Publications

Bidadi, Shreyas B.; Brazell, Michael B.; Brunhart-Lupo, Nicholas B.; Henry de Frahan, Marc T.; Lee, Dong H.; Hu, Jonathan J.; Melvin, Jeremy M.; Mullowney, Paul M.; Vijayakumar, Ganesh V.; Moser, Robert D.; Rood, Jon R.; Sakievich, Philip S.; Sharma, Ashesh S.; Williams, Alan B.; Sprague, Michael A.

The goal of the ExaWind project is to enable predictive simulations of wind farms comprised of many megawatt-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines, capturing the thin boundary layers, and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources.

International Conference for High Performance Computing, Networking, Storage and Analysis, SC

Mullowney, Paul; Li, Ruipeng; Thomas, Stephen; Ananthan, Shreyas; Sharma, Ashesh; Rood, Jon S.; Williams, Alan B.; Sprague, Michael A.

The U.S. Department of Energy has identified exascale-class wind farm simulation as critical to wind energy scientific discovery. A primary objective of the ExaWind project is to build high-performance, predictive computational fluid dynamics (CFD) tools that satisfy these modeling needs. GPU accelerators will serve as the computational thoroughbreds of next-generation, exascale-class supercomputers. Here, we report on our efforts in preparing the ExaWind unstructured mesh solver, Nalu-Wind, for exascale-class machines. For computing at this scale, a simple port of the incompressible-flow algorithms to GPUs is insufficient. To achieve high performance, one needs novel algorithms that are application aware, memory efficient, and optimized for the latest-generation GPU devices the result of our efforts are unstructured-mesh simulations of wind turbines that can effectively leverage thousands of GPUs. In particular, we demonstrate a first-of-its-kind, incompressible-flow simulation using Algebraic Multigrid solvers that strong scales to more than 4000 GPUs on the Summit supercomputer.

Crozier, Paul C.; Adcock, Christiane A.; Ananthan, Shreyas A.; Berger-Vergiat, Luc B.; Brazell, Michael B.; Brunhart-Lupo, Nicholas B.; Henry de Frahan, Marc T.; Hu, Jonathan J.; Knaus, Robert C.; Melvin, Jeremy M.; Moser, Bob M.; Mullowney, Paul M.; Rood, Jon R.; Sharma, Ashesh S.; Thomas, Stephen T.; Vijayakumar, Ganesh V.; Williams, Alan B.; Wilson, Robert V.; Yamazaki, Ichitaro Y.; Sprague, Michael S.

Abstract not provided.

Mullowney, Paul M.; Li, Ruipeng L.; Thomas, Stephen T.; Ananthan, Shreyas A.; Sharma, Ashesh S.; Rood, Jon R.; Williams, Alan B.; Sprague, Michael S.

Abstract not provided.

Adcock, Christiane A.; Ananthan, Shreyas A.; Berger-Vergiat, Luc B.; Brazell, Michael B.; Brunhart-Lupo, Nicholas B.; Hu, Jonathan J.; Knaus, Robert C.; Melvin, Jeremy M.; Moser, Bob M.; Mullowney, Paul M.; Rood, Jon R.; Sharma, Ashesh S.; Thomas, Stephen T.; Vijayakumar, Ganesh V.; Williams, Alan B.; Wilson, Robert V.; Yamazaki, Ichitaro Y.; Sprague, Michael S.

Isocontours of Q-criterion with velocity visualized in the wake for two NREL 5-MW turbines operating under uniform-inflow wind speed of 8 m/s. Simulation performed with the hybrid-Nalu-Wind/AMR-Wind solver.

Adcock, Christiane A.; Ananthan, Shreyas A.; Berget-Vergiat, Luc B.; Brazell, Michael B.; Brunhart-Lupo, Nicholas B.; Hu, Jonathan J.; Knaus, Robert C.; Melvin, Jeremy M.; Moser, Bob M.; Mullowney, Paul M.; Rood, Jon R.; Sharma, Ashesh S.; Thomas, Stephen T.; Vijayakumar, Ganesh V.; Williams, Alan B.; Wilson, Robert V.; Yamazaki, Ichitaro Y.; Sprague, Michael S.

