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This document is about NASA advanced supercomputing by building Columbia through the configuration of its software, hardware, and network. The content also includes future plans which include tight scheduling, maintaining production while building Columbia and continued production work while reworking the facilities to acommodate Columbia.
From its bold start nearly 30 years ago and continuing today, the NASA Advanced Supercomputing (NAS) facility at Ames Research Center has enabled remarkable breakthroughs in the space agency s science and engineering missions. Throughout this time, NAS experts have influenced the state-of-the-art in high-performance computing (HPC) and related technologies such as scientific visualization, system benchmarking, batch scheduling, and grid environments. We highlight the pioneering achievements and innovations originating from and made possible by NAS resources and know-how, from early supercomputing environment design and software development, to long-term simulation and analyses critical to design safe Space Shuttle operations and associated spinoff technologies, to the highly successful Kepler Mission s discovery of new planets now capturing the world s imagination.
The new source model was combined with the original sound propagation model developed for rectangular jets to produce a new version of the rectangular jet noise prediction code. This code was validated using a set of rectangular nozzles whose geometries were specified by NASA. Nozzles of aspect ratios two, four and eight were studied at jet exit Mach numbers of 0.5, 0.7 and 0.9, for a total of nine cases. Reynolds-averaged Navier-Stokes solutions for these jets were provided to the contactor for use as input to the code. Quantitative comparisons of the predicted azimuthal and polar directivity of the acoustic spectrum were made with experimental data provided by NASA. The results of these comparisons, along with a documentation of the propagation and source models, were reported in a journal article publication (Ref. 4). The complete set of computer codes and computational modules that make up the prediction scheme, along with a user's guide describing their use and example test cases, was provided to NASA as a deliverable of this task. The use of conformal mapping, along with simplified modeling of the mean flow field, for noise propagation modeling was explored for other nozzle geometries, to support the task milestone of developing methods which are applicable to other geometries and flow conditions of interest to NASA. A model to represent twin round jets using this approach was formulated and implemented. A general approach to solving the equations governing sound propagation in a locally parallel nonaxisymmetric jet was developed and implemented, in aid of the tasks and milestones charged with selecting more exact numerical methods for modeling sound propagation, and developing methods that have application to other nozzle geometries. The method is based on expansion of both the mean-flowdependent coefficients in the governing equation and the Green's function in series of orthogonal functions. The method was coded and tested on two analytically prescribed mean flows which were meant to represent noise reduction concepts being considered by NASA. Testing (Ref. 5) showed that the method was feasible for the types of mean flows of interest in jet noise applications. Subsequently, this method was further developed to allow use of mean flow profiles obtained from a Reynolds-averaged Navier-Stokes (RANS) solution of the flow. Preliminary testing of the generalized code was among the last tasks completed under this contract. The stringent noise-reduction goals of NASA's Fundamental Aeronautics Program suggest that, in addition to potentially complex exhaust nozzle geometries, next generation aircraft will also involve tighter integration of the engine with the airframe. Therefore, noise generated and propagated by jet flows in the vicinity of solid surfaces is expected to be quite significant, and reduced-order noise prediction tools will be needed that can deal with such geometries. One important source of noise is that generated by the interaction of a turbulent jet with the edge of a solid surface (edge noise). Such noise is generated, for example, by the passing of the engine exhaust over a shielding surface, such as a wing. Work under this task supported an effort to develop a RANS-based prediction code for edge noise based on an extension of the classical Rapid Distortion Theory (RDT) to transversely sheared base flows (Refs. 6 and 7). The RDT-based theoretical analysis was applied to the generic problem of a turbulent jet interacting with the trailing edge of a flat plate. A code was written to evaluate the formula derived for the spectrum of the noise produced by this interaction and results were compared with data taken at NASA Glenn for a variety of jet/plate configurations and flow conditions (Ref. 8). A longer-term goal of this task was to work toward the development of a high-fidelity model of sound propagation in spatially developing non-axisymmetric jets using direct numerical methods for solving the relevant equations. Working with NASA Glenn Acoustics Branch personnel, numerical methods and boundary conditions appropriate for use in a high-resolution calculation of the full equations governing sound propagation in a steady base flow were identified. Computer codes were then written (by NASA) and tested (by OAI) for an increasingly complex set of flow conditions to validate the methods. The NASA-supplied codes were ported to the High-End Computing resources of the NASA Advanced Supercomputing facility for testing and validation against analytical (where possible) and independent numerical solutions. The cases which were completed during the course of this contract were solutions of the two-dimensional linearized Euler equations with no mean flow, a uniform mean flow and a nonuniform mean flow representative of a parallel flow jet.
