A system integration framework for coupled multiphysics simulations

Jiao, Xiangmin; Zheng, Gengbin; Alexander, Phillip; Campbell, Michael T.; Lawlor, Orion Sky; Norris, John M.; Haselbacher, Andreas; Heath, Michael T.

doi:10.1007/s00366-006-0034-x

Cited by 24 publications

(14 citation statements)

References 17 publications

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“…Besides general-purpose component frameworks, several domainspecific component frameworks have been created in many domains, including climate modeling [61], rocket simulation [125], and engineering [188]. Contract negotiation and its usage for modeling and fulfilling non-functional requirements is discussed in [198,67,143].…”

Section: Component Models In Hpcmentioning

confidence: 99%

Performance-aware component composition for GPU-based systems

Dastgeer¹

2014

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This thesis adresses issues associated with efficiently programming modern heterogeneous GPU-based systems, containing multicore CPUs and one or more programmable Graphics Processing Units (GPUs). We use ideas from component-based programming to address programming, performance and portability issues of these heterogeneous systems. Specifically, we present three approaches that all use the idea of having multiple implementations for each computation; performance is achieved/retained either a) by selecting a suitable implementation for each computation on a given platform or b) by dividing the computation work across different implementations running on CPU and GPU devices in parallel.In the first approach, we work on a skeleton programming library (SkePU) that provides high-level abstraction while making intelligent implementation selection decisions underneath either before or during the actual program execution. In the second approach, we develop a composition tool that parses extra information (metadata) from XML files, makes certain decisions offline, and, in the end, generates code for making the final decisions at runtime. The third approach is a framework that uses source-code annotations and program analysis to generate code for the runtime library to make the selection decision at runtime. With a generic performance modeling API alongside program analysis capabilities, it supports online tuning as well as complex program transformations.These approaches differ in terms of genericity, intrusiveness, capabilities and knowledge about the program source-code; however, they all demonstrate usefulness of component programming techniques for programming GPU-based systems. With experimental evaluation, we demonstrate how all three approaches, although different in their own way, provide good performance on different GPU-based systems for a variety of applications.This work has been supported by two EU FP7 projects (PEP-PHER, EXCESS) and by SeRC. Populärvetenskaplig sammanfattningAtt få varje generation av datorer att fungera snabbareär viktigt för samhä-llets utveckling och tillväxt. Traditionellt hade de flesta datorer bara en general-purpose processor (den så kallade CPU:n) som bara kunde exekvera en beräkningsuppgift i taget. Under det senasteårtiondet har dock flerkärniga och mångkärniga processorer blivit vanliga, och datorer har också blivit mer heterogena. Ett modernt datorsystem innehåller vanligtvis flerän en CPU, tillsammans med specialprocessorer såsom grafikprocessorer (GPU:er) som ar anpassade för att kunna exekvera vissa typer av beräkningar effektivarë an CPU:er. Vi kallar ett sådant system med en eller flera GPU:er för ett GPU-baserat system. GPU:er i sådana system har sitt eget separata minne, och för att kunna köra en beräkning på en GPU så behöver man vanligtvis flytta all indata till GPU:ns minne och sedan hämta tillbaka resultatet när beräkningenär klar.Programmeringen av GPU-baserade systemär icke-trivialt av flera anledningar: (1) CPU:er och GPU:er kräver olika programmeringsexper...

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Section: Component Models In Hpcmentioning

confidence: 99%

Performance-aware component composition for GPU-based systems

Dastgeer¹

2014

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“…Our original motivating application was Rocstar [10], a very large-scale detailed whole-system multiphysics simulation of solid rocket motors under normal and abnormal operating conditions. Another motivating application was FLASH [11], [12], [13], a large scale multi-dimensional code used to study astrophysical fluids.…”

Section: Motivationmentioning

confidence: 99%

Automatic Handling of Global Variables for Multi-threaded MPI Programs

Zheng

Negara

Mendes

et al. 2011

2011 IEEE 17th International Conference on Parallel and Distributed Systems

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Abstract-Hybrid programming models, such as MPI combined with threads, are one of the most efficient ways to write parallel applications for current machines comprising multi-socket/multicore nodes and an interconnection network. Global variables in legacy MPI applications, however, present a challenge because they may be accessed by multiple MPI threads simultaneously. Thus, transforming legacy MPI applications to be thread-safe in order to exploit multi-core architectures requires proper handling of global variables.In this paper, we present three approaches to eliminate global variables to ensure thread-safety for an MPI program. These approaches include: (a) a compiler-based refactoring technique, using a Photran-based tool as an example, which automates the source-to-source transformation for programs written in Fortran; (b) a technique based on a global offset table (GOT); and (c) a technique based on thread local storage (TLS). The second and third methods automatically detect global variables and privatize them for each thread at runtime. We discuss the advantages and disadvantages of these approaches and compare their performance using both synthetic benchmarks, such as the NAS Benchmarks, and a real scientific application, the FLASH code.

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“…In past decades, different disciplines within the overall geoscientific subject area have used coupling (e.g., numerical modeling of high-level-radioactive-waste disposal repositories) and fusion (e.g., remote sensing data processing) techniques widely, but there has been little interaction between the separate disciplines (e.g., Hudson et al, 2005;Li, 2006;Jiao et al, 2006;Sasai et al, 2005). The principles of mathematics and physics, though, form a common basis for the component disciplines of all geosciences.…”

Section: Introductionmentioning

confidence: 99%