The goal of the ExaWind project is to enable predictive simulations of wind farms comprised of many megawatt-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines, capturing the thin boundary layers, and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources.

Sprague, Michael S.; Ananthan, Shreyas A.; Binyahib, Roba B.; Brazell, Michael B.; de Frahan, Marc H.; King, Ryan N.; Mullowney, Paul M.; Rood, Jon R.; Sharma, Ashesh S.; Thomas, Stephen T.; Vijayakumar, Ganesh V.; Crozier, Paul C.; Berger-Vergiat, Luc B.; Cheung, Lawrence C.; Dement, David C.; deVelder, Nathaniel d.; Glaze, D.J.; Hu, Jonathan J.; Knaus, Robert C.; Lee, Dong H.; Matula, Neil M.; Okusanya, Tolulope O.; Overfelt, James R.; Rajamanickam, Sivasankaran R.; Sakievich, Philip S.; Smith, Timothy A.; Vo, Johnathan V.; Williams, Alan B.; Yamazaki, Ichitaro Y.; Turner, William J.; Prokopenko, Andrey P.; Wilson, Robert V.; Moser, &.; Melvin, Jeremy M.; Sitaraman, &.

Abstract not provided.

Sprague, M.S.; Ananthan, S.A.; Brazell, M.x.; Glaws, A.G.; De Frahan, M.D.; King, R.K.; Natarajan, M.N.; Rood, J.R.; Sharma, A.L.; Sirydowicz, K.S.; S., Thomas S.; Vijaykumar, G.V.; Yellapantula, S.Y.; Crozier, Paul C.; Berger-Vergiat, Luc B.; Cheung, Lawrence C.; Glaze, D.J.; Hu, Jonathan J.; Knaus, Robert C.; Lee, Dong H.; Okusanya, Tolulope O.; Overfelt, James R.; Rajamanickam, Sivasankaran R.; Sakievich, Philip S.; Smith, Timothy A.; Vo, Johnathan V.; Williams, Alan B.; Yamazaki, Ichitaro Y.; Turner, J.H.; Prokopenko, A.P.; Wilson, R.W.; Moser, R.M.; Melvin, J.M.; Sitaraman, J S.

Abstract not provided.

Domino, Stefan P.; Williams, Alan B.

The Nalu Exascale Wind application assembles linear systems using data structures provided by the Tpetra package in Trilinos. This note describes the initialization and assembly process. The purpose of this note is to help Nalu developers and maintainers to understand the code surrounding linear system assembly, in order to facilitate debugging, optimizations, and maintenance. 1 1 Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. De- partment of Energy's National Nuclear Security Administration under contract DE-NA0003525. This report followed the Sandia National Laboratories formal review and approval process (SAND2019-0120), and is suitable for unlimited release. This page intentionally left blank.

Sprague, Michael S.; Ananthan, Shreyas A.; Gruchalla, Kenny G.; lawson, michael j.; Rood, Jon R.; Swirydowicz, K.S.; Thomas, Steve T.; Vijayakumar, Ganesh V.; Crozier, Paul C.; Dohrmann, Clark R.; Hu, Jonathan J.; Williams, Alan B.; Turner, John A.; Prokopenko, A.P.; Moser, Robert M.; Melvin, J.M.

Abstract not provided.

Prokopenko, Andrey P.; Thomas, Stephen T.; Swirydowicz, Kasia S.; Ananthan, Shreyas A.; Hu, Jonathan J.; Williams, Alan B.; Sprague, Michael S.