This presentation describes the Nagios 4 APIs and how the NASA Advanced Supercomputing at Ames Research Center is employing them to upgrade its graphical status display (the HUD) and explain why it's worth trying to use them yourselves.
The Transiting Exoplanet Survey Satellite (TESS) will conduct a search for Earth's closest cousins starting in early 2018 and is expected to discover approximately 1,000 small planets with R(sub p) less than 4 (solar radius) and measure the masses of at least 50 of these small worlds. The Science Processing Operations Center (SPOC) is being developed at NASA Ames Research Center based on the Kepler science pipeline and will generate calibrated pixels and light curves on the NASA Advanced Supercomputing Division's Pleiades supercomputer. The SPOC will also search for periodic transit events and generate validation products for the transit-like features in the light curves. All TESS SPOC data products will be archived to the Mikulski Archive for Space Telescopes (MAST).
Combining the strengths of MPIProf and IOT, an efficient and systematic method is devised for I/O characterization at the per-job, per-rank, per-file and per-call levels of HPC programs running on the NASA Advanced Supercomputing Center. This method is applied to answer four I/O questions in this paper. A total of 13 MPI programs and 15 cases, ranging from 24 to 5968 ranks, are analyzed to establish the I/O landscape from answers to the four questions. Four of the 13 programs use MPI I/O and the behavior of their collective writes depends on the specific implementation of the MPI library used. The SGI MPT library, the prevailing MPI library for our systems, was found to gather small writes from a large number of ranks to perform larger writes by a small subset of collective buffering ranks. The number of collective buffering ranks invoked by MPT depends on the Lustre stripe count and the number of nodes used for the run. A demonstration of varying the stripe count to achieve double-digit speedup of one program's I/O was presented. Another program, which concurrently opens private files by all ranks and could potentially create a heavy load on the Lustre servers, was identified. The ability to systematically characterize I/O for a large number of programs running on a supercomputer, seek I/O optimization opportunity and identify programs that could cause a high load and instability on the filesystems is important for pursuing exascale in a real production environment.
