The Multiphysics on Advanced Platforms Project

Anderson, Robert; Black, A.; Blakeley, B.; Bleile, Ryan; Camier, J.-S.; Ciurej, J.; Cook, Andrew W.; Dobrev, Veselin; Elliott, N. S.; Grondalski, J.; Harrison, Cyrus; Hornung, Richard D.; Kolev, Tzanio V.; Legendre, Matthew; Liu, W.; W, I Nissen,; Olson, Britton J.; Osawe, Maxwell; Papadimitriou, Georgios I.; Pearce, Olga; Pember, Richard B.; Skinner, A.D.; Stevens, Dave; Stitt, Thomas; Taylor, L.; Tomov, Vladimir; Rieben, Robert N.; Vargas, Anielle de; Weiss, Kenneth; White, Dan; Busby, Lee

doi:10.2172/1724326

Cited by 15 publications

(6 citation statements)

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“…MARBL is a new multi-physics simulation code at LLNL [13]. Some of its key capabilities include multi-material radiation hydrodynamics in both an Eulerian and an Arbitrary Lagrangian-Eulerian (ALE) framework for applications in inertial confinement fusion (ICF), pulsed power and equation of state/material strength experiments.…”

Section: Marblmentioning

confidence: 99%

Understanding Power and Energy Utilization in Large Scale Production Physics Simulation Codes

Ryujin¹,

Vargas²,

Karlin³

et al. 2022

Preprint

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Power is an often-cited reason for moving to advanced architectures on the path to Exascale computing. This is due to the practical concern of delivering enough power to successfully site and operate these machines, as well as concerns over energy usage while running large simulations. Since accurate power measurements can be difficult to obtain, processor thermal design power (TDP) is a possible surrogate due to its simplicity and availability. However, TDP is not indicative of typical power usage while running simulations. Using commodity and advance technology systems at Lawrence Livermore National Laboratory (LLNL) and Sandia National Laboratory, we performed a series of experiments to measure power and energy usage in running simulation codes. These experiments indicate that large scale LLNL simulation codes are significantly more efficient than a simple processor TDP model might suggest.

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Section: Marblmentioning

confidence: 99%

Understanding Power and Energy Utilization in Large Scale Production Physics Simulation Codes

Ryujin¹,

Vargas²,

Karlin³

et al. 2022

Preprint

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“…In this section we demonstrate the proposed GPU-algorithms in the settings of a multimaterial ALE hydrodynamics production code. We consider a simulation of the BRL81a shaped charge using the MARBL multiphysics application [40]. A shaped charge is a device which focuses the pressures of a high explosive onto a metal "liner" to form a hyper-velocity jet which can be used in many applications, including armor penetration, metal cutting and perforation of wells for the oil/gas industry [41].…”

Section: Ale Simulation With Material-adaptive Tmopmentioning

confidence: 99%

Accelerating High-Order Mesh Optimization Using Finite Element Partial Assembly on GPUs

Camier¹,

Dobrev²,

Knupp³

et al. 2022

Preprint

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In this paper we present a new GPU-oriented mesh optimization method based on highorder finite elements. Our approach relies on node movement with fixed topology, through the Target-Matrix Optimization Paradigm (TMOP) and uses a global nonlinear solve over the whole computational mesh, i.e., all mesh nodes are moved together. A key property of the method is that the mesh optimization process is recast in terms of finite element operations, which allows us to utilize recent advances in the field of GPU-accelerated high-order finite element algorithms. For example, we reduce data motion by using tensor factorization and matrix-free methods, which have superior performance characteristics compared to traditional full finite element matrix assembly and offer advantages for GPU-based HPC hardware. We describe the major mathematical components of the method along with their efficient GPU-oriented implementation. In addition, we propose an easily reproducible mesh optimization test that can serve as a performance benchmark for the mesh optimization community.

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“…Building from the MFEM finite element library, MARBL uses arbitrary high-order finite elements to solve Equations ( 1)- (5). A continuous Galerkin discretization is used for the kinematic variables (mesh velocity and position), while a discontinuous Galerkin discretization is used for the thermodynamic variables (indicator functions, density, and energy).…”

Section: High-order Lagrange Phasementioning

confidence: 99%

“…Here u corresponds to the mesh displacement, and the field variables are the same as those in the Lagrange phase Equation( 1)- (5).…”

Section: Remap Phasementioning

confidence: 99%

“…Our focus in this work is on MARBL, a next-generation multi-physics simulation code developed at Lawrence Livermore National Laboratory for simulating high energy density physics (HEDP) and focused experiments driven by highexplosive, magnetic or laser based energy sources for applications including pulsed power and inertial confinement fusion (ICF) [5]. MARBL is built on modular physics and computer science components and makes extensive use of high-order finite element numerical methods.…”

Section: Introductionmentioning

confidence: 99%

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Matrix-free approaches for GPU acceleration of a high-order finite element hydrodynamics application using MFEM, Umpire, and RAJA

Vargas¹,

Stitt²,

Weiss³

et al. 2021

Preprint

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With the introduction of advanced heterogeneous computing architectures based on GPU accelerators, large-scale production codes have had to rethink their numerical algorithms and incorporate new programming models and memory management strategies in order to run efficiently on the latest supercomputers. In this work we discuss our co-design strategy to address these challenges and achieve performance and portability with MARBL, a next-generation multi-physics code in development at Lawrence Livermore National Laboratory. We present a two-fold approach, wherein new hardware is used to motivate both new algorithms and new abstraction layers, resulting in a single source application code suitable for a variety of platforms. Focusing on MARBL's ALE hydrodynamics package, we demonstrate scalability on different platforms and highlight that many of our innovations have been contributed back to open-source software libraries, such as MFEM (finite element algorithms) and RAJA (kernel abstractions).

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The Multiphysics on Advanced Platforms Project

Cited by 15 publications

References 0 publications

Understanding Power and Energy Utilization in Large Scale Production Physics Simulation Codes

Understanding Power and Energy Utilization in Large Scale Production Physics Simulation Codes

Accelerating High-Order Mesh Optimization Using Finite Element Partial Assembly on GPUs

Matrix-free approaches for GPU acceleration of a high-order finite element hydrodynamics application using MFEM, Umpire, and RAJA

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