An Integration Framework for Simulations of Solid Rocket Motors

Jiao, Xiangmin; Zheng, Gengbin; Lawlor, Orion Sky; Alexander, Phillip; Campbell, Michael T.; Heath, Michael T.; Fiedler, Robert

doi:10.2514/6.2005-3991

Cited by 13 publications

(4 citation statements)

References 11 publications

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“…The combination of a natural encapsulation mechanism and an intelligent runtime system has made Charm++ suitable for parallel code development over a range of computing architectures, from personal computers to large-scale parallel clusters, from multicore CPUs to massively-parallel GPUs. In addition, it has been applied to scale real-world applications to thousands of processors on several scientific and engineering fields, such as quantum chemistry (Bohm et al 2008), computational cosmology (Jetley et al 2008), rocket simulation (Jiao et al 2005) and weather forecasting (Rodrigues et al , 2011.…”

Section: Charm++ and Processor Virtualizationmentioning

confidence: 99%

A New Approach to Load Balance for Parallel Compositional Simulation Based on Reservoir Model Over-decomposition

Wang

Killough

2013

All Days

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The quest for efficient and scalable parallel reservoir simulators has been evolving with the advancement of high performance computing architectures. Among the various challenges of efficiency and scalability, load imbalance is a major obstacle that has not been fully addressed and solved. The reasons that cause load imbalance in parallel reservoir simulation are both static and dynamic. Robust graph partitioning algorithms are capable of handling static load imbalance by decomposing the underlying reservoir geometry to distribute a roughly equal load to each processor. However, these loads determined by a static load balancer seldom remain unchanged as the simulation proceeds in time. This so called dynamic imbalance can be further exacerbated in parallel compositional simulations. The flash calculations for equations of state in complex compositional simulations not only can consume over half of the total execution time but also are difficult to balance merely by a static load balancer. The computational cost of flash calculations in each grid block heavily depends on the dynamic data such as pressure, temperature, and hydrocarbon composition. Thus, any static assignment of grid blocks may lead to dynamic load imbalance in unpredictable manners. A dynamic load balancer can often provide solutions for this difficulty. However, traditional techniques are inflexible and tedious to implement in legacy reservoir simulators. In this paper, we present a new approach to address dynamic load imbalance in parallel compositional simulation. It overdecomposes the reservoir model to assign each processor a bundle of subdomains. Processors treat these bundles of subdomains as virtual processes or user-level migratable threads which can be dynamically migrated across processors in the run-time system. This technique is shown to be capable of achieving better overlap between computation and communication for cache efficiency. We employ this approach in a legacy reservoir simulator and demonstrate reduction in the execution time of parallel compositional simulations while requiring minimal changes to the source code. Finally, it is shown that domain over-decomposition together with a load balancer can improve speedup from 29.27 to 62.38 on 64 physical processors for a realistic simulation problem.

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Section: Charm++ and Processor Virtualizationmentioning

confidence: 99%

A New Approach to Load Balance for Parallel Compositional Simulation Based on Reservoir Model Over-decomposition

Wang

Killough

2013

All Days

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“…The combination of features in Charm++ has made it suitable for the expression of parallelism over a range of architectures, from multi-core desktops to existing petaFLOP-scale parallel machines. Moreover, it has enabled scaling real applications to thousands of processors on several scientific areas, such as molecular dynamics [15], quantum chemistry [16], computational cosmology [17], rocket simulation [18] and others.…”

Section: B Processor Virtualization With Charm++mentioning

confidence: 99%

Optimizing an MPI weather forecasting model via processor virtualization

Rodrigues

Navaux

Panetta

et al. 2010

2010 International Conference on High Performance Computing

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Abstract-Weather forecasting models are computationally intensive applications. These models are typically executed in parallel machines and a major obstacle for their scalability is load imbalance. The causes of such imbalance are either static (e.g. topography) or dynamic (e.g. shortwave radiation, moving thunderstorms). Various techniques, often embedded in the application's source code, have been used to address both sources. However, these techniques are inflexible and hard to use in legacy codes.In this paper, we demonstrate the effectiveness of processor virtualization for dynamically balancing the load in BRAMS, a mesoscale weather forecasting model based on MPI parallelization. We use the Charm++ infrastructure, with its overdecomposition and object-migration capabilities, to move subdomains across processors during execution of the model. Processor virtualization enables better overlap between computation and communication and improved cache efficiency. Furthermore, by employing an appropriate load balancer, we achieve better processor utilization while requiring minimal changes to the model's code.

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“…In addition, it has been applied to scale real-world applications to thousands of processors in several scientific and engineering fields, such as quantum chemistry , computational cosmology (Jetley et al 2008), rocket simulation (Jiao et al 2005), and weather forecasting (Rodrigues et al 2010a, b, c).…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Load Balance for Parallel/Compositional Simulation Based on Reservoir-Model Overdecomposition

Wang

Killough

2013

SPE Journal

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The quest for efficient and scalable parallel reservoir simulators has been evolving with the advancement of high-performance computing architectures. Among the various challenges of efficiency and scalability, load imbalance is a major obstacle that has not been fully addressed and solved. The causes of load imbalance in parallel reservoir simulation are both static and dynamic. Robust graph-partitioning algorithms are capable of handling static load imbalance by decomposing the underlying reservoir geometry to distribute a roughly equal load to each processor. However, these loads that are determined by a static load balancer seldom remain unchanged as the simulation proceeds in time. This socalled dynamic imbalance can be exacerbated further in parallel compositional simulations. The flash calculations for equations of state (EOSs) in complex compositional simulations not only can consume more than half of the total execution time but also are difficult to balance merely by a static load balancer. The computational cost of flash calculations in each gridblock heavily depends on the dynamic data such as pressure, temperature, and hydrocarbon composition. Thus, any static assignment of gridblocks may lead to dynamic load imbalance in unpredictable manners. A dynamic load balancer can often provide solutions for this difficulty. However, traditional techniques are inflexible and tedious to implement in legacy reservoir simulators. In this paper, we present a new approach to address dynamic load imbalance in parallel compositional simulation. It overdecomposes the reservoir model to assign each processor a bundle of subdomains. Processors treat these bundles of subdomains as virtual processes or userlevel migratable threads that can be dynamically migrated across processors in the run-time system. This technique is shown to be capable of achieving better overlap between computation and communication for cache efficiency. We use this approach in a legacy reservoir simulator and demonstrate a reduction in the execution time of parallel compositional simulations while requiring minimal changes to the source code. Finally, it is shown that domain overdecomposition, together with a load balancer, can improve speedup from 29.27 to 62.38 on 64 physical processors for a realistic simulation problem.

show abstract

An Integration Framework for Simulations of Solid Rocket Motors

Cited by 13 publications

References 11 publications

A New Approach to Load Balance for Parallel Compositional Simulation Based on Reservoir Model Over-decomposition

A New Approach to Load Balance for Parallel Compositional Simulation Based on Reservoir Model Over-decomposition

Optimizing an MPI weather forecasting model via processor virtualization

A New Approach to Load Balance for Parallel/Compositional Simulation Based on Reservoir-Model Overdecomposition

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