This report provides a description and performance evaluation of the optimization techniques for high performance computing (HPC) implementation of the open source Computational Materials mini-application Aladyn (https://github.com/nasa/aladyn). Aladyn is a basic molecular dynamics code written in FORTRAN 2003, which is designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory (DFT) method. While achieving orders of magnitude faster computational performance than DFT, the ANN-based approach was still very computationally demanding compared to the conventional approach of using empirically fitted energy functions. After its initial development, Aladyn was evaluated and optimized by experts at the NASA Advanced Supercomputing (NAS) division to exploit modern supercomputer architectures. The code has been optimized for execution on multicore central processing units (CPUs), including Intel® Skylake microarchitecture, and on graphic accelerators, such as Nvidia® V100 graphic processing units (GPUs), using Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC) programming interfaces. The optimization achieved a speedup of 4.7 times the baseline version on CPU performance and an additional 2.4 times on CPU+GPU performance. Atomistic computer simulations are a fundamental tool in materials research to model material properties form physics-based first principles. Atomic interaction, governed by Quantum Mechanics (QM) require sophisticated and highly computationally demanding mathematical models to calculate [1]. Classical methods use approximate functional forms, empirically fitted through a set of variable parameters to emulate atomic energies as direct functions of atomic coordinates [2]. While empirical potentials are computationally much simpler, allowing simulations of large-scale systems of up to a trillion (1012) atoms [3], they are substantially less accurate compared to quantum calculations and applicable only to very specific atomic configurations or predefined crystallographic phases. A recently suggested approach is to use heuristic machine learning methods [4], such as those based on Adaptive Neural Networks (ANNs) to predict atomic energies, after being trained on a sufficiently large database of QM-calculated structures [5,6]. This approach reduces significantly the computational complexity, allowing for simulations of orders of magnitude larger systems compared to QM-based methods without compromising accuracy. Still, compared to classical methods using empirical energy functions, ANN methods remain two- to three orders of magnitude more computationally demanding. Hence, the computational cost of simulations, together with the need for extensive training of ANNs, still makes the practical implementation of ANN-based methods quite challenging. The purpose of the Aladyn mini-application software [7], available as open source at https://github.com/nasa/aladyn, is to be a testbed for exploring possible optimization strategies to develop highly scalable parallel algorithms for ANN-based atomistic simulations. Aladyn is aimed at utilizing the architecture of the high-end modern highperformance computing (HPC) hardware based on multicore central processing units (CPUs) equipped with graphic processing unit (GPU) accelerators. Specifically, the goal is to optimize the performance on a single HPC compute node, before implementing scaling to multi-node parallelization using message passing interface (MPI). At the same time, the open source code of Aladyn can serve as a training model for students and professors in academia.
The Unsteady Pressure-Sensitive Paint (uPSP) projects uses Pressure-Sensitive paint applied over aerospace models during wind tunnel testing to collect pressure data with high spatial and temporal resolution in order to inform unsteady aerodynamics studies. For each of the 800+ experimental runs, four cameras generate up to 50 GB of video data, which must then be processed, analyzed, and visualized on the NASA Advanced Supercomputing system (NAS) to assess the result. One of the final data analysis products is the dynamic modal decomposition (DMD) results, which decomposes the pressure reading signals by their frequency component. The goal of this project is to visualize the DMD results over a 3D rendering of the model, using efficient and parallelized python routines. The software uses the pytecplot library, a high-level API that connects python scripting to a Tecplot 360 engine. Tecplot is an industry standard high-performance visualization tool that can handle large datasets and workflow. Various animation, rendering, and image-combination techniques were investigated to generate the final videos using OpenCV on the NAS. The final result is a software tool that takes in data products from the uPSP processing chain and generates high resolution visualization videos in parallel for every data file, allowing researchers to view their results efficiently and at an unprecedentedly detailed level.
Pressure transducers have been the instrumentation of choice for measuring unsteady flow phenomena. With recent advances in high-speed cameras, high-powered LEDs, and fast-response, pressure-sensitive paint, the unsteady pressure-sensitive paint (uPSP) technique has become a valuable alternative for production wind tunnel facilities, enabling time-resolved measurements of unsteady pressure fluctuations over a dense spatial grid on a wind tunnel model. Launch vehicle ground tests have proven to be a particularly well-matched application for uPSP due to the high signal level relative to tunnel background acoustics, relatively simple camera optical access, and rigidity of the model in wind-on test conditions. This presentation will highlight recent advances in data reduction of uPSP measurement data from recent launch vehicle wind tunnel tests at the NASA Ames Unitary Plan Wind Tunnel Complex (UPWT). The system can provide both localized surface pressure spectra as well as regional or zonal estimates of turbulence correlation model parameters. In addition, integrated vehicle-scale loads can be provided for buffet analysis. Data is reduced at the on-premise NASA Advanced Supercomputer (NAS) Division for just-in-time delivery of results during an ongoing wind tunnel test.