The goal of the ExaWind project is to enable predictive simulations of wind farms composed of many MW-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines, and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources. The primary code in the ExaWind project is Nalu, which is an unstructured-grid solver for the acousticallyincompressible Navier-Stokes equations, and mass continuity is maintained through pressure projection. The model consists of the mass-continuity Poisson-type equation for pressure and a momentum equation for the velocity. For such modeling approaches, simulation times are dominated by linear-system setup and solution for the continuity and momentum systems. For the ExaWind challenge problem, the moving meshes greatly affect overall solver costs as re-initialization of matrices and re-computation of preconditioners is required at every time step In this Milestone, we examine the effect of threading on the solver stack performance against flat-MPI results obtained from previous milestones using Haswell performance data full-turbine simulations. Whereas the momentum equations are solved only with the Trilinos solvers, we investigate two algebraic-multigrid preconditioners for the continuity equations: Trilinos/Muelu and HYPRE/BoomerAMG. These two packages embody smoothed-aggregation and classical Ruge-Stiiben AMG methods, respectively. In our FY18 Q2 report, we described our efforts to improve setup and solve of the continuity equations under flat-MPI parallelism. While significant improvement was demonstrated in the solve phase, setup times remained larger than expected. Starting with the optimized settings described in the Q2 report, we explore here simulation performance where OpenMP threading is employed in the solver stack. For Trilinos, threading is acheived through the Kokkos abstraction where, whereas HYPRE/BoomerAMG employs straight OpenMP. We examined results for our mid-resolution baseline turbine simulation configuration (229M DOF). Simulations on 2048 Haswell cores explored the effect of decreasing the number of MPI ranks while increasing the number of threads. Both HYPRE and Trilinos exhibited similar overal solution times, and both showed dramatic increases in simulation time in the shift from MPI ranks to OpenMP threads. This increase is attributed to the large amount of work per MPI rank starting at the single-thread configuration. Decreasing MPI ranks, while increasing threads, may be increasing simulation time due to thread synchronization and start-up overhead contributing to the latency and serial time in the model. These result showed that an MPI+OpenMP parallel decomposition will be more effective as the amount per MPI rank computation per MPI rank decreases and the communication latency increases. This idea was demonstrated in a strong scaling study of our low-resolution baseline model (29M DOF) with the Trilinos-HYPRE configuration. While MPI-only results showed scaling improvement out to about 1536 cores, engaging threading carried scaling improvements out to 4128 cores — roughly 7000 DOF per core. This is an important result as improved strong scaling is needed for simulations to be executed over sufficiently long simulated durations (i.e., for many timesteps). In addition to threading work described above, the team examined solver-performance improvements by exploring communication-overhead in the HYPRE-GMRES implementation through a communicationoptimal- GMRE algorithm (CO-GMRES), and offloading compute-intensive solver actions to GPUs. To those ends, a HYPRE mini-app was allow us to easily test different solver approaches and HYPRE parameter settings without running the entire Nalu code. With GPU acceleration on the Summitdev supercomputer, a 20x speedup was achieved for the overall preconditioner and solver execution time for the mini-app. A study on Haswell processors showed that CO-GMRES provides benefits as one increases MPI ranks.

Prokopenko, Andrey P.; Thomas, Stephen T.; Swirydowicz, Kasia S.; Ananthan, Shreyas A.; Hu, Jonathan J.; Williams, Alan B.; Sprague, Michael S.

Abstract not provided.

Hu, Jonathan J.; Thomas, Stephen T.; Dohrmann, Clark R.; Ananthan, Shreyas A.; Domino, Stefan P.; Williams, Alan B.; Sprague, Michael S.

