Real-Time Simulation and Prognosis of Smoke Propagation in Compartments Using a GPU

The evaluation of life safety in buildings in case of fire is often based on smoke spread calculations. However, recent simulation models – in general, based on computational fluid dynamics – often require long execution times or high-performance computers to achieve simulation results in or faster than real-time. Therefore, the objective of this study is the development of a concept for the real-time and prognosis simulation of smoke propagation in compartments using a graphics processing unit (GPU). The developed concept is summarized in an expandable open source software basis, called JuROr (Jülich's Real-time simulation within ORPHEUS). JuROr simulates buoyancy-driven, turbulent smoke spread based on a reduced modeling approach using finite differences and a Large Eddy Simulation turbulence model to solve the incompressible Navier-Stokes and energy equations. This reduced model is fully adapted to match the target hardware of highly parallel computer architectures. Thereby, the code is written in the object-oriented programming language C++ and the pragma-based programming model OpenACC. This model ensures to maintain a single source code, which can be executed in serial and parallel on various architectures. Further, the study provides a proof of JuROr's concept to balance sufficient accuracy and practicality. First, the code was successfully verified using unit and (semi-) analytical tests. Then, the underlying model was validated by comparing the numerical results to the experimental results of scenarios relevant for fire protection. Thereby, verification and validation showed acceptable accuracy for JuROr's application. Lastly, the performance criteria of JuROr – being real-time and prognosis capable with comparable performance across various architectures – was successfully evaluated. Here, JuROr also showed high speedup results on a GPU and faster time-to-solution compared to the established Fire Dynamics Simulator. These results show JuROr's practicality.Die Bewertung der Personensicherheit bei Feuer in Gebäuden basiert häufig auf Berechnungen zur Rauchausbreitung. Bisherige Simulationsmodelle – im Allgemeinen basierend auf numerischer Strömungsdynamik – erfordern jedoch lange Ausführungszeiten oder Hochleistungsrechner, um Simulationsergebnisse in und schneller als Echtzeit liefern zu können. Daher ist das Ziel dieser Arbeit die Entwicklung eines Konzeptes für die Echtzeit- und Prognosesimulation der Rauchausbreitung in Gebäuden mit Hilfe eines Grafikprozessors (GPU). Zusammengefasst ist das entwickelte Konzept in einer erweiterbaren Open-Source-Software, genannt JuROr (Jülich's Real-time Simulation in ORPHEUS). JuROr simuliert die Ausbreitung von auftriebsgetriebenem, turbulentem Rauch basierend auf einem reduzierten Modellierungsansatz mit finiten Differenzen und einem Large Eddy Simulation Turbulenzmodell, um inkompressible Navier- Stokes und Energiegleichungen zu lösen. Das reduzierte Modell ist voll- ständig angepasst an hochparallele Computerarchitekturen. Dabei ist der Code implementiert mit C++ und OpenACC. Dies hat den Vorteil mit nur einem Quellcode verschiedenste serielle und parallele Ausführungen des Programms für unterschiedliche Architekturen erstellen zu können. Die Studie liefert weiterhin einen Konzeptnachweis dafür, ausreichende Genauigkeit und Praktikabilität im Gleichgewicht zu halten. Zunächst wurde der Code erfolgreich mit Modul- und (semi-) analytischen Tests verifiziert. Dann wurde das zugrundeliegende Modell durch einen Vergleich der numerischen mit den experimentellen Ergebnissen für den Brandschutz relevanter Szenarien validiert. Die Verifizierung und Validierung zeigten dabei ausreichende Genauigkeit für JuROr. Zuletzt, wurden die Kriterien von JuROr – echtzeit- und prognosefähig zu sein mit vergleichbarer Leistung auf unterschiedlichsten Architekturen – erfolgreich geprüft. Zudem zeigte JuROr hohe Beschleunigungseffekte auf einer GPU und schnellere Lösungszeiten im Vergleich zum etablierten Fire Dynamics Simulator. Diese Ergebnisse zeigen JuROr's Praktikabilität

CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences

This report documents the results of a study to address the long range, strategic planning required by NASA's Revolutionary Computational Aerosciences (RCA) program in the area of computational fluid dynamics (CFD), including future software and hardware requirements for High Performance Computing (HPC). Specifically, the "Vision 2030" CFD study is to provide a knowledge-based forecast of the future computational capabilities required for turbulent, transitional, and reacting flow simulations across a broad Mach number regime, and to lay the foundation for the development of a future framework and/or environment where physics-based, accurate predictions of complex turbulent flows, including flow separation, can be accomplished routinely and efficiently in cooperation with other physics-based simulations to enable multi-physics analysis and design. Specific technical requirements from the aerospace industrial and scientific communities were obtained to determine critical capability gaps, anticipated technical challenges, and impediments to achieving the target CFD capability in 2030. A preliminary development plan and roadmap were created to help focus investments in technology development to help achieve the CFD vision in 2030

