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An Assessment of the Laminar Hypersonic Double-Cone Experiments in the LENS-XX Tunnel

AIAA JOURNAL 2023年第8期61卷 3298-3312页

作者： Ray, Jaideep Blonigan, Patrick Phipps, Eric T. Maupin, Kathryn Sandia Natl Labs Extreme Scale Data Sci & Analyt MS 9152 Livermore CA 94550 USA Sandia Natl Labs Scalable Algorithms Albuquerque NM 87185 USA Sandia Natl Labs Optimizat & Uncertainty Quantificat MS 1318 Albuquerque NM 87185 USA

This is an investigation on two experimental datasets of laminar hypersonic flows, over a double-cone geometry, acquired in Calspan-University at Buffalo Research Center's Large Energy National Shock (LENS)-XX expansion tunnel. These datasets have yet to be modeled accurately. A previous paper suggested that this could partly be due to mis-specified inlet conditions. The authors of this paper solved a Bayesian inverse problem to infer the inlet conditions of the LENS-XX test section and found that in one case they lay outside the uncertainty bounds specified in the experimental dataset. However, the inference was performed using approximate surrogate models. In this paper, the experimental datasets are revisited and inversions for the tunnel test-section inlet conditions are performed with a Navier-Stokes simulator. The inversion is deterministic and can provide uncertainty bounds on the inlet conditions under a Gaussian assumption. It was found that deterministic inversion yields inlet conditions that do not agree with what was stated in the experiments. An a posteriori method is also presented to check the validity of the Gaussian assumption for the posterior distribution. This paper contributes to ongoing work on the assessment of datasets from challenging experiments conducted in extreme environments, where the experimental apparatus is pushed to the margins of its design and performance envelopes.

关键词： Inverse Problems Uncertainty Quantification Computational Fluid Dynamics Computing and Informatics Hypersonic Flows

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Parallel memory-efficient computation of symmetric higher-order joint moment tensors 22

Parallel memory-efficient computation of symmetric higher-or...

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2022 Platform for Advanced Scientific Computing Conference, PASC 2022

作者： Li, Zitong Kolla, Hemanth Phipps, Eric T. Department of Computer Science Wake Forest University Winston-SalemNC United States Scalable Modelling and Analysis Sandia National Laboratories LivermoreCA United States Scalable Algorithms Sandia National Laboratories AlbuquerqueNM United States

ISBN: (纸本)9781450394109

The decomposition of higher-order joint cumulant tensors of spatio-temporal data sets is useful in analyzing multi-variate non-Gaussian statistics with a wide variety of applications (e.g. anomaly detection, independent component analysis, dimensionality reduction). Computing the cumulant tensor often requires computing the joint moment tensor of the input data first, which is very expensive using a naïve algorithm. The current state-of-the-art algorithm takes advantage of the symmetric nature of a moment tensor by dividing it into smaller cubic tensor blocks and only computing the blocks with unique values and thus reducing computation. We propose a refactoring of this algorithm by posing its computation as matrix operations, specifically Khatri-Rao products and standard matrix multiplications. An analysis of the computational and cache complexity indicates significant performance savings due to the refactoring. Implementations of our refactored algorithm in Julia show speedups up to 10x over the reference algorithm in single processor experiments. We describe multiple levels of hierarchical parallelism inherent in the refactored algorithm, and present an implementation using an advanced programming model that shows similar speedups in experiments run on a GPU. © 2022 ACM.

关键词： Higher order statistics

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An Assessment of the Laminar Hypersonic Double-Cone Experiments in the LENS-XX Tunnel

An Assessment of the Laminar Hypersonic Double-Cone Experime...

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AIAA Science and Technology (SciTech) Forum

ISBN: (数字)9781624106996

ISBN: (纸本)9781624106996

关键词： Inverse Problems Uncertainty Quantification Computational Fluid Dynamics Computing and Informatics Hypersonic Flows

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QR Factorization of Block Low-Rank Matrices on Multi-instance GPU 23rd

QR Factorization of Block Low-Rank Matrices on Multi-insta...