The goal of the ExaWind project is to enable predictive simulations of wind farms composed of many MW-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines, and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources. The primary code in the ExaWind project is Nalu, which is an unstructured-grid solver for the acoustically-incompressible Navier-Stokes equations, and mass continuity is maintained through pressure projection. The model consists of the mass-continuity Poisson-type equation for pressure and a momentum equation for the velocity. For such modeling approaches, simulation times are dominated by linear-system setup and solution for the continuity and momentum systems. For the ExaWind challenge problem, the moving meshes greatly affect overall solver costs as re-initialization of matrices and re-computation of preconditioners is required at every time step We describe in this report our efforts to decrease the setup and solution time for the mass-continuity Poisson system with respect to the benchmark timing results reported in FY18 Q1. In particular, we investigate improving and evaluating two types of algebraic multigrid (AMG) preconditioners: Classical Ruge-Stfiben AMG (C-AMG) and smoothed-aggregation AMG (SA-AMG), which are implemented in the Hypre and Trilinos/MueLu software stacks, respectively. Preconditioner performance was optimized through existing capabilities and settings.

Hu, Jonathan J.; Thomas, Stephen T.; Dohrmann, Clark R.; Ananthan, Shreyas A.; Domino, Stefan P.; Williams, Alan B.; Sprague, Michael S.

The goal of the ExaWind project is to enable predictive simulations of wind farms composed of many MW-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines, and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources.

Williams, Alan B.

Abstract not provided.

Domino, Stefan P.; Barone, Matthew F.; Williams, Alan B.; Knaus, Robert C.

Wind applications require the ability to simulate rotating blades. To support this use-case, a novel design-order sliding mesh algorithm has been developed and deployed. The hybrid method combines the control volume finite element methodology (CVFEM) with concepts found within a discontinuous Galerkin (DG) finite element method (FEM) to manage a sliding mesh. The method has been demonstrated to be design-order for the tested polynomial basis (P=1 and P=2) and has been deployed to provide production simulation capability for a Vestas V27 (225 kW) wind turbine. Other stationary and canonical rotating ow simulations are also presented. As the majority of wind-energy applications are driving extensive usage of hybrid meshes, a foundational study that outlines near-wall numerical behavior for a variety of element topologies is presented. Results indicate that the proposed nonlinear stabilization operator (NSO) is an effective stabilization methodology to control Gibbs phenomena at large cell Peclet numbers. The study also provides practical mesh resolution guidelines for future analysis efforts. Application-driven performance and algorithmic improvements have been carried out to increase robustness of the scheme on hybrid production wind energy meshes. Specifically, the Kokkos-based Nalu Kernel construct outlined in the FY17/Q4 ExaWind milestone has been transitioned to the hybrid mesh regime. This code base is exercised within a full V27 production run. Simulation timings for parallel search and custom ghosting are presented. As the low-Mach application space requires implicit matrix solves, the cost of matrix reinitialization has been evaluated on a variety of production meshes. Results indicate that at low element counts, i.e., fewer than 100 million elements, matrix graph initialization and preconditioner setup times are small. However, as mesh sizes increase, e.g., 500 million elements, simulation time associated with \setup-up" costs can increase to nearly 50% of overall simulation time when using the full Tpetra solver stack and nearly 35% when using a mixed Tpetra- Hypre-based solver stack. The report also highlights the project achievement of surpassing the 1 billion element mesh scale for a production V27 hybrid mesh. A detailed timing breakdown is presented that again suggests work to be done in the setup events associated with the linear system. In order to mitigate these initialization costs, several application paths have been explored, all of which are designed to reduce the frequency of matrix reinitialization. Methods such as removing Jacobian entries on the dynamic matrix columns (in concert with increased inner equation iterations), and lagging of Jacobian entries have reduced setup times at the cost of numerical stability. Artificially increasing, or bloating, the matrix stencil to ensure that full Jacobians are included is developed with results suggesting that this methodology is useful in decreasing reinitialization events without loss of matrix contributions. With the above foundational advances in computational capability, the project is well positioned to begin scientific inquiry on a variety of wind-farm physics such as turbine/turbine wake interactions.

Domino, Stefan P.; Williams, Alan B.; Knaus, Robert C.