An unsteady pressure-sensitive paint (uPSP) system has been developed to provide time-resolved pressure measurements in the NASA Langley Transonic Dynamics Tunnel (TDT). Obtaining these measurements necessitated the development of environmental enclosures to protect the high-speed camera and ultraviolet lights required for uPSP from the harsh environment present during tunnel operation. Since the facility main drive was non-functioning during the testing window, performance of the uPSP system was demonstrated using an impinging jet with a passive oscillator attachment to provide unsteady flow with a known frequency independent of amplitude. Measurements were obtained for tunnel pressures ranging from 565 to 2116 psf, and model angles of attack between -4 degrees and 4 degrees. Results indicate that the system is capable of measuring surface pressure differentials on the order of 0.01 psi at full scale with a camera frame rate of at least 10 kHz. Spectral analysis shows that the fundamental frequency of the oscillating jet is captured by the uPSP system, as are the second and third harmonics. Dynamic mode decomposition highlights the dominant coherent spatial structures of the surface pressure, along with the associated frequency and growth rate of each mode, allowing for a de-noised reconstruction of the uPSP measurements. The experimental campaign outlined within this report also confirmed compatibility of the uPSP system with the TDT facility data acquisition system, and verified the successful integration with existing processing capabilities within the NASA advanced supercomputing environment.
An unsteady pressure-sensitive paint (uPSP) system has been developed to provide time-resolved pressure measurements in the NASA Langley Transonic Dynamics Tunnel (TDT). Obtaining these measurements necessitated the development of environmental enclosures to protect the high-speed camera and ultraviolet lights required for uPSP from the harsh environment present during tunnel operation. Since the facility main drive was non-functioning during the testing window, performance of the uPSP system was demonstrated using an impinging jet with a passive oscillator attachment to provide unsteady flow with a known frequency independent of amplitude. Measurements were obtained for tunnel pressures ranging from 565 to 2116 psf, and model angles of attack between -4 degrees and 4 degrees. Results indicate that the system is capable of measuring surface pressure differentials on the order of 0.01 psi at full scale with a camera frame rate of at least 10 kHz. Spectral analysis shows that the fundamental frequency of the oscillating jet is captured by the uPSP system, as are the second and third harmonics. Dynamic mode decomposition highlights the dominant coherent spatial structures of the surface pressure, along with the associated frequency and growth rate of each mode, allowing for a de-noised reconstruction of the uPSP measurements. The experimental campaign outlined within this report also confirmed compatibility of the uPSP system with the TDT facility data acquisition system, and verified the successful integration with existing processing capabilities within the NASA advanced supercomputing environment.
An unsteady pressure-sensitive paint (uPSP) system has been developed to provide time-resolved pressure measurements in the NASA Langley Transonic Dynamics Tunnel (TDT). Obtaining these measurements necessitated the development of environmental enclosures to protect the high-speed camera and ultraviolet lights required for uPSP from the harsh environment present during tunnel operation. Since the facility main drive was non-functioning during the testing window, performance of the uPSP system was demonstrated using an impinging jet with a passive oscillator attachment to provide unsteady flow with a known frequency independent of amplitude. Measurements were obtained for tunnel pressures ranging from 565 to 2116 psf, and model angles of attack between -4 degrees and 4 degrees. Results indicate that the system is capable of measuring surface pressure differentials on the order of 0.01 psi at full scale with a camera frame rate of at least 10 kHz. Spectral analysis shows that the fundamental frequency of the oscillating jet is captured by the uPSP system, as are the second and third harmonics. Dynamic mode decomposition highlights the dominant coherent spatial structures of the surface pressure, along with the associated frequency and growth rate of each mode, allowing for a de-noised reconstruction of the uPSP measurements. The experimental campaign outlined within this report also confirmed compatibility of the uPSP system with the TDT facility data acquisition system, and verified the successful integration with existing processing capabilities within the NASA advanced supercomputing environment. Note: this presentation is an MP4 video with sound, color with a run time of 10 minutes 37 seconds.
