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Machine learning (ML) is gaining popularity in the Earth science domain. Higher the amount of quality data, the better the model. CPU training of such ML models is slow; GPU is used for training. Maintaining GPU servers is an additional responsibility. Multiple iterations of experiments needed before a better performing model is trained. Dataset creation, versioning of datasets, models, and experiments is hard.
Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will be discussing the progress made developing SWIPE: Spectral Water Inversion Processor and Emulator. SWIPE is a platform for advanced modeling of coastal and inland aquatic habitats. The goal is create a comprehensive and cohesive system to leverage recent advancements in computation and machine learning to develop a synthetic training ground for sensitivity studies and algorithm development. The four principal facets of SWIPE include: 1. Advanced two-layer coated sphere bio-optical modeling and GPU radiative transfer modeling, 2. Big Data involving massive synthetic spectral libraries of optical properties of various global aquatic particles, surface reflectance, and top-of-atmosphere reflectance, all at hyperspectral resolution leveraging high-end computing systems at NASA Ames Research Center, 3. Deep Learning for algorithm development for water quality inversion of concentrations of common biogeophysical variables as well as optics, full uncertainty characterization by water type, and forward emulation, and lastly, 4. Image Processing for application of developed retrieval algorithms for both hyperspectral and multispectral sensors with experimental corrections for global adjacency, noise, sunglint, and benthic reflectance. This presentation will demonstrate the Equivalent Algal Populations (EAP) two-layer coated sphere scattering model which has been used develop spectral libraries of hyperspectral inherent optical properties of roughly 80 species of phytoplankton, covering 15 different classes and nine taxonomic functional types. The EAP model was also used to derive spectral properties of 10 different non-algal particle functional types. Examples of how the SMART-G (Speed-up Monte-carlo Advanced Radiative Transfer using GPU) radiative transfer code is used to model optically complex aquatic signals will be presented and discussed in the context of creating a massive synthetic database which can leverage the full power of next generation machine learning techniques and high end computing for water quality inversion. We will discuss our active investigation in things like appropriate model architectures, dimensionality reduction techniques such as PCA and autoencoders, uncertainty quantification and abstaining, and which variables actually benefit most from hyperspectral information versus multispectral resolution. We are also curious about questions relating to cost/benefit analysis in terms of computation resources, neural network complexity, and data volumes. Answers to these questions will hopefully elaborate on cost efficiency for potential future sensor design considerations.
Earth science (ES) digital twins will help us understand the complex interactions and interrelationships that make up our Earth system and the impacts of earth science phenomena on it. Our work addresses two underdeveloped areas in current ES digital twin work: improving the understanding and interaction with ES model outputs by using Virtual and Mixed Reality (XR) tools and improving the non-intuitive mapping of continuous ES natural phenomena to gridded reference frames in current numerical models. Traditionally, scientists working on ES view and analyze the results of calculated or measured observables with static 1-dimensional (1D), 2D or 3D plots displayed on flat computer screens or paper. Using such limited mediums, it can be very difficult to identify, track and understand the evolution of key features due to poor viewing angles and the nature of flat computer screens. In addition, numerical models, such as the NASA Goddard Earth Observing System (GEOS) ES model, are almost exclusively formulated, visualized and analyzed in an Eulerian reference frame with fixed grid points in space and time. However, ES phenomena such as convective clouds, hurricanes and wildfire smoke plumes are visualized and analyzed in a Lagrangian reference frame: therefore it is often difficult and unnatural to understand these phenomena in relation to each other, visualized either in an Eulerian or Lagrangian context. In 3D visualizations, data generally takes one of three forms: gridded (e.g., voxelized) data, where space is divided into regions; point clouds, where data is represented as a set of points; and meshes, where objects are rendered as surfaces composed of small polygons (usually triangles). A gridded, Eulerian reference frame has been the default representation for the 2D visual analysis of atmospheric data in part because the numerical methods used to generate atmospheric model data in the first place use a gridded approach, with equations defining the relationships between the physical variables in each of a grid's cells across successive timesteps. In our work, we are particularly interested in data from GEOS. Another reason why gridded representations tend to be used for visualizing data from such models is because trajectories are difficult to interpret from representations on 2D surfaces, due to line-of-sight ambiguity. Instead of a fixed grid from GEOS, we embed a trajectory model to simulate particles' movement throughout a GEOS run. We then ingest these particle trajectories as