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23rd International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2022

作者： Ohshima, Satoshi Ida, Akihiro Yokota, Rio Yamazaki, Ichitaro Information Technology Center Nagoya University Aichi Japan Research Institute for Value-Added-Information Generation Japan Agency for Marine-Earth Science and Technology Kanagawa Japan Global Scientific Information and Computing Center Tokyo Institute of Technology Tokyo Japan Scalable Algorithms Department Sandia National Laboratories New Mexico United States

ISBN: (纸本)9783031299261

The QR factorization, which is a fundamental operation in linear algebra, is used extensively in scientific simulations. The acceleration and memory reduction of it are important research targets. QR factorization using block low-rank matrices (BLR-QR) has previously been proposed to address this issue. In this study, we consider its implementation on a GPU. Current CPUs and GPUs have numerous computational cores and the performance consists of the total performance of them. Therefore, the degree of parallelism of the target calculation is important for obtaining high performance. By contrast, many applications, including BLR-QR, do not have sufficient parallelism. Batched computation has attracted attention for achieving high performance in such calculations. However, the use of it requires major code rewriting and is extremely laborious. Thus, we propose the use of the multi-instance GPU (MIG) feature of current GPUs. Using MIG, we succeeded in obtaining a 53.3% time reduction over the CPU and 77.6% over the GPU without MIG. From the above result, we succeeded in demonstrating rapid implementation of BLR-QR on MIG and usefulness of MIG. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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GMRES with embedded ensemble propagation for the efficient solution of parametric linear systems in uncertainty quantification of computational models

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2020年 369卷

作者： Liegeois, Kim Boman, Romain Phipps, Eric T. Wiesner, Tobias A. Arnst, Maarten ULiege Aerosp & Mech Engn Liege Belgium Sandia Natl Labs Scalable Algorithms Dept Albuquerque NM 87185 USA

In a previous work, embedded ensemble propagation was proposed to improve the efficiency of sampling-based uncertainty quantification methods of computational models on emerging computational architectures. It consists of simultaneously evaluating the model for a subset of samples together, instead of evaluating them individually. A first approach introduced to solve parametric linear systems with ensemble propagation is ensemble reduction. In Krylov methods for example, this reduction consists in coupling the samples together using an inner product that sums the sample contributions. Ensemble reduction has the advantages of being able to use optimized implementations of BLAS functions and having a stopping criterion which involves only one scalar. However, the reduction potentially decreases the rate of convergence due to the gathering of the spectra of the samples. In this paper, we investigate a second approach: ensemble propagation without ensemble reduction in the case of GMRES. This second approach solves each sample simultaneously but independently to improve the convergence compared to ensemble reduction. This raises two new issues which are solved in this paper: the fact that optimized implementations of BLAS functions cannot be used anymore and that ensemble divergence, whereby individual samples within an ensemble must follow different code execution paths, can occur. We tackle those issues by implementing a high-performing ensemble GEMV and by using masks. The proposed ensemble GEMV leads to a similar cost per GMRES iteration for both approaches, i.e. with and without reduction. For illustration, we study the performances of the new linear solver in the context of a mesh tying problem. This example demonstra (C) 2020 Elsevier B.V. All rights reserved.

关键词： GMRES Intrusive uncertainty quantification Embedded ensemble propagation BLAS Control flow divergence

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Parallel graph coloring algorithms for distributed GPU environments

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PARALLEL COMPUTING 2022年 110卷

作者： Bogle, Ian Slota, George M. Boman, Erik G. Devine, Karen D. Rajamanickam, Sivasankaran Rensselaer Polytech Inst Dept Comp Sci Troy NY 12180 USA Sandia Natl Labs Scalable Algorithms Dept POB 5800 Albuquerque NM 87185 USA

Graph coloring is often used in parallelizing scientific computations that run in distributed and multi-GPU environments;it identifies sets of independent data that can be updated in parallel. Many algorithms exist for graph coloring on a single GPU or in distributed memory, but to the best of our knowledge, hybrid MPI+GPU algorithms have been unexplored until this work. We present several MPI+GPU coloring approaches based on the distributed coloring algorithms of Gebremedhin et al. and the shared-memory algorithms of Deveci et al. The on-node parallel coloring uses implementations in KokkosKernels, which provide parallelization for both multicore CPUs and GPUs. We further extend our approaches to compute distance-2 and partial distance-2 colorings, giving the first known distributed, multi-GPU algorithm for these problems. In addition, we propose a novel heuristic to reduce communication for recoloring in distributed graph coloring. Our experiments show that our approaches operate efficiently on inputs too large to fit on a single GPU and scale up to graphs with 76.7 billion edges running on 128 GPUs.