As efforts of the National Aeronautics and Space Administration (NASA) and the Federal Aviation Administration (FAA) continue to digitize the air traffic management (ATM) domain, there is countless times of need for downstream natural language processing (NLP) tasks such as named entity recognition, text summarization, classification, and more. Although there are a plethora of open-sourced pre-trained transformer models in the NLP field such as BERT, RoBERTa, XLNet, and GPT-3, these models are trained on general corpora and perform poorly on domain-specific terminology and phraseology seen in ATM documents such as Notice to Airmen (NOTAMs) and Letters of Agreement (LoA). Our proposed research objective will be to first gather a large corpus of air traffic management related documents, orders, notices, books, technical papers, conference papers, articles, and other miscellaneous sources of text data from the FAA, NASA, and accredited conference and publication societies. After gathering this data, many steps will have to be taken to collate and preprocess the data into a format understandable by our test transformer models. Thirdly, we will set up training pipelines to train the RoBERTa model on its unsupervised training task masked language modelling (MLM) using resources provided by the NASA Advanced Supercomputing (NAS) facilities. Finally, these fine-tuned transformer models will be evaluated on their performance on down-stream NLP tasks as mentioned above, to show whether they will be effective when working with ATM related data or not. Once complete, this model could be made open-sourced on the HuggingFace website, where the rest of the ATM community can access and utilize this tool.
Pressure transducers have been the instrumentation of choice for measuring unsteady flow phenomena. With recent advances in high-speed cameras, high-powered LEDs, and fast-response, pressure-sensitive paint, the unsteady pressure-sensitive paint (uPSP) technique has become a valuable alternative for production wind tunnel facilities, enabling time-resolved measurements of unsteady pressure fluctuations over a dense spatial grid on a wind tunnel model. Launch vehicle ground tests have proven to be a particularly well-matched application for uPSP due to the high signal level relative to tunnel background acoustics, relatively simple camera optical access, and rigidity of the model in wind-on test conditions. This presentation will highlight recent advances in data reduction of uPSP measurement data from recent launch vehicle wind tunnel tests at the NASA Ames Unitary Plan Wind Tunnel Complex (UPWT). The system can provide both localized surface pressure spectra as well as regional or zonal estimates of turbulence correlation model parameters. In addition, integrated vehicle-scale loads can be provided for buffet analysis. Data is reduced at the on-premise NASA Advanced Supercomputer (NAS) Division for just-in-time delivery of results during an ongoing wind tunnel test.
Since 2019, a group out of NASA Ames Research Center(ARC)has been focused on implementing systematic updates to make unsteady pressure-sensitive paint (uPSP) a more viable capability for production wind tunnel testing. Focusing on the general categories of data acquisition, data transfer, data processing, and data visualization, the uPSP Development Team has made several improvements to increase data quality and innovate a more workable system that would provide valuable surface pressure data to customers. As a result of the COVID-19 pandemic and several initial demonstration tests prior to the formal start of the development effort, the decision was made to focus on launch vehicles as the test article. To-date this development has focused on implementation in the ARC Unitary Plan Wind Tunnel 11-by11-footTransonic Wind Tunnel due to the large optical access of the test section and that the NASA Advanced Supercomputer is located at ARC. This paper summarizes the project origins and the results of four years of development effort, which will all culminate in a final demonstration test in the first half of2024.