Acceleration Techniques for Industrial Large Eddy Simulation with High-Order Methods on CPU-GPU Clusters

One of the NASA's 2030 CFD Vision document key finding is that the use of CFD in the aerospace design process is severely limited by the inability to accurately and reliably predict turbulent flows with significant regions of separation. Scale-resolving simulations such as large eddy simulation (LES) are increasingly utilized with more complex problems such as flow over high lift configurations and through aircraft engines. The present work has the overall objective of reducing the computational cost of industrial LES. The high-order flux reconstruction (FR) method is used as the spatial discretization scheme. First, two acceleration techniques are investigated: the p-multigrid algorithm and Mach number preconditioning. The Weiss and Smith low Mach number preconditioner is used together with the p-multigrid method, and the third order explicit Runge-Kutta (RK3) scheme is considered as the smoother to reduce memory requirements. Mach number preconditioning significantly increased the efficiency of the p-multigrid method. For unsteady simulations, the preconditioner helped with the efficiency of the p-multigrid with larger physical time steps. In most steady cases, the preconditioned p-multigrid approach is comparable to or faster than the implicit LU-SGS algorithm and requires less memory, specially for p 2 schemes. An efficient implementation of the FR method is done for modern GPU clusters and the speedup is investigated for different polynomial orders and cell types. Approaches to improve the parallel efficiency of multi-GPU simulations are also studied. The simulation node-hour cost on the Summit supercomputer is reduced by a factor of 50 for hexahedron cells and up to 200 for tetrahedron cells. Two low memory implicit time integration methods are implemented on GPUs: the matrix-free GMRES solver and a novel local GMRES-SGS method. Parametric studies are done to evaluate their performance on LES benchmark cases. On the High-Lift Common Research Model case for the 2021 4th AIAA High-Lift Prediction Workshop, both GPU implicit time methods provide an additional speedup of 14 and 68, respectively, over the GPU explicit time simulation

Accessible software frameworks for reproducible image analysis of host-pathogen interactions

Um die Mechanismen hinter lebensgefährlichen Krankheiten zu verstehen, müssen die zugrundeliegenden Interaktionen zwischen den Wirtszellen und krankheitserregenden Mikroorganismen bekannt sein. Die kontinuierlichen Verbesserungen in bildgebenden Verfahren und Computertechnologien ermöglichen die Anwendung von Methoden aus der bildbasierten Systembiologie, welche moderne Computeralgorithmen benutzt um das Verhalten von Zellen, Geweben oder ganzen Organen präzise zu messen. Um den Standards des digitalen Managements von Forschungsdaten zu genügen, müssen Algorithmen den FAIR-Prinzipien (Findability, Accessibility, Interoperability, and Reusability) entsprechen und zur Verbreitung ebenjener in der wissenschaftlichen Gemeinschaft beitragen. Dies ist insbesondere wichtig für interdisziplinäre Teams bestehend aus Experimentatoren und Informatikern, in denen Computerprogramme zur Verbesserung der Kommunikation und schnellerer Adaption von neuen Technologien beitragen können. In dieser Arbeit wurden daher Software-Frameworks entwickelt, welche dazu beitragen die FAIR-Prinzipien durch die Entwicklung von standardisierten, reproduzierbaren, hochperformanten, und leicht zugänglichen Softwarepaketen zur Quantifizierung von Interaktionen in biologischen System zu verbreiten. Zusammenfassend zeigt diese Arbeit wie Software-Frameworks zu der Charakterisierung von Interaktionen zwischen Wirtszellen und Pathogenen beitragen können, indem der Entwurf und die Anwendung von quantitativen und FAIR-kompatiblen Bildanalyseprogrammen vereinfacht werden. Diese Verbesserungen erleichtern zukünftige Kollaborationen mit Lebenswissenschaftlern und Medizinern, was nach dem Prinzip der bildbasierten Systembiologie zur Entwicklung von neuen Experimenten, Bildgebungsverfahren, Algorithmen, und Computermodellen führen wird

XSEDE: eXtreme Science and Engineering Discovery Environment Third Quarter 2012 Report

The Extreme Science and Engineering Discovery Environment (XSEDE) is the most advanced, powerful, and robust collection of integrated digital resources and services in the world. It is an integrated cyberinfrastructure ecosystem with singular interfaces for allocations, support, and other key services that researchers can use to interactively share computing resources, data, and expertise.This a report of project activities and highlights from the third quarter of 2012.National Science Foundation, OCI-105357

Software for Exascale Computing - SPPEXA 2016-2019

This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest