Engineering topics
Bell, John
Publications and source records attributed to Bell, John.
Exascale applications: skin in the game
As noted in Wikipedia, skin in the game refers to having ‘incurred risk by being involved in achieving a goal’, where ‘ skin is a synecdoche for the person involved, and game is the metaphor for actions on the field of play under discussion’. For exascale applications under development in the US Department of Energy Exascale Computing Project, nothing could be more apt, with the skin being exascale applications and the game being delivering comprehensive science-based computational applications that effectively exploit exascale high-performance computing technologies to provide breakthrough modelling and simulation and data science solutions. These solutions will yield high-confidence insights and answers to the most critical problems and challenges for the USA in scientific discovery, national security, energy assurance, economic competitiveness and advanced healthcare. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.
Semi-empirical estimation of the attenuation loss for amorphous ZBLAN glass
ZBLAN glass tends to undergo heavy devitrification, forming crystallites that act as scattering centers and results in a large attenuation loss. In this study, attenuation loss values were obtained for ZBLAN glass test samples that were melted and then cooled rapidly at a rate of 4000 °C/min, which yielded amorphous samples that were free of micro/nano-crystalline inclusions. Using an approach by Forouhi and Bloomer, which incorporates Kramers–Kronig relations, in conjunction with measured optical data using ellipsometry, an attenuation loss of 0.092 dB/Km at 1800 nm wavelength was estimated for amorphous ZBLAN test samples.
AMReX: a framework for block-structured adaptive mesh refinement
AMReX is a C++ software framework that supports the development of block-structured adaptive mesh refinement (AMR) algorithms for solving systems of partial differential equations (PDEs) with complex boundary conditions on current and emerging architectures. AMR reduces the computational cost and memory footprint compared to a uniform mesh while preserving the local descriptions of different physical processes in complex multiphysics algorithms. Current AMReX-based application codes span a number of areas, including atmospheric modeling, astrophysics, combustion, cosmology, fluctuating hydrodynamics, multiphase flows, and particle accelerators. In particular, the AMReX-Astro GitHub repository holds a number of astrophysical modeling tools based on AMReX. The origins of AMReX trace back to the BoxLib software framework. AMReX supports a number of different time-stepping strategies and spatial discretizations. Solution strategies supported by AMReX range from level-by-level approaches (with or without subcycling in time) with multilevel synchronization to full-hierarchy approaches, and any combination thereof. User-defined kernels that operate on patches of data can be written in C++ or Fortran; there is also a Fortran-interface functionality which wraps the core C++ data structures and operations in Fortran wrappers so that an application code based on AMReX can be written entirely in Fortran.
A Bayesian approach to calibrating hydrogen flame kinetics using many experiments and parameters
First-principles Markov Chain Monte Carlo sampling is used to investigate uncertainty quantification and uncertainty propagation in parameters describing hydrogen kinetics. Specifically, we sample the posterior distribution for thirty-one parameters focusing on the H 2 O 2 and HO 2 reactions resulting from conditioning on ninety-one experiments. Established literature values are used for the remaining parameters in the mechanism as well as other thermodynamic and transport data needed to specify fluid properties. The samples are computed using an affine invariant sampler starting with broad, noninformative priors. Autocorrelation analysis shows that O(1M) samples are sufficient to obtain a reasonable sampling of the posterior. The resulting distribution identifies strong positive and negative correlations and several non-Gaussian characteristics. Using samples drawn from the posterior, we investigate the impact of parameter uncertainty on the prediction of two more complex flames: a 2D premixed flame kernel and the ignition of a hydrogen jet issuing into a heated chamber. The former represents a combustion regime similar to the target experiments used to calibrate the mechanism and the latter represents a different combustion regime. For the premixed flame, the net amount of product after a given time interval has a standard deviation of less than 2% whereas the standard deviation of the ignition time for the jet is more than 10%. The samples used for these studies are posted online. These results indicate the degree to which parameters consistent with the target experiments constrain predicted behavior in different combustion regimes. Finally, this process provides a framework for both identifying reactions for further study from candidate mechanisms as well as combining uncertainty quantification and propagation to, ultimately, tie uncertainty in laboratory flame experiments to uncertainty in end-use numerical predictions of more complicated scenarios.
DNS of Multi-Injection Mixing and Combustion at Compression Ignition Engine Conditions.
Abstract not provided.
Achieving algorithmic resilience for temporal integration through spectral deferred corrections
Spectral deferred corrections (SDC) is an iterative approach for constructing higher-order-accurate numerical approximations of ordinary differential equations. SDC starts with an initial approximation of the solution defined at a set of Gaussian or spectral collocation nodes over a time interval and uses an iterative application of lower-order time discretizations applied to a correction equation to improve the solution at these nodes. Each deferred correction sweep increases the formal order of accuracy of the method up to the limit inherent in the accuracy defined by the collocation points. In this paper, we demonstrate that SDC is well suited to recovering from soft (transient) hardware faults in the data. A strategy where extra correction iterations are used to recover from soft errors and provide algorithmic resilience is proposed. Specifically, in this approach the iteration is continued until the residual (a measure of the error in the approximation) is small relative to the residual of the first correction iteration and changes slowly between successive iterations. Here, we demonstrate the effectiveness of this strategy for both canonical test problems and a comprehensive situation involving a mature scientific application code that solves the reacting Navier-Stokes equations for combustion research.
AMReX
AMReX is a software framework for the development and implementation of massively parallel algorithms using block-structured adaptive mesh refinement (AMR). Block-structured AMR provides the basis for the temporal and spatial discretization strategy for a large variety of applications of interest to the DOE. AMReX provides a unified infrastructure with the functionality needed for applications to be able to effectively and efficiently utilize machines from laptops to exascale accelerator-based architectures. AMR reduces the computational cost and memory footprint compared to a uniform mesh while preserving accurate descriptions of different physical processes in complex multi-physics algorithms. AMReX supports algorithms that solve systems of partial differential equations (PDEs) in simple or complex geometries, and those that use particles and/or particle-mesh operations to represent component physical processes.
Topology and burning rates of turbulent, lean, H 2 /air flames
Improved understanding of turbulent flames characterized by negative consumption speed-based Markstein lengths is necessary to develop better models for turbulent lean combustion of high hydrogen content fuels. In this paper we investigate the topology and burning rates of turbulent, lean (φ = 0.31), H 2 /air flames obtained from a recently published DNS database (Aspden et al., 2011). We calculate local flame front curvatures, strain rates, thicknesses, and burning velocities and compare these values to reference quantities obtained from stretched laminar flames computed numerically in three model geometrical configurations-a counterflow twin flame, a tubular counterflow flame and an expanding cylindrical flame. We compare and contrast the DNS with these model laminar flame calculations, and show both where they closely correlate with each other, as well as where they do not. These results in the latter case are shown to result from non-flamelet behaviors, unsteady effects, and curvature-strain correlations. These insights are derived from comparisons conditioned on different topological features, such as portions of the flame front with a spherical/cylindrical shape, the leading edge of the flame, and portions of the flame front with low mean curvature. We also show that reference time scales vary appreciably over the flame, and characterizing the relative values of fluid mechanic and kinetic time scales by a single value leads to erroneous conclusions. For example, there is a two order of magnitude decrease in chemical time scales at the leading edge of the front relative to its unstretched value. In conclusion, for this reason, the leading edge of the front quite closely tracks quasi-steady calculations, even in the lowest Damköhler number case, Da F ~0.005
Wearable Health Monitoring Systems
The shrinking size and weight of electronic circuitry has given rise to a new generation of smart clothing that enables biological data to be measured and transmitted. As the variation in the number and type of deployable devices and sensors increases, technology must allow their seamless integration so they can be electrically powered, operated, and recharged over a digital pathway. Nyx Illuminated Clothing Company has developed a lightweight health monitoring system that integrates medical sensors, electrodes, electrical connections, circuits, and a power supply into a single wearable assembly. The system is comfortable, bendable in three dimensions, durable, waterproof, and washable. The innovation will allow astronaut health monitoring in a variety of real-time scenarios, with data stored in digital memory for later use in a medical database. Potential commercial uses are numerous, as the technology enables medical personnel to noninvasively monitor patient vital signs in a multitude of health care settings and applications.