animated point clouds with a NASA open source XR toolkit, the Mixed Reality Exploration Toolkit (MRET), and merge GEOS data with ES phenomena data onto one combined visualization that the user can intuitively interact with. Efficient rendering of arbitrarily large point clouds is an ongoing challenge being addressed by the computer science community, with the GPU-based optimizations and efficient GPU memory utilization a common theme of recent advances, especially for XR, where sustained high frame rate is mandatory to save the user from suffering due to simulation sickness. In this work, we describe and evaluate our progress in choosing and implementing appropriate methods for rendering arbitrarily large point clouds within MRET for XR. While tracking the XR headset enables the immersion of a user within a 3D scene of a data visualization, tracking of XR handheld controllers or user’s hands enables us to implement intuitive user interactions with the visualized datasets. Conventional tools require a user working with an ES visualization to conduct many interactions to commit their intended selections or manipulations with a visualized dataset; for example to specify a set of points in 3D space. Doing so in a 2D flat screen interface has traditionally required specifying a set of points in three distinct 2D coordinate systems (XY, XZ, and YZ), which is cumbersome. In other scientific domains, it has been shown that specifying or selecting a location or volume in XR using handheld controllers or tracked hands allows for greater speed and accuracy. We anticipate the same will hold true for atmospheric data, and we will share initial results of measuring the utility of such an interface. Notably, as the data being visualized is generated by GEOS as a prediction based on initial conditions, an intended application of our tool is to serve as part of an iterative feedback loop. Through XR, a scientist will review and manipulate a GEOS model run, modifying the conditions as needed to do subsequent runs of GEOS. Thereby, XR-based improvements to speed and accuracy of 3D tagging of points minimizes the effort required by both the scientist and the computer cluster conducting the necessary calculations.
Computational fluid dynamics (CFD) has become widely used in the design and analysis of turbomachinery components such as centrifugal compressors. However, CFD is only a limited representation of experimental cases and struggles to model complex flows or can lack small model details to increase computation speed. To make advancements in compressor technology, designers need tools like CFD that can help them predict flow behavior in new designs. The High Efficiency Centrifugal Compressor (HECC) was designed by United Technologies Research Center (UTRC) for NASA to investigate the difficulties behind improving centrifugal compressor technology and to provide an open case to the turbomachinery research community. CFD analysis was completed by UTRC to aid in the design process. The pre-test study significantly overpredicted the compressor’s performance, and while post-test analysis was more representative, its results have yet to be publicly available. CFD technology has also since improved in recent years. In this study, a high-fidelity computational model for the vaneless configuration of HECC operating at its design point was developed and compared to an equivalent experimental case. This model incorporated GPU versions of mesh generation and solver codes from AeroDynamic Solutions, Inc. The development of such a model is significant since there has yet to be a published numerical simulation of HECC’s vaneless configuration. Additionally, the use of GPU codes decreases computation times for the model, which has allowed for the inclusion of impeller blade fillets. Initial results showed a general overprediction of compressor performance by the model. Predictions were more accurate for the impeller compared to predictions for the full compressor stage. Spanwise analyses showed that the model tended to follow flow behavior patterns exhibited in the experiment. Beyond this study, this model will be iterated upon and experimentally validated to further examine HECC performance.
In preparation of the return of humans to the Moon with the coming Artemis missions, NASA scientists must evaluate proposed landing site locations for terrain and communications viability. The Glenn Research Center Communication Analysis Suite (GCAS) combines sophisticated communication network models with accurate lunar terrain to access sites across the Moon’s south pole. Given the importance of proper site selection to crew safety and mission success, many locations need to be analyzed resulting in a large computational load needing to be performed. To meet the growing project demands, development has begun to improve the runtime efficiency of GCAS with GPU parallelization by way of multi-stream CUDA kernels. One of the most prominent factors in kernel optimization is the proper selection of thread-block dimensions in order to maximize the concurrent operation on the device. Typically, thread-block dimensions are optimized by hand requiring many stages of benchmarking and iteration. Additionally, given the main conditions to optimization are the physical GPU architecture and problem size, these optimal dimensions are non-portable and fragile in their scope. As such, a novel optimization routine was developed to generate the optimal thread-block dimensions during runtime with considerations to hardware specifications and problem size resolving the issues of portability and enabling the function of more dynamic routines.
