Systematic investigation of mechanistic computational models can augment traditional laboratory and clinical studies, helping identify the factors driving a treatment's success or failure.
Therapies act on this combined cancer-host system, sometimes with unexpected results. = ,īackgroundCancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous host systems. We present the high-level programming model of the EMEWS framework and demonstrate how it is used to integrate an active learning ME algorithm to dynamically and efficiently characterize the more » parameter space of a large and complex, distributed Message Passing Interface (MPI) agent-based infectious disease model. EMEWS combines novel stateful tasks with traditional run-to-completion many task computing (MTC) and solves many problems relevant to highperformance workflows, including scaling to very large numbers (millions) of tasks, maintaining state and locality information, and enabling effective multiple-language problem solving. In this paper we describe the Extreme-scale Model Exploration with Swift (EMEWS) framework, which is capable of efficiently composing and executing large ensembles of simulations and other “black box” scientific applications while integrating model exploration (ME) algorithms developed with the use of widely available 3rd-party libraries written in popular languages such as R and Python. However, the development, validation and use of ABMs is hampered by the need to execute very large numbers of simulations in order to identify their behavioral properties, a challenge accentuated by the computational cost of running realistic, large-scale, potentially distributed ABM simulations.
#Vienna ensemble pro mtc full#
The 53 full papers were carefully reviewed and selected from 135 submissions.
This book constitutes the refereed proceedings of the 21st IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2020, held in Valencia, Spain, in November 2020.
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