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Solving Complex Problems with High Performance Computing, Network and Storage at Los Alamos National Laboratory
Abstract Not Provided
Maximize, minimize or target - optimization for a fitted response from a designed experiment
One of the common goals of running and analyzing a designed experiment is to find a location in the design space that optimizes the response of interest. Depending on the goal of the experiment, we may seek to maximize or minimize the response, or set the process to hit a particular target value. After the designed experiment, a response model is fitted and the optimal settings of the input factors are obtained based on the estimated response model. Furthermore, the suggested optimal settings of the input factors are then used in the production environment.
What’s Driving Uncertainty? The Model or the Model Parameters (What’s Driving Uncertainty? The influences of model and model parameters in data analysis)
Here, one of the substantial improvements to the practice of data analysis in recent decades is the change from reporting just a point estimate for a parameter or characteristic, to now including a summary of uncertainty for that estimate. Understanding the precision of the estimate for the quantity of interest provides better understanding of what to expect and how well we are able to predict future behavior from the process. For example, when we report a sample average as an estimate of the population mean, it is good practice to also provide a confidence interval (or credible interval, if you are doing a Bayesian analysis) to accompany that summary. This helps to calibrate what ranges of values are reasonable given the variability observed in the sample and the amount of data that were included in producing the summary.
QP Statistics Spotlight: Practical versus Statistical Priorities
Randomization during data collection in a designed experiment is an effective strategy for reducing system bias and ensuring independent observations1, but at what cost should it be fully implemented? We recently encountered a scenario that highlights an important aspect of collaboration2 between statisticians, scientists and engineers – the idea of compromise for the sake of achieving wins for all team members. In a multi-million-dollar experiment with a fixed amount of time on the test equipment, each experimental run involved resetting the input values and then waiting for the continuous process to reach a new equilibrium. For example, the temperature of the furnace could be reset, but it takes some time for the test chamber to reach the new temperature and rest at that temperature for a while until all components in the chamber settle at that new setting. Similarly, a critical gas flow component also required a settling period before data could be collected that would be representative of that input setting value. In all, the experiment involved 5 factors, where 4 of the 5 were time-consuming and costly to adjust, with the duration of time needed to reach new equilibrium being dependent on how far the previous setting was from the new one for the combination of these 4 factors. For example, changing from 150° to 200° would reach equilibrium faster than changing from 100° to 200°.
Mathematical Methods and Modeling in the National Security Sciences
This presentation will be given as a recruiting presentation at Bethany Lutheran College, Minnesota State University Mankato, Illinois Technical University, and Southern Illinois University.
Mathematical Methods and Modeling in the National Security Sciences
Luttman will be giving this presentation at several colleges and universities at the end of March 2017
Preparing an Incompressible-Flow Fluid Dynamics Code for Exascale-Class Wind Energy Simulations: Preprint
The US Department of Energy has identified Exascale-Class wind farm simulation tools as critical to wind energy scientific discovery. A primary objective of the Exawind project is to build high-performance, predictive Computational Fluid Dynamics tools that satisfy these modeling needs. GPU accelerators will serve as the computational thoroughbreds of next generation, Exascale-Class, platforms. Here, we report on our efforts for 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 not sufficient. One needs novel algorithms that are application aware, memory efficient, and optimized for latest generation GPU devices to get high-performance. The result of our efforts are unstructured mesh simulations of wind turbines that use 1/6 the compute resources of Summit supercomputer at Oak Ridge National Lab. In particular, we demonstrate a first-of-its-kind, simulation using Algebraic Multigrid solvers on over 4000 GPUs.
Transforming Energy through Computational Excellence. Exascale Computing: Combustion; Deep Learning for Presumed Probability Density Function (PDF) Models
NREL researchers use advanced machine learning techniques to define improved methods using deep learning models to resolve reacting flows in turbulent combustion flows, reducing the computational burden, increasing computational speed, and improving accuracy. These advancements reduce cost and improve fidelity of rapid-turn-around engineering calculations.
Enabling Combustion Science Simulations for Future Exascale Machines
Reacting flow simulations for combustion applications require extensive computing capabilities. Leveraging the AMReX library, the Pele suite of combustion simulation tools targets the largest supercomputers available and future exascale machines. We introduce PeleC, the compressible solver in the Pele suite, and detail its capabilities, including complex geometry representation, chemistry integration, and discretization. We present a comparison of development efforts using both OpenACC and AMReX's C++ performance portability framework for execution on multiple GPU architectures. We discuss relevant details that have allowed PeleC to achieve high performance and scalability. PeleC's performance characteristics are measured through relevant simulations on multiple supercomputers. The success of PeleC's design for exascale is exhibited through demonstration of a 160 billion cell simulation and weak scaling onto 100\% of Summit, an NVIDIA-based GPU supercomputer at Oak Ridge National Laboratory. Our results provide confidence that PeleC will enable future combustion science simulations with unprecedented fidelity.