Physics-based simulations are actively used in the design, testing, and operations phases of surface and near-surface planetary space missions. One of the challenges in realtime simulations is the ability to handle large multi-resolution terrain data sets within models as well as for visualization. In this paper, we describe special techniques that we have developed for visualization, paging, and data storage for dealing with these large data sets. The visualization technique uses a real-time GPU-based continuous level-of-detail technique that delivers multiple frames a second performance even for planetary scale terrain model sizes.
NASA is interested in designing a spacecraft capable of visiting a Near Earth Object (NEO), performing experiments, and then returning safely. Certain periods of this mission will require the spacecraft to remain stationary relative to the NEO. Such situations require an anchoring mechanism that is compact, easy to deploy and upon mission completion, easily removed. The design philosophy used in the project relies on the simulation capability of a multibody dynamics physics engine. On Earth it is difficult to create low gravity conditions and testing in low gravity environments, whether artificial or in space is costly and therefore not feasible. Through simulation, gravity can be controlled with great accuracy, making it ideally suited to analyze the problem at hand. Using Chrono::Engine [1], a simulation package capable of utilizing massively parallel GPU hardware, several validation experiments will be performed. Once there is sufficient confidence, modeling of the NEO regolith interaction will begin after which the anchor tests will be performed and analyzed. The outcome of this task is a study with an analysis of several different anchor designs, along with a recommendation on which anchor is better suited to the task of anchoring. With the anchors tested against a range of parameters relating to soil, environment and anchor penetration angles/velocities on a NEO.
A linear solver algorithm used by a large-scale unstructured-grid computational fluid dynamics application is examined for a broad range of familiar and emerging architectures. Efficient implementation of a linear solver is challenging on recent CPUs offering vector architectures. Vector loads and stores are essential to effectively utilize available memory bandwidth on CPUs, and maintaining performance across different CPUs can be difficult in the face of varying vector lengths offered by each. A similar challenge occurs on GPU architectures, where it is essential to have coalesced memory accesses to utilize memory bandwidth effectively. In this work, we demonstrate that restructuring a computation, and possibly data layout, with regard to architecture is essential to achieve optimal performance by establishing a performance benchmark for each target architecture in a low level language such as vector intrinsics or CUDA. In doing so, we demonstrate how a linear solver kernel can be mapped to Intel® Xeon™ and Xeon Phi™, Marvell® ThunderX2®, NEC® SX-Aurora™ TSUBASA Vector Engine, and NVIDIA® and AMD® GPUs. We further demonstrate that the required code restructuring can be achieved in higher level programming environments such as OpenACC, OCCA, and Intel® OneAPI™/SYCL, and that each generally results in optimal performance on the target architecture. Relative performance metrics for all implementations are shown, and subjective ratings for ease of implementation and optimization are suggested.
As next-generation exascale-class systems arrive, existing software must be updated accordingly to effectively utilize these systems. For high concurrency and energy efficiency, many of these systems utilize GPU architectures. In this work, we present a CUDA C++ implementation of FUN3D's thermochemical nonequilibrium capability for turbulent flows. Efficiency is demonstrated at scale using the Summit system at the Oak Ridge Leadership Computing Facility which is representative of future exascale systems. This work enables faster, higher fidelity, and scale-resolving simulations of thermochemical nonequilibrium flows including reentry, hypersonics, and combustion.
Thermochemical nonequilibrium flow simulation capabilities have been previously implemented, verified, and validated for central processing unit (CPU) systems in NASA’s unstructured-grid computational fluid dynamics solver FUN3D. Many exascale-class high-performance computing systems will rely on graphics processing unit (GPU) architectures for high throughput and energy efficiency; thus, CPU-based scientific computing software unable to effectively utilize these systems must be updated. In this work, we present a CUDA C++ implementation of FUN3D’s thermochemical nonequilibrium flow simulation capabilities targeting NVIDIA Tesla GPUs. An overview of the porting and optimization strategy is described and performance comparisons with other recent architectures are presented. Scaling to thousands of GPUs is demonstrated, yielding computational performance equivalent to that of several million CPU cores. The implementation enables efficient, high-fidelity, scale-resolving simulations of thermochemical nonequilibrium flows for many applications including atmospheric entry, hypersonics, and combustion.
Retropropulsion ground tests require significant compromises on physical scale, instrumentation, configuration, and environments. Matching the full Martian environment is simply not possible on Earth. Ground tests of retropropulsion configurations thus far have neglected effects of chemistry due to physical constraints of wind tunnel models and facilities; most experiments use inert simulant gases at relatively low temperatures. As such, a strong reliance on high-fidelity computational analyses is required to expand the knowledge of retropropulsion aerodynamics. In this work, we investigate the effects of chemistry using scale-resolving computational fluid dynamics (CFD) with finite-rate chemistry and a graphics processing unit (GPU)-enabled implementation of the NASA FUN3D flow solver on a human-scale Mars lander concept at supersonic freestream conditions. Results are compared to previous perfect gas simulations.
