Search NASA⌕ Search

SEARCH · Search NASA

Results for “Quantum algorithm”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

187 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

HTMT-class Latency Tolerant Parallel Architecture for Petaflops Scale Computation

Computational Aero Sciences and other numeric intensive computation disciplines demand computing throughputs substantially greater than the Teraflops scale systems only now becoming available. The related fields of fluids, structures, thermal, combustion, and dynamic controls are among the interdisciplinary areas that in combination with sufficient resolution and advanced adaptive techniques may force performance requirements towards Petaflops. This will be especially true for compute intensive models such as Navier-Stokes are or when such system models are only part of a larger design optimization computation involving many design points. Yet recent experience with conventional MPP configurations comprising commodity processing and memory components has shown that larger scale frequently results in higher programming difficulty and lower system efficiency. While important advances in system software and algorithms techniques have had some impact on efficiency and programmability for certain classes of problems, in general it is unlikely that software alone will resolve the challenges to higher scalability. As in the past, future generations of high-end computers may require a combination of hardware architecture and system software advances to enable efficient operation at a Petaflops level. The NASA led HTMT project has engaged the talents of a broad interdisciplinary team to develop a new strategy in high-end system architecture to deliver petaflops scale computing in the 2004/5 timeframe. The Hybrid-Technology, MultiThreaded parallel computer architecture incorporates several advanced technologies in combination with an innovative dynamic adaptive scheduling mechanism to provide unprecedented performance and efficiency within practical constraints of cost, complexity, and power consumption. The emerging superconductor Rapid Single Flux Quantum electronics can operate at 100 GHz (the record is 770 GHz) and one percent of the power required by convention semiconductor logic. Wave Division Multiplexing optical communications can approach a peak per fiber bandwidth of 1 Tbps and the new Data Vortex network topology employing this technology can connect tens of thousands of ports providing a bi-section bandwidth on the order of a Petabyte per second with latencies well below 100 nanoseconds, even under heavy loads. Processor-in-Memory (PIM) technology combines logic and memory on the same chip exposing the internal bandwidth of the memory row buffers at low latency. And holographic storage photorefractive storage technologies provide high-density memory with access a thousand times faster than conventional disk technologies. Together these technologies enable a new class of shared memory system architecture with a peak performance in the range of a Petaflops but size and power requirements comparable to today's largest Teraflops scale systems. To achieve high-sustained performance, HTMT combines an advanced multithreading processor architecture with a memory-driven coarse-grained latency management strategy called "percolation", yielding high efficiency while reducing the much of the parallel programming burden. This paper will present the basic system architecture characteristics made possible through this series of advanced technologies and then give a detailed description of the new percolation approach to runtime latency management.

Sterling, Thomas↗
NASA NTRS2019

High-Performance Computing Optimization for Aladyn – Adaptive Neural Network Molecular Dynamics Mini-Application

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.

Yamakov, Vesselin I.↗
NASA NTRS2005

NASA Tech Briefs, March 2005

Topics covered include: Scheme for Entering Binary Data Into a Quantum Computer; Encryption for Remote Control via Internet or Intranet; Coupled Receiver/Decoders for Low-Rate Turbo Codes; Processing GPS Occultation Data To Characterize Atmosphere; Displacing Unpredictable Nulls in Antenna Radiation Patterns; Integrated Pointing and Signal Detector for Optical Receiver; Adaptive Thresholding and Parameter Estimation for PPM; Data-Driven Software Framework for Web-Based ISS Telescience; Software for Secondary-School Learning About Robotics; Fuzzy Logic Engine; Telephone-Directory Program; Simulating a Direction-Finder Search for an ELT; Formulating Precursors for Coating Metals and Ceramics; Making Macroscopic Assemblies of Aligned Carbon Nanotubes; Ball Bearings Equipped for In Situ Lubrication on Demand; Synthetic Bursae for Robots; Robot Forearm and Dexterous Hand; Making a Metal-Lined Composite-Overwrapped Pressure Vessel; Ex Vivo Growth of Bioengineered Ligaments and Other Tissues; Stroboscopic Goggles for Reduction of Motion Sickness; Articulating Support for Horizontal Resistive Exercise; Modified Penning-Malmberg Trap for Storing Antiprotons; Tumbleweed Rovers; Two-Photon Fluorescence Microscope for Microgravity Research; Biased Randomized Algorithm for Fast Model-Based Diagnosis; Fast Algorithms for Model-Based Diagnosis; Simulations of Evaporating Multicomponent Fuel Drops; Formation Flying of Tethered and Nontethered Spacecraft; and Two Methods for Efficient Solution of the Hitting- Set Problem.

Source record↗
NASA NTRS2015

Development of the Science Data System for the International Space Station Cold Atom Lab

Cold Atom Laboratory (CAL) is a facility that will enable scientists to study ultra-cold quantum gases in a microgravity environment on the International Space Station (ISS) beginning in 2016. The primary science data for each experiment consists of two images taken in quick succession. The first image is of the trapped cold atoms and the second image is of the background. The two images are subtracted to obtain optical density. These raw Level 0 atom and background images are processed into the Level 1 optical density data product, and then into the Level 2 data products: atom number, Magneto-Optical Trap (MOT) lifetime, magnetic chip-trap atom lifetime, and condensate fraction. These products can also be used as diagnostics of the instrument health. With experiments being conducted for 8 hours every day, the amount of data being generated poses many technical challenges, such as downlinking and managing the required data volume. A parallel processing design is described, implemented, and benchmarked. In addition to optimizing the data pipeline, accuracy and speed in producing the Level 1 and 2 data products is key. Algorithms for feature recognition are explored, facilitating image cropping and accurate atom number calculations.

