Engineering topics

Gaudin, Wayne

Publications and source records attributed to Gaudin, Wayne.

4 records

DOE OSTI2020

A Baseline for Global Weather and Climate Simulations at 1 km Resolution

In an attempt to advance the understanding of the Earth's weather and climate by representing deep convection explicitly, we present a global, four-month simulation (November 2018 to February 2019) with ECMWF's hydrostatic Integrated Forecasting System (IFS) at an average grid spacing of 1.4 km. The impact of explicitly simulating deep convection on the atmospheric circulation and its variability is assessed by comparing the 1.4 km simulation to the equivalent well-tested and calibrated global simulations at 9 km grid spacing with and without parametrized deep convection. The explicit simulation of deep convection at 1.4 km results in a realistic large-scale circulation, better representation of convective storm activity, and stronger convective gravity wave activity when compared to the 9 km simulation with parametrized deep convection. Comparison of the 1.4 km simulation to the 9 km simulation without parametrized deep convection shows that switching off deep convection parametrization at a too coarse resolution (i.e., 9 km) generates too strong convective gravity waves. Based on the limited statistics available, improvements to the Madden-Julian Oscillation or tropical precipitation are not observed at 1.4 km, suggesting that other Earth system model components and/or their interaction are important for an accurate representation of these processes and may well need adjusting at deep convection resolving resolutions. Overall, the good agreement of the 1.4 km simulation with the 9 km simulation with parametrized deep convection is remarkable, despite one of the most fundamental parametrizations being turned off at 1.4 km resolution and despite no adjustments being made to the remaining parametrizations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

TeaLeaf: A Mini-Application to Enable Design-Space Explorations for Iterative Sparse Linear Solvers

Iterative sparse linear solvers are an important class of algorithm in high performance computing, and form a crucial component of many scientific codes. As intra and inter node parallelism continues to increase rapidly, the design of new, scalable solvers which can target next generation architectures becomes increasingly important. In this work we present TeaLeaf, a recent mini-app constructed to explore design space choices for highly scalable solvers. We then use TeaLeaf to compare the standard CG algorithm with a Chebyshev Polynomially Preconditioned Conjugate Gradient (CPPCG) iterative sparse linear solver. CPPCG is a communication-avoiding algorithm, requiring less global communication than previous approaches. TeaLeaf includes support for many-core processors, such as GPUs and Xeon Phi, and we include strong-scaling results across a range of world-leading Petascale supercomputers, including Titan and Piz Daint.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Many-Core Acceleration of a Discrete Ordinates Transport Mini-App at Extreme Scale, In: ISC High Performance 2016: High Performance Computing

Time-dependent deterministic discrete ordinates transport codes are an important class of application which provide significant challenges for large, many-core systems. One such challenge is the large memory capacity needed by the solve step, which requires us to have a scalable solution in order to have enough node-level memory to store all the data. In our previous work, we demonstrated the first implementation which showed a significant performance benefit for single node solves using GPUs. In this paper we extend our work to large problems and demonstrate the scalability of our solution on two Petascale GPU-based supercomputers: Titan at Oak Ridge and Piz Daint at CSCS. Our results show that our improved node-level parallelism scheme scales just as well across large systems as previous approaches when using the tried and tested KBA domain decomposition technique. We validate our results against an improved performance model which predicts the runtime of the main ‘sweep’ routine when running on different hardware, including CPUs or GPUs.

Computer Science↗

DOE OSTI2015

Resident Block-Structured Adaptive Mesh Refinement on Thousands of Graphics Processing Units, In: 2015 44th International Conference on Parallel Processing

Block-structured adaptive mesh refinement (AMR) is a technique that can be used when solving partial differential equations to reduce the number of cells necessary to achieve the required accuracy in areas of interest. These areas (shock fronts, material interfaces, etc.) are recursively covered with finer mesh patches that are grouped into a hierarchy of refinement levels. Despite the potential for large savings in computational requirements and memory usage without a corresponding reduction in accuracy, AMR adds overhead in managing the mesh hierarchy, adding complex communication and data movement requirements to a simulation. In this paper, we describe the design and implementation of a resident GPU-based AMR library, including: the classes used to manage data on a mesh patch, the routines used for transferring data between GPUs on different nodes, and the data-parallel operators developed to coarsen and refine mesh data. We validate the performance and accuracy of our implementation using three test problems and two architectures: an 8 node cluster, and 4,196 nodes of Oak Ridge National Laboratory's Titan supercomputer. Our GPU-based AMR hydrodynamics code performs up to 4.87× faster than the CPU-based implementation, and is scalable on 4,196 K20x GPUs using a combination of MPI and CUDA.

Computer Science↗