Title: MaMiCo: Parallel Noise Reduction for Multi-instance Molecular-Continuum Flow Simulation
Authors: Jarmatz, Piet 
Neumann, Philipp 
Language: en
Subject (DDC): DDC::000 Informatik, Informationswissenschaft, allgemeine Werke
DDC::500 Naturwissenschaften und Mathematik
Issue Date: 2019
Document Type: Conference Object
Project: Task-basierte Lastverteilung und Auto-Tuning in der Partikelsimulation 
Journal / Series / Working Paper (HSU): Lecture notes in computer science 
Volume: 11539
Page Start: 451
Page End: 464
Conference: 20th International Conference on Computational Science ICCS 2020 
Transient molecular-continuum coupled flow simulations often suffer from high thermal noise, created by fluctuating hydrodynamics within the molecular dynamics (MD) simulation. Multi-instance MD computations are an approach to extract smooth flow field quantities on rather short time scales, but they require a huge amount of computational resources. Filtering particle data using signal processing methods to reduce numerical noise can significantly reduce the number of instances necessary. This leads to improved stability and reduced computational cost in the molecular-continuum setting. We extend the Macro-Micro-Coupling tool (MaMiCo) – a software to couple arbitrary continuum and MD solvers – by a new parallel interface for universal MD data analytics and post-processing, especially for noise reduction. It is designed modularly and compatible with multi-instance sampling. We present a Proper Orthogonal Decomposition (POD) implementation of the interface, capable of massively parallel noise filtering. The resulting coupled simulation is validated using a three-dimensional Couette flow scenario. We quantify the denoising, conduct performance benchmarks and scaling tests on a supercomputing platform. We thus demonstrate that the new interface enables massively parallel data analytics and post-processing in conjunction with any MD solver coupled to MaMiCo.
Organization Units (connected with the publication): High Performance Computing 
URL: https://api.elsevier.com/content/abstract/scopus_id/85067611215
ISBN: 9783030227463
ISSN: 03029743
DOI: 10.1007/978-3-030-22747-0_34
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