too often neglected aspect of computing on e-infrastructures, although it is one of the principal factors militating against the widespread uptake of distributed computing. The usability problems are twofold: the user needs to know how to execute the applications they need to use on a particular resource, and also to gain access to suit- able resources to run their workloads as they need them. In this thesis we present our solutions to these two problems. Firstly we propose a new model of e-infrastructure resource interaction, which we call the user–application interaction model, designed to simplify executing application on high performance computing resources. We describe the implementation of this model in the Application Hosting Environment, which pro- vides a Software as a Service layer on top of distributed e-infrastructure resources. We compare the usability of our system with commonly deployed middleware tools using five usability metrics. Our middleware and security solutions are judged to be more usable than other commonly deployed middleware tools. We go on to describe the requirements for a resource trading platform that allows users to purchase access to resources within a distributed e-infrastructure. We present the implementation of this Resource Allocation Market Place as a distributed multi- agent system, and show how it provides a highly flexible, efficient tool to schedule workflows across high performance computing resources

Large-scale binding affinity calculations on commodity compute clouds

In recent years, it has become possible to calculate binding affinities of compounds bound to proteins via rapid, accurate, precise and reproducible free energy calculations. This is imperative in drug discovery as well as personalized medicine. This approach is based on molecular dynamics (MD) simulations and draws on sequence and structural information of the protein and compound concerned. Free energies are determined by ensemble averages of many MD replicas, each of which requires hundreds of cores and/or GPU accelerators, which are now available on commodity cloud computing platforms; there are also requirements for initial model building and subsequent data analysis stages. To automate the process, we have developed a workflow known as the binding affinity calculator. In this paper, we focus on the software infrastructure and interfaces that we have developed to automate the overall workflow and execute it on commodity cloud platforms, in order to reliably predict their binding affinities on time scales relevant to the domains of application, and illustrate its application to two free energy methods

Flexible composition and execution of high performance, high fidelity multiscale biomedical simulations

Multiscale simulations are essential in the biomedical domain to accurately model human physiology. We present a modular approach for designing, constructing and executing multiscale simulations on a wide range of resources, from laptops to petascale supercomputers, including combinations of these. Our work features two multiscale applications, in-stent restenosis and cerebrovascular bloodflow, which combine multiple existing single-scale applications to create a multiscale simulation. These applications can be efficiently coupled, deployed and executed on computers up to the largest (peta) scale, incurring a coupling overhead of 1–10% of the total execution time

Taxonomy of Multiscale Computing Communities

We present a concise and comprehensive review of research communities which perform multiscale computing. We provide an overview of communities in a range of domains, and compare these communities to assess the level of use of multiscale methods in different research domains. Additionally, we characterize several areas where inter-disciplinary multiscale collaboration or the introduction of common and reusable methods could be particularly beneficial. We conclude that multiscale computing has become increasingly popular in recent years, that different communities adopt radically different organizational approaches, and that simulations on a length scale of a few metres and a time scale of a few hours can be found in many of the multiscale research domains. Sharing multiscale methods specifically geared towards these scales between communities may therefore be particularly beneficial. © 2011 IEEE

Constructing chained molecular dynamics. Simulations of HIV-1 protease using the application hosting environment

Many crystal structures of HIV-1 protease exist, but the number of clinically interesting drug resistant mutational patterns is far larger than the available crystal structures. Mutational protocols convert one protease sequence with available crystal structure into another that diverges by a small number of mutations. It is important that such mutational algorithms are followed by suitable multi-step equilibration protocols, e.g. using chained molecular dynamics simulations, to ensure that the desired mutant structure is an accurate representation. Previously these have been difficult to perform on computational grids due to the need to keep track of large numbers of simulations. Here we present a simple way to construct a chained MD simulation using the Application Hosting Environment

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

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