bose einstein condensate↗
NASA NTRS1992

Resource constrained design of artificial neural networks using comparator neural network

We present a systematic design method executed under resource constraints for automating the design of artificial neural networks using the back error propagation algorithm. Our system aims at finding the best possible configuration for solving the given application with proper tradeoff between the training time and the network complexity. The design of such a system is hampered by three related problems. First, there are infinitely many possible network configurations, each may take an exceedingly long time to train; hence, it is impossible to enumerate and train all of them to completion within fixed time, space, and resource constraints. Second, expert knowledge on predicting good network configurations is heuristic in nature and is application dependent, rendering it difficult to characterize fully in the design process. A learning procedure that refines this knowledge based on examples on training neural networks for various applications is, therefore, essential. Third, the objective of the network to be designed is ill-defined, as it is based on a subjective tradeoff between the training time and the network cost. A design process that proposes alternate configurations under different cost-performance tradeoff is important. We have developed a Design System which schedules the available time, divided into quanta, for testing alternative network configurations. Its goal is to select/generate and test alternative network configurations in each quantum, and find the best network when time is expended. Since time is limited, a dynamic schedule that determines the network configuration to be tested in each quantum is developed. The schedule is based on relative comparison of predicted training times of alternative network configurations using comparator network paradigm. The comparator network has been trained to compare training times for a large variety of traces of TSSE-versus-time collected during back-propagation learning of various applications.

Wah, Benjamin W.↗
NASA NTRS2012

NASA Tech Briefs, July 2012

Topics covered include: Instrument Suite for Vertical Characterization of the Ionosphere-Thermosphere System; Terahertz Radiation Heterodyne Detector Using Two-Dimensional Electron Gas in a GaN Heterostructure; Pattern Recognition Algorithm for High-Sensitivity Odorant Detection in Unknown Environments; Determining Performance Acceptability of Electrochemical Oxygen Sensors; Versatile Controller for Infrared Lamp and Heater Arrays; High-Speed Scanning Interferometer Using CMOS Image Sensor and FPGA Based on Multifrequency Phase-Tracking Detection; Ultra-Low-Power MEMS Selective Gas Sensors; Compact Receiver Front Ends for Submillimeter-Wave Applications; Dynamically Reconfigurable Systolic Array Accelerator; Blocking Losses With a Photon Counter; Motion-Capture-Enabled Software for Gestural Control of 3D Mod; Orbit Software Suite; CoNNeCT Baseband Processor Module Boot Code SoftWare (BCSW); Trajectory Software With Upper Atmosphere Model; ALSSAT Version 6.0; Employing a Grinding Technology to Assess the Microbial Density for Encapsulated Organisms; Demonstration of Minimally Machined Honeycomb Silicon Carbide Mirrors; Polyimide Aerogel Thin Films; Nanoengineered Thermal Materials Based on Carbon Nanotube Array Composites; Composite Laminate With Coefficient of Thermal Expansion Matching D263 Glass; Robust Tensioned Kevlar Suspension Design; Focal Plane Alignment Utilizing Optical CMM; Purifying, Separating, and Concentrating Cells From a Sample Low in Biomass; Virtual Ultrasound Guidance for Inexperienced Operators; Beat-to-Beat Blood Pressure Monitor; Non-Contact Conductivity Measurement for Automated Sample Processing Systems; An MSK Radar Waveform; Telescope Alignment From Sparsely Sampled Wavefront Measurements Over Pupil Subapertures; Method to Remove Particulate Matter from Dusty Gases at Low Pressures; Terahertz Quantum Cascade Laser With Efficient Coupling and Beam Profile; Measurement Via Optical Near-Nulling and Subaperture Stitching; 885-nm Pumped Ceramic Nd:YAG Master Oscillator Power Amplifier Laser System; Airborne Hyperspectral Imaging System; Heat Shield Employing Cured Thermal Protection Material Blocks Bonded in a Large-Cell Honeycomb Matrix; and Asymmetric Supercapacitor for Long-Duration Power Storage.

Source record↗
NASA NTRS2024

Accurate Infrared Line Lists for 20 Isotopologues of CS 2 at Room Temperature

To facilitate atmospheric and spectroscopic studies of carbon disulfide, or CS 2 , in both planetary and exoplanetary atmospheres, we adopt the “Best Theory + Reliable High-resolution Experiment” algorithm to generate semiempirical IR line lists for the 20 most abundant CS 2 isotopologues, denoted as Ames-296K. The IR lists are computed using the Ames-1 potential energy surface, refined using the experimental transition set and an ab initio dipole moment surface fitted from CCSD(T)/aug-cc-pV(T/Q/5+d)Z dipoles extrapolated to a one-particle basis set limit. The IR lists cover the range of 0–10,000 cm -1 , with an S 296K cutoff at 10 -31 cm -1 /molecule·cm -2 (abundance included). A “natural” IR line list at 296 K includes about 10 million lines of the 20 isotopologues, with their intensities scaled by the corresponding abundances. The zero-point energy, partition functions, and abundances are reported for each isotopologue. The energy levels in the global effective Hamiltonian model for 12 C 32 S 2 are adopted to improve the line position accuracy. This new IR list for the main isotopologue is denoted as A+I.296K. Reliable HITRAN2020 line positions are also utilized to improve the accuracy of the 32 S 12 C 34 S, 32 S 12 C 33 S, and 32 S 13 C 32 S isotopologue line lists. The final composite line list is validated against Pacific Northwest National Laboratory experimental cross sections, showing excellent agreement. The agreement supports the quality of the composite line list and the power of synergy between experiment and theory. The new data are proposed for use in updating and expanding the CS 2 data in HITRAN and other high-resolution IR databases. Supplementary files are available in Zenodo and AHED.

Exoplanet atmospheric composition↗