关键词： Graph coloring Distributed algorithms GPU Combinatorial scientific computing

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scalable, Multi-Constraint, Complex-Objective Graph Partitioning

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2020年第12期31卷 2789-2801页

作者： Slota, George M. Root, Cameron Devine, Karen Madduri, Kamesh Rajamanickam, Sivasankaran Rensselaer Polytech Inst Dept Comp Sci Troy NY 12180 USA Sandia Natl Labs Scalable Algorithms Dept Albuquerque NM 87123 USA Penn State Univ Elect Engn & Comp Sci Dept University Pk PA 16810 USA

We introduce XtraPuLP, a distributed-memory graph partitioner designed to process irregular trillion-edge graphs. XtraPuLP is based on the scalable label propagation community detection technique, which has been demonstrated in various prior works as a viable means to produce high quality partitions of skewed and small-world graphs with minimal computation time. Our XtraPuLP implementation can also be generalized to compute partitions with an arbitrary number of constraints, and it can compute partitions with balanced communication load across all parts. On a collection of large sparse graphs, we show that XtraPuLP partitioning is considerably faster than state-of-the-art partitioning methods, while also demonstrating that XtraPuLP can produce partitions of real-world graphs with billion+ vertices and over a hundred billion edges in minutes. Additionally, we demonstrate XtraPuLP on a variety of applications, including large-scale graph analytics and sparse matrix-vector multiplication.

关键词： Partitioning algorithms Scalability Sparse matrices Computational modeling Rats Indexes Social networking (online) Graph partitioning load balancing label propagation graph analysis

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Parallel graph coloring algorithms for distributed GPU environments

arXiv

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arXiv 2021年

作者： Bogle, Ian Slota, George M. Boman, Erik G. Devine, Karen D. Rajamanickam, Sivasankaran Department of Computer Science Rensselaer Polytechnic Institute Lally 317 110 8th St Troy NY12180 United States Scalable Algorithms Department Sandia National Laboratories New Mexico United States

关键词： Graphics processing unit

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Composite matrix construction for structured grid adaptive mesh refinement

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COMPUTER PHYSICS COMMUNICATIONS 2019年 244卷 35-39页

作者： Adams, Mark F. Cornford, Stephen L. Martin, Daniel F. McCorquodale, Peter Lawrence Berkeley Natl Lab Scalable Solvers Grp Berkeley CA 94720 USA Swansea Univ Dept Geog Coll Sci Swansea W Glam Wales Lawrence Berkeley Natl Lab Appl Numer Algorithms Grp Berkeley CA USA

Structured-grid adaptive mesh refinement (SAMR) is an approach to mesh generation that supports structured access to data and adaptive mesh refinement for discretized partial differential equations (PDEs). Solution algorithms often require that an inverse of an operator be applied, a system of algebraic equations must be solved, and this process is often the primary computational cost in an application. SAMR is well suited to geometric multigrid solvers, which can be effective, but often do not adapt well to complex geometry including material coefficients. Algebraic multigrid (AMG) is more robust in the face of complex geometry, in both boundary conditions and internal material interfaces. AMG requires a stored matrix linearization of the operator. We discuss an approach, and an implementation in the Chombo block-structured AMR framework, for constructing composite grid matrices from a SAMR hierarchy of grids for use in linear solvers in the PETSc numerical library. We consider a case study with the Chombo-based BISICLES ice sheet modeling application. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Algebraic multigrid Preconditioning PETSc Adaptive mesh refinement

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FROSch: A Fast And Robust Overlapping Schwarz Domain Decomposition Preconditioner Based on Xpetra in Trilinos 25th

FROSch: A Fast And Robust Overlapping Schwarz Domain Decompo...

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25th International Conference on Domain Decomposition Methods in Science and Engineering, DD 2018

作者： Heinlein, Alexander Klawonn, Axel Rajamanickam, Sivasankaran Rheinbach, Oliver Department of Mathematics and Computer Science University of Cologne Weyertal 86-90 Köln50931 Germany Center for Data and Simulation Science University of Cologne Cologne Germany Center for Computing Research Scalable Algorithms Department Sandia National Laboratories NM 87123 Albuquerque Mexico INMO Technische Universität Bergakademie Freiberg Akademiestr. 6 Freiberg09599 Germany

ISBN: (纸本)9783030567491

This article describes a parallel implementation of a two-level overlapping Schwarz preconditioner with the GDSW (Generalized Dryja–Smith–Widlund) coarse space described in previous work [12, 10, 15] into the Trilinos framework;cf. [16]. The software is a significant improvement of a previous implementation [12];see Sec. 4 for results on the improved performance. © 2020, Springer Nature Switzerland AG.

关键词： Domain decomposition methods

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