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Embedded ensemble propagation for improving performance, portability, and scalability of uncertainty quantification on emerging computational architectures

SIAM Journal on Scientific Computing

Phipps, Eric T.; D'Elia, Marta D.; Edwards, Harold C.; Hoemmen, Mark F.; Hu, Jonathan J.; Rajamanickam, Sivasankaran R.

In this study, quantifying simulation uncertainties is a critical component of rigorous predictive simulation. A key component of this is forward propagation of uncertainties in simulation input data to output quantities of interest. Typical approaches involve repeated sampling of the simulation over the uncertain input data, and can require numerous samples when accurately propagating uncertainties from large numbers of sources. Often simulation processes from sample to sample are similar and much of the data generated from each sample evaluation could be reused. We explore a new method for implementing sampling methods that simultaneously propagates groups of samples together in an embedded fashion, which we call embedded ensemble propagation. We show how this approach takes advantage of properties of modern computer architectures to improve performance by enabling reuse between samples, reducing memory bandwidth requirements, improving memory access patterns, improving opportunities for fine-grained parallelization, and reducing communication costs. We describe a software technique for implementing embedded ensemble propagation based on the use of C++ templates and describe its integration with various scientific computing libraries within Trilinos. We demonstrate improved performance, portability and scalability for the approach applied to the simulation of partial differential equations on a variety of CPU, GPU, and accelerator architectures, including up to 131,072 cores on a Cray XK7 (Titan).

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TYPE Journal Article YEAR 2017
DOIOSTI

The Portals 4.1 Network Programming Interface

Barrett, Brian; Brightwell, Ronald B.; Grant, Ryan E.; Hemmert, Karl S.; Laros, James H.; Wheeler, Kyle; Underwood, Keith; Riesen, Rolf; Maccabe, Arthur B.; Hudson, Trammel

This report presents a specification for the Portals 4 networ k programming interface. Portals 4 is intended to allow scalable, high-performance network communication betwee n nodes of a parallel computing system. Portals 4 is well suited to massively parallel processing and embedded syste ms. Portals 4 represents an adaption of the data movement layer developed for massively parallel processing platfor ms, such as the 4500-node Intel TeraFLOPS machine. Sandia's Cplant cluster project motivated the development of Version 3.0, which was later extended to Version 3.3 as part of the Cray Red Storm machine and XT line. Version 4 is tar geted to the next generation of machines employing advanced network interface architectures that support enh anced offload capabilities.

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TYPE SAND Report YEAR 2017
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Results 4051–4100 of 9,998
Results 4051–4100 of 9,998