The Unsteady Pressure-Sensitive Paint (uPSP) Development Team outof NASA Ames Research Center (ARC) has spent the past five yearsimproving the systems and processes to advance the uPSP technology for production-level wind tunnel testing. Already considered turnkey for small-scale and research applications, development in acquisition, calibration, data transfer, and data processing were needed to be useful to customers testing at NASA wind tunnels. This development focused at ARC at the Unitary Plan Wind Tunnel (UPWT) 11-by 11-ft Transonic Wind Tunnel due to the large optical access of the test section and the NASA Advanced Supercomputer(NAS), also located at ARC. A Launch Vehicle Demonstration Test (LVDT) at the UPWT represents a milestone of this initial phase of development where several new improvements were demonstrated in a production wind tunnel environment for the first time. LVDT was conducted in April 2024 and used a 4% forebody Space Launch System (SLS) Block 1B model as the test article. Both a crew and cargo configuration were tested, with varying Mach numbers, pressures, model positions, and camera magnifications. This paper summarizes the details of the test and is part of a collection with four additional papers that provide greater detail on: high-speed lifetime methodology, spectral proper orthogonal decomposition analysis, quality of high-resolution data compared to Corcos model, and data quality, calibration, and uncertainty.
The research outlined in this presentation investigates the use of various lunar regolith simulants in geopolymer lunar concrete mixes mixed and cured on the International Space Station (ISS). The motivation for this work is to study the effects of gravity on the microstructure of alkali-activated materials cured with heat, and to develop materials for the construction of long-term infrastructure on the lunar surface with in-situ resource utilization (ISRU). ISRU for construction materials reduces the cost and mass of payloads related to lunar construction. The advantage of geopolymer concrete as opposed to traditional portland cement concrete is that water acts as a medium for the polymerization reaction and leaves the system throughout the process, reducing its demand. Twelve samples of lunar regolith simulant and a solution composed of sodium hydroxide and sodium silicate were sent to the ISS. The three simulants were OPRH2N, OPRL2N, and JSC-1AF, using only particles less than 53 µm in diameter to increase reactivity of the simulant. Simulant to solution ratios were determined by workability while mixing. The simulant and solution were sealed Burst Pouches® along with 2 other sealed bags to prevent material from leaking. Crew member F-14 conducted testing on the ISS by introducing the solution to the simulant in the Burst Pouch®, mixing the sample with a spatula, and then clamping the specimen in the fresh state to prevent flow inside the Burst Pouch®. These specimens were then put in a thermos heated to 80C via sealed drinking water bags to cure for 24 hours with a temperature logger. The cured specimens remained in microgravity for at least 28 days and were returned from the ISS in February 2025. The specimens were then brought to the NASA Marshall Space Flight Center (MSFC) to analyze. Material characterization consisted of conducting Micro-CT tests of entire samples in their sealed apparatus to a resolution of 25µm. 2D image slices were saved in each orthogonal direction of each specimen at a 0.03 mm step size from the 3D model to conduct analytical porosity calculations. Representative samples from each specimen were sampled to perform helium gas pycnometery and were then mounted in resin for SEM imaging, EDS, and nanoindentation. Porosity was analyzed analytically using micromechanics modelling with the assistance of the NASA Multiscale Analysis Tool (NASMAT), as well as the NASA Advanced Supercomputing (NAS) servers (V. Saseendran & N. Yamamoto, 2024). Density was measured using helium gas pycnometery and was then compared to the theoretical density for experimental porosity calculation. Due to the samples’ non-uniform shape being cured in a pouch, traditional compression and tensile strength testing could not be performed. Nanoindentation was conducted at Clarkson University to determine the microhardness and reduced modulus of elasticity. Results from flight samples can be compared to ground samples currently in DLR’s possession to determine the effect on microstructure from being mixed and cured in microgravity. This study gives further insight and understanding of geopolymer lunar concrete and its viability as a lunar construction material with ISRU.
A brief overview of NASA's recent experience in supercomputing is presented from two perspectives: early systems development and advanced supercomputing applications. NASA's role in supercomputing systems development is illustrated by discussion of activities carried out by the Numerical Aerodynamical Simulation Program. Current capabilities in advanced technology applications are illustrated with examples in turbulence physics, aerodynamics, aerothermodynamics, chemistry, and structural mechanics. Capabilities in science applications are illustrated by examples in astrophysics and atmospheric modeling. Future directions and NASA's new High Performance Computing Program are briefly discussed.