Abstract: Multiscale Modelling and Simulation (MMS) is a cornerstone in the today’s research in computational science. Simulations containing multiple models, with each model operating at a different temporal or spatial scale, are a challenging setting that frequently require innovative approaches in areas such as scale bridging, code deployment, error quantification, and scientific analysis. The aim of the MMS workshop is to encourage and consolidate the progress in this multidisciplinary research field, both in the areas of the scientific applications and the underlying infrastructures that enable these applications. Here we briefly introduce the scope of the workshop and highlight some of the key aspects of this this year’s submissions.

Abstract: Simulation of charge transport in disordered organic materials requires a huge number of quantum mechanical calculations of charge hopping parameters which are then used to compute important macroscopic properties such as the charge mobility. We present the realization of the quantum patch approach to solve a tightly coupled multiscale model for charge transport in organic materials. In contrast to previously used models, this model includes the effect of the electrostatic environment of the molecules on the energy disorder (the so-called polaron effect) explicitly and self-consistently on the quantum mechanics level. This gives rise to tasks of very different resource footprints and, on the other hand, to dependencies between very large number of tasks, representing a considerable computational challenge. Our solution concept is based on embedding the quantum mechanics tasks into a workflow, which accounts for the dependencies arising from the self-consistency loops, and applies a specific scheduling strategy based on the computational characteristics of the different task types. We have implemented the model as part of the software package Shredder and show how the implementation exploits the inherent parallelism of the multiscale model and effectively alleviate the effects of load imbalance and dependencies. The model can be used to virtually explore properties of numerous organic materials using high performance computing and so to optimize material composition, morphology and manufacturing processes.

Abstract: The ever increasing demand for higher levels of detail and accuracy in modeling of complex systems has led researchers in recent years to turn to multiscale modeling (MSM) as a mechanism to extend traditional models. The creation of multiscale models for a complex system largely involves identification of the individual scales relevant to the system and their integration into a single encompassing model. Our work is focused on the development of an adaptive computational framework for MSM that allows for rapid construction of multiscale models through composition of individual at-scale models. Our primary focus is on new scalable numerical algorithms applicable to a wide range of MSM applications. These algorithms include: i) adaptive computational strategies for MSM, ii) algorithms for scale-bridging in MSM, and iii) algorithms for development of surrogate models to reduce the computational cost associated with MSM. We will describe our computational framework for MSM and highlight its use in developing a multiscale model of an energetic material and a high-throughput capability for battery research.

Abstract: We present FabSim, a toolkit developed to simplify a range of computational tasks for researchers in diverse disciplines. FabSim is flexible, adaptable, and allows users to perform a wide range of tasks with ease. It also provides a systematic way to automate the use of resourcess, including HPC and distributed resources, and to make tasks easier to repeat by recording contextual information. To demonstrate this, we present three use cases where FabSim has enhanced our research productivity. These include simulating cerebrovascular bloodflow, modelling clay-polymer nanocomposites across multiple scales, and calculating ligand-protein binding affinities.

Abstract: Coupled multi-model simulation strategies used in various scientific such as climate, MHD, nuclear reactors and subsurface physics implementations that are tuned to achieve high efficiency while satisfying problem-dependent approximations. Often, highly specialized, domain specific tools are preferred for accurate resolution of characteristic scales in these models due to inherent difficulties in generalizing the workflow in complex multi-scale applications. In this talk, we conduct a survey of several successful frameworks and categorize them based on scalable and flexible algorithms exposed under four specific operators applied on coupled solution data: specification, transformation, transfer and orchestration. We also identify essential attributes to improve scientific productivity in terms of feature extensibility, supported numerical algorithms, computational efficiency, and software abstractions that would make an ideal coupling tool for a variety of applications.