Abstract
New applications that can exploit emerging exascale computing resources efficiently, while providing meaningful scientific results, are eagerly anticipated. Multi-scale models, especially multi-scale applications, will assuredly run at the exascale. We have established that a class of multi-scale applications implementing the heterogeneous multi-scale model follows, a heterogeneous multi-scale computing (HMC) pattern, which typically features a macroscopic model synchronising numerous independent microscopic model simulations. Consequently, communication between microscopic simulations is limited. Furthermore, a surrogate model can often be introduced between macro-scale and micro-scale models to interpolate required data from previously computed micro-scale simulations, thereby substantially reducing the number of micro-scale simulations. Nonetheless, HMC applications, though versatile, remain constrained by load balancing issues. We discuss two main issues: the a priori unknown and variable execution time of microscopic simulations, and the dynamic number of micro-scale simulations required. We tackle execution time variability using a pilot job mechanism to handle internal queuing and multiple sub-model execution on large-scale supercomputers, together with a data-informed execution time prediction model. To dynamically select the number of micro-scale simulations, the HMC pattern automatically detects and identifies three surrogate model phases that help control the available and used core amount. After relevant phase detection and micro-scale simulation scheduling, any idle cores can be used for surrogate model update or for processor release back to the system. We demonstrate HMC performance by testing it on two representative multi-scale applications. We conclude that, considering the subtle interplay between the macroscale model, surrogate models and micro-scale simulations, HMC provides a promising path towards exascale for many multiscale applications.
Highlights
Science has the ability to describe phenomena and often to predict their occurrence and outcome in an accurate and rapid manner
We will discuss our results for addressing the issues of load balancing both due to variable execution time and to a dynamic number of micro-model using two representative applications, which are a nano-materials system and a red blood cell suspension system, respectively
Ab initio physical models are unfeasible beyond the smallest scales, density functional theory and molecular dynamics are the models of choice of material scientists, these models overlook the geometrical and structural complexity of the engineering scale, which induces a heterogeneous conglomeration of local mechanical states
Summary
Science has the ability to describe phenomena and often to predict their occurrence and outcome in an accurate and rapid manner. These phenomena naturally, and computationally, are multi-scale both in time and space [4, 13, 15, 16, 18, 28, 32]. By investigating a broad range of multi-scale applications from many different domains, we have identified a set of generic patterns that are common to these applications These patterns can be seen as a “high-level call sequences that exploit the functional decomposition of multi-scale models in terms of single scale models” [5].We believe
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