Retropropulsion ground tests require significant compromises on physical scale, instrumentation, configuration, and environments. Matching the full Martian environment is simply not possible on Earth. Ground tests of retropropulsion configurations thus far have neglected effects of chemistry due to physical constraints of wind tunnel models and facilities; most experiments use inert simulant gases at relatively low temperatures. As such, a strong reliance on high-fidelity computational analyses is required to expand the knowledge of retropropulsion aerodynamics. In this work, we investigate the effects of chemistry using scale-resolving computational fluid dynamics (CFD) with finite-rate chemistry and a graphics processing unit (GPU)-enabled implementation of the NASA FUN3D flow solver on a human-scale Mars lander concept at supersonic freestream conditions. Results are compared to previous perfect gas simulations.
Overview - Goals of OVERFLOW GPU Port - Miniapps - Lessons Learned - Current Status - What is Coming
High-performance computing (HPC) architectures are trending toward manycore paradigms such as graphics processing units (GPUs). Approximately half of the top 100 publicly disclosed supercomputers in the world utilize GPU accelerators for performance. This is in contrast to a decade ago, where there were only a few such machines in the top 100. It is not currently possible to compile and run legacy central processing unit (CPU) software efficiently on GPUs without significant refactoring. Though a number of frameworks offering performance portability exist, none offer a standardized specification that is supported by all major hardware vendors. Additionally, experiences show that obtaining a high percentage of peak performance often requires architecture-specific code. This work details a pragmatic multi-architecture computational fluid dynamics library focused on aerospace problems across the speed range from low subsonic to hypersonic flows involving thermochemical nonequilibrium. A thin abstraction layer above NVIDIA CUDA C++ is utilized, which enables primarily single-source software currently capable of running efficiently on multicore CPUs, NVIDIA GPUs, AMD GPUs, and Intel GPUs. Results on various problems of interest across the speed range are presented and performance is compared between various architectures.
High-performance computing (HPC) architectures are trending toward manycore paradigms such as graphics processing units (GPUs). Approximately half of the top 100 publicly disclosed supercomputers in the world utilize GPU accelerators for performance. This is in contrast to a decade ago, where there were only a few such machines in the top 100. It is not currently possible to compile and run legacy central processing unit (CPU) software efficiently on GPUs without significant refactoring. Though a number of frameworks offering performance portability exist, none offer a standardized specification that is supported by all major hardware vendors. Additionally, experiences show that obtaining a high percentage of peak performance often requires architecture-specific code. This work details a pragmatic multi-architecture computational fluid dynamics library focused on aerospace problems across the speed range from low subsonic to hypersonic flows involving thermochemical nonequilibrium. A thin abstraction layer above NVIDIA CUDA C++ is utilized, which enables primarily single-source software currently capable of running efficiently on multicore CPUs, NVIDIA GPUs, AMD GPUs, and Intel GPUs. Results on various problems of interest across the speed range are presented and performance is compared between various architectures.
Rendering highly detailed terrain is a process with the potential to consume a great deal of a computer’s random access memory (RAM). In a browser-based application, this resource is limited even further, leading to the necessity to use alternative methods of rendering the large amount of data needed for high detail. This report describes one such method that places the onus of rendering on the speed of the graphics processing unit (GPU) rather than on the computer’s memory. By removing attribute buffers, which contribute greatly to memory costs, from the rendering pipeline and generating the requisite attributes on the fly using a heightmap texture instead, it is estimated that memory usage can be cut down to one-sixth that of the previous method.
A brief description is given of the GPU 3-2 hardware, the test methods used, and the result of these tests. Comparison is made to unpublished data from similar hydrogen tests performed by the U.S. Army.
A calculation method is presented for a rhombic drive Stirling engine with a tubular heater and cooler and a screen type regenerator. Generally the equations presented describe power generation and consumption and heat losses. It is the simplest type of analysis that takes into account the conflicting requirements inherent in Stirling engine design. The method itemizes the power and heat losses for intelligent engine optimization. The results of engine analysis of the GPU-3 Stirling engine are compared with more complicated engine analysis and with engine measurements.