ExaSAT: An exascale co-design tool for performance modeling
One of the emerging challenges to designing HPC systems is understanding and projecting the requirements of exascale applications. In order to determine the performance consequences of different hardware designs, analytic models are essential because they can provide fast feedback to the co-design centers and chip designers without costly simulations. However, current attempts to analytically model program performance typically rely on the user manually specifying a performance model. Here we introduce the ExaSAT framework that automates the extraction of parameterized performance models directly from source code using compiler analysis. The parameterized analytic model enables quantitative evaluation of a broad range of hardware design trade-offs and software optimizations on a variety of different performance metrics, with a primary focus on data movement as a metric. Finally, we demonstrate the ExaSAT framework’s ability to perform deep code analysis of a proxy application from the Department of Energy Combustion Co-design Center to illustrate its value to the exascale co-design process. ExaSAT analysis provides insights into the hardware and software trade-offs and lays the groundwork for exploring a more targeted set of design points using cycle-accurate architectural simulators.
A survey of high level frameworks in block-structured adaptive mesh refinement packages
Over the last decade block-structured adaptive mesh refinement (SAMR) has found increasing use in large, publicly available codes and frameworks. SAMR frameworks have evolved along different paths. Some have stayed focused on specific domain areas, others have pursued a more general functionality, providing the building blocks for a larger variety of applications. In this survey paper we examine a representative set of SAMR packages and SAMR-based codes that have been in existence for half a decade or more, have a reasonably sized and active user base outside of their home institutions, and are publicly available. The set consists of a mix of SAMR packages and application codes that cover a broad range of scientific domains. We look at their high-level frameworks, their design trade-offs and their approach to dealing with the advent of radical changes in hardware architecture. Finally, the codes included in this survey are BoxLib, Cactus, Chombo, Enzo, FLASH, and Uintah.
Tiling as a Durable Abstraction for Parallelism and Data Locality
Tiling is a useful loop transformation for expressing parallelism and data locality. Automated tiling transformations that preserve data-locality are increasingly important due to hardware trends towards massive parallelism and the increasing costs of data movement relative to the cost of computing. We propose TiDA as a durable tiling abstraction that centralizes parameterized tiling information within array data types with minimal changes to the source code. The data layout information can be used by the compiler and runtime to automatically manage parallelism, optimize data locality, and schedule tasks intelligently. In this study, we present the design features and early interface of TiDA along with some preliminary results.
Software design space exploration for exascale combustion co-design
The design of hardware for next-generation exascale computing systems will require a deep understanding of how software optimizations impact hardware design trade-offs. In order to characterize how co-tuning hardware and software parameters affects the performance of combustion simulation codes, we created ExaSAT, a compiler-driven static analysis and performance modeling framework. Our framework can evaluate hundreds of hardware/software configurations in seconds, providing an essential speed advantage over simulators and dynamic analysis techniques during the co-design process. Our analytic performance model shows that advanced code transformations, such as cache blocking and loop fusion, can have a significant impact on choices for cache and memory architecture. Our modeling helped us identify tuned configurations that achieve a 90% reduction in memory traffic, which could significantly improve performance and reduce energy consumption. These techniques will also be useful for the development of advanced programming models and runtimes, which must reason about these optimizations to deliver better performance and energy efficiency.
An AMR Task Dependency Analysis and Communications Simulation Methodology.
Abstract not provided.
Multiphysics Simulations: Challenges and Opportunities
We consider multiphysics applications from algorithmic and architectural perspectives, where ‘‘algorithmic’’ includes both mathematical analysis and computational complexity, and ‘‘architectural’’ includes both software and hardware environments. Many diverse multiphysics applications can be reduced, en route to their computational simulation, to a common algebraic coupling paradigm. Mathematical analysis of multiphysics coupling in this form is not always practical for realistic applications, but model problems representative of applications discussed herein can provide insight. A variety of software frameworks for multiphysics applications have been constructed and refined within disciplinary communities and executed on leading-edge computer systems. We examine several of these, expose some commonalities among them, and attempt to extrapolate best practices to future systems. From our study, we summarize challenges and forecast opportunities.
A Deep Dive Analysis of Hardware Design Trade-offs for Exascale Combustion Applications.
Abstract Not Provided