Improving the Performance of DGEMM with MoA and Cache-Blocking: Preprint
The goal of this paper is to demonstrate performance enhancements of the high performance dense linear algebra matrix-matrix multiply DGEMM kernel, widely implemented by vendors in the basic linear algebra subroutine BLAS library. The mathematics of arrays (MoA) paradigm due to Mullin (1988) results in contiguous memory accesses in combination with Church-Rosser complete language constructs optimized for target processor architectures [3]. Our performance studies demonstrate that the MoA implementation of DGEMM combined with optimal cache-blocking strategies results in at least a 25% performance gain on both Intel Xeon Skylake and IBM Power-9 processors over the vendor supplied Intel MKL and IBM ESSL basic linear algebra libraries. Results are presented for the NREL Eagle and ORNL Summit supercomputers.
Threaded Multi-Core GEMM with MoA and Cache-Blocking: Preprint
A threaded multi-core implementation of the high performance dense linear algebra matrix-matrix multiply GEMM kernel is described. This kernel is widely implemented by vendors in the basic linear algebra subroutine BLAS library. The mathematics of arrays (MoA) paradigm due to Mullin (1988) results in contiguous memory accesses by employing outer-product forms. Our performance studies demonstrate that the MoA implementation of double precision DGEMM combined with optimal cache-blocking strategies results in at least a 25% performance gain on the Intel Xeon Skylake processor over the vendor supplied Intel MKL basic linear algebra libraries. Results are presented for the NREL Eagle supercomputer. The multi-core DGEMM achieves over 100 GigaFlops/sec with eight openMP threads.
Advanced Computing Annual Report 2021
Advanced computing at the National Renewable Energy Laboratory (NREL) had an exciting 2021. More than 300 projects advanced the U.S. Department of Energy's (DOE's) Office of Energy Efficiency and Renewable Energy (EERE) mission. This annual report highlights capabilities, project highlights, innovations, etc. to showcase the great work conducted within Advanced Computing at NREL.
Novel Solver Algorithms for Nearly Singular Linear Systems Arising in Combustion Modelling
Direct Numerical Simulations of realistic combustion devices are extremely challenging due to the wide separation of scales in the simulation, for example an internal combustion (IC) engine chamber, and the flame thickness of a high-pressure flame. The PeleLMeX solver uses adaptive mesh refinement (AMR) to evolve multi-species reacting flows in the low Mach number limit at the Exascale and relies on an embedded boundary (EB) approach to represent complex geometries. In that framework, the EB geometries often give rise to very small cut-cells along the boundary, which translate into extreme ill-conditioning of the pressure-projection, with eigenvalues that span 15-16 orders of magnitude. In this talk, we focus on the case of a typical IC piston bowl geometry for which we present on a novel approach towards solving these nearly singular linear systems with ILU-based, C-AMG smoothers on massively parallel architectures. In particular, we use scaling and equilibration algorithms to handle the non-normality of the upper triangular factors. This enables us to approximate the highly sequential triangular solve algorithm, embedded in the AMG smoothing-solve phase, with Jacobi iterations. This approximation can be written as a convergent Neumann series whose terms are composed of highly parallel sparse matrix vector multiplications. The result is an algorithm that substantially decreases setup and solve time, compared to state-of-the-art, for these challenging linear systems.
Mesoflow: An Open-Source Reacting Flow Solver for Catalysis at Mesoscale
We present the capabilities and software performance metrics of our open-source continuum solver for catalysis, Mesoflow, developed specifically for modeling transport and chemistry at the mesoscale. Our solver utilizes Cartesian block-structured adaptive mesh refinement to resolve complex catalyst surface morphologies directly obtained from X-ray tomography data. An immersed boundary based formulation enables rapid representation of complex geometries prevalent in most mesoporous catalyst interfaces. The solver is developed on top of open-source performance portable library, AMReX, providing parallel execution capabilities on current and upcoming high-performance-computing (HPC) architectures. Our flexible software framework enables integration of complex chemical mechanisms at heterogenous interfaces and time-split algorithms for circumventing highly disparate reaction and flow time-scales. Our current studies indicate a ten-fold performance gain by using graphics-processing-units (GPUs) compared to a single processor for representative problem sizes (2 million cell mesh). We will also present a brief introduction on how to build and use this software for application problems pertaining to catalytic upgrading and gas transport within porous catalyst particles.
From PeleC to PeleACC, to PeleC++
PeleC is an Exascale Computing Project application for simulating compressible combustion in complex geometries. It has been built on top of the popular AMReX library. In the beginning of the Exascale Computing Project, PeleC was focused on KNL. It uses a mixture of C++, C, and kernels written in Fortran to obtain performance by focusing on vectorization. Recently we have taken two approaches in deciding PeleC's future for obtaining performance on exascale GPU machines. In the first programming model, we decorated the Fortran kernels with OpenACC directives. This expedited our ability to run at large scales on Summit's GPUs, where we achieved a significant speedup over the CPUs on Summit. The second programming model involved rewriting the Fortran kernels in C++ and using AMReX's Kokkos-like lambda abstractions for running on the GPU. This resulted in similar speedups on Summit's GPUs over merely utilizing the CPUs. Both approaches involved AMReX's management of memory transfers between the device and host. In this work, we compare and contrast the benefits and pitfalls to both programming approaches regarding performance, performance portability, and productivity. We also discuss advantages we have found in taking the time to modernize our code and why have chosen a specific pathway to prepare our code for the future DOE exascale machines.