The former Nalu interior heterogeneous algorithm design, which was originally designed to manage matrix assembly operations over all elemental topology types, has been modified to operate over homogeneous collections of mesh entities. This newly templated kernel design allows for removal of workset variable resize operations that were formerly required at each loop over a Sierra ToolKit (STK) bucket (nominally, 512 entities in size). Extensive usage of the Standard Template Library (STL) std::vector has been removed in favor of intrinsic Kokkos memory views. In this milestone effort, the transition to Kokkos as the underlying infrastructure to support performance and portability on many-core architectures has been deployed for key matrix algorithmic kernels. A unit-test driven design effort has developed a homogeneous entity algorithm that employs a team-based thread parallelism construct. The STK Single Instruction Multiple Data (SIMD) infrastructure is used to interleave data for improved vectorization. The collective algorithm design, which allows for concurrent threading and SIMD management, has been deployed for the core low-Mach element- based algorithm. Several tests to ascertain SIMD performance on Intel KNL and Haswell architectures have been carried out. The performance test matrix includes evaluation of both low- and higher-order methods. The higher-order low-Mach methodology builds on polynomial promotion of the core low-order control volume nite element method (CVFEM). Performance testing of the Kokkos-view/SIMD design indicates low-order matrix assembly kernel speed-up ranging between two and four times depending on mesh loading and node count. Better speedups are observed for higher-order meshes (currently only P=2 has been tested) especially on KNL. The increased workload per element on higher-order meshes bene ts from the wide SIMD width on KNL machines. Combining multiple threads with SIMD on KNL achieves a 4.6x speedup over the baseline, with assembly timings faster than that observed on Haswell architecture. The computational workload of higher-order meshes, therefore, seems ideally suited for the many-core architecture and justi es further exploration of higher-order on NGP platforms. A Trilinos/Tpetra-based multi-threaded GMRES preconditioned by symmetric Gauss Seidel (SGS) represents the core solver infrastructure for the low-Mach advection/diffusion implicit solves. The threaded solver stack has been tested on small problems on NREL's Peregrine system using the newly developed and deployed Kokkos-view/SIMD kernels. fforts are underway to deploy the Tpetra-based solver stack on NERSC Cori system to benchmark its performance at scale on KNL machines.

Concurrency and Computation. Practice and Experience

Lin, Paul L.; Heroux, Michael A.; Williams, Alan B.; Barrett, Richard F.

The performance of a large-scale, production-quality science and engineering application (‘app’) is often dominated by a small subset of the code. Even within that subset, computational and data access patterns are often repeated, so that an even smaller portion can represent the performance-impacting features. If application developers, parallel computing experts, and computer architects can together identify this representative subset and then develop a small mini-application (‘miniapp’) that can capture these primary performance characteristics, then this miniapp can be used to both improve the performance of the app as well as provide a tool for co-design for the high-performance computing community. However, a critical question is whether a miniapp can effectively capture key performance behavior of an app. This study provides a comparison of an implicit finite element semiconductor device modeling app on unstructured meshes with an implicit finite element miniapp on unstructured meshes. The goal is to assess whether the miniapp is predictive of the performance of the app. Finally, single compute node performance will be compared, as well as scaling up to 16,000 cores. Results indicate that the miniapp can be reasonably predictive of the performance characteristics of the app for a single iteration of the solver on a single compute node.

Williams, Alan B.

Abstract not provided.

Noble, David R.; Sjaardema, Gregory D.; Clausen, Jonathan C.; Thomas, Jesse D.; Foucar, James G.; Franko, Kenneth J.; Heinstein, Martin W.; Williams, Alan B.

Abstract not provided.

Williams, Alan B.

Abstract not provided.

Barrett, Richard F.; Crozier, Paul C.; Doerfler, Douglas W.; Hammond, Simon D.; Heroux, Michael A.; Lin, Paul L.; Trucano, Timothy G.; Vaughan, Courtenay T.; Williams, Alan B.

Abstract not provided.

Williams, Alan B.

Abstract not provided.

Lin, Paul L.; Heroux, Michael A.; Williams, Alan B.; Barrett, Richard F.

Abstract not provided.

Williams, Alan B.

Abstract not provided.