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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.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Diagnosis of Factors Impacting Yield in Multilayer Devices for Superconducting Electronics

The ability to localize defects in order to understand failure mechanisms in complex superconducting electronics circuits, while operating at low temperature, does not yet exist. Here, this work applies thermally-induced voltage alteration (TIVA), to a biased superconducting electronics (SCE) circuit at ambient temperature. TIVA is a commonly used, laser-based failure analysis technique developed for silicon-based microelectronics. The non-operational circuit consisted of an arithmetic logic unit (ALU) in a high-frequency test bed designed at HYPRES and fabricated by MIT Lincoln Laboratory using their SFQ5ee process. Localized TIVA signals were correlated with reflected light images at the surface, and these sites were further investigated by scanning electron microscopy imaging of focused ion-beam cross-sections. The areas investigated, where prominent TIVA signals were observed, showed seams in the Nb wiring layers at contacts to Josephson junctions or inductors and/or disrupted junction morphologies. Finally, these results suggest that the TIVA technique can be used at ambient temperature to diagnose fabrication defects that may cause low temperature circuit failure.

36 MATERIALS SCIENCE↗
DOE OSTI2019

TTHRESH: Tensor Compression for Multidimensional Visual Data

Memory and network bandwidth are decisive bottlenecks when handling high-resolution multidimensional data sets in visualization applications, and they increasingly demand suitable data compression strategies. In this paper, we introduce a novel lossy compression algorithm for multidimensional data over regular grids. It leverages the higher-order singular value decomposition (HOSVD), a generalization of the SVD to three dimensions and higher, together with bit-plane, run-length and arithmetic coding to compress the HOSVD transform coefficients. Our scheme degrades the data particularly smoothly and achieves lower mean squared error than other state-of-the-art algorithms at low-to-medium bit rates, as it is required in data archiving and management for visualization purposes. Further advantages of the proposed algorithm include very fine bit rate selection granularity and the ability to manipulate data at very small cost in the compression domain, for example to reconstruct filtered and/or subsampled versions of all (or selected parts) of the data set.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Physiological coherence in healthy volunteers during laboratory‐induced stress and controlled breathing

Abstract Physiological coherence has been related with a general sense of well‐being and improvements in health and physical, social, and cognitive performance. The aim of this study was to evaluate the relationship between acute stress, controlled breathing, and physiological coherence, and the degree of body systems synchronization during a coherence‐generation exercise. Thirty‐four university employees were evaluated during a 20‐min test consisting of four stages of 5‐min duration each, during which basal measurements were obtained (Stage 1), acute stress was induced using validated mental stressors (Stroop test and mental arithmetic task, during Stage 2 and 3, respectively), and coherence states were generated using a controlled breathing technique (Stage 4). Physiological coherence and cardiorespiratory synchronization were assessed during each stage from heart rate variability, pulse transit time, and respiration. Coherence measurements derived from the three analyzed variables increased during controlled respiration. Moreover, signals synchronized during the controlled breathing stage, implying a cardiorespiratory synchronization was achieved by most participants. Hence, physiological coherence and cardiopulmonary synchronization, which could lead to improvements in health and better life quality, can be achieved using slow, controlled breathing exercises. Meanwhile, coherence measured during basal state and stressful situations did not show relevant differences using heart rate variability and pulse transit time. More studies are needed to evaluate the ability of coherence ratio to reflect acute stress.

Mejía‐Mejía, Elisa↗
DOE OSTI2014

Sci—Fri AM: Mountain — 03: Current status of the NRC primary standard for {sup 192}Ir HDR brachytherapy sources

The Canadian primary standard for {sup 192}Ir HDR brachytherapy sources has been recently revised in a more accurate manner allowing for more realistic uncertainty estimation. Air-kerma strength S{sub k} is derived from measurements of the source's output using a graphite-walled spherical ionization chamber (2S) at several distances. Traceability to NRC primary standards for the {sup 192}Ir calibration coefficient N{sub k} is insured by estimating it as the inverse arithmetic mean of the inverse of the calibration coefficients for a {sup 137}Cs beam and the medium energy x-ray beam quality N250, both of which are traceable to NRC primary standards. The multiple-distance method is combined with a non-linear least squares fit to determine St, while at the same time removing the effects of room scatter and position offset. The previously used shadow-cone method for directly measuring the room scatter is found to be inadequate due to the increased scatter contribution from the lead cone itself, especially at short source-detector distances. Rather than including the reported 1% difference in source strength for {sup 192}Ir HDR sources of different construction into the total uncertainty, users are cautioned that the calibration coefficient provided by NRC is only valid for a microSelectron V2 model. A comprehensive uncertainty budged shows that the total one sigma uncertainty of the standard is actually 0.6% rather than the previously assumed 1.2%. NRC measured S{sub k} agrees within 0.03% of the manufacturer's value.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

SU-D-BRD-05: Online Framework for Plan Tracking and Automatic Checks in Radiation Therapy

Purpose: To develop an online framework that tracks a patient’s plan from initial simulation to treatment and that helps automate elements of the physics plan checks usually performed in the record and verify (RV) system and treatment planning system. Methods: We have developed PlanTracker, an online plan tracking system that automatically imports new patients tasks and follows it through treatment planning, physics checks, therapy check, and chart rounds. A survey was designed to collect information about the amount of time spent by medical physicists in non-physics related tasks. We then assessed these non-physics tasks for automation. Using these surveys, we directed our PlanTracker software development towards the automation of intra-plan physics review. We then conducted a systematic evaluation of PlanTracker’s accuracy by generating test plans in the RV system software designed to mimic real plans, in order to test its efficacy in catching errors both real and theoretical. Results: PlanTracker has proven to be an effective improvement to the clinical workflow in a radiotherapy clinic. We present data indicating that roughly 1/3 of the physics plan check can be automated, and the workflow optimized, and show the functionality of PlanTracker. When the full system is in clinical use we will present data on improvement of time use in comparison to survey data prior to PlanTracker implementation. Conclusion: We have developed a framework for plan tracking and automatic checks in radiation therapy. We anticipate using PlanTracker as a basis for further development in clinical/research software. We hope that by eliminating the most simple and time consuming checks, medical physicists may be able to spend their time on plan quality and other physics tasks rather than in arithmetic and logic checks. We see this development as part of a broader initiative to advance the clinical/research informatics infrastructure surrounding the radiotherapy clinic. This research project has been financially supported by Varian Medical Systems, Palo Alto, CA, through a Varian MRA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-277: Using Dose Weighted Mean Effective Depth to Improve Accuracy of Secondary MU Calculation for VMAT Plans

Purpose: In commercial secondary dose calculation system, an average effective depth is used to calculate the Monitor Units for an arc beam from the volumetric modulated arc (VMAT) plans. Typically, an arithmetic mean of the effective depths (AMED) of a VMAT arc beam is used, which may result in large MU discrepancy from that of the primary treatment planning system. This study is to demonstrate the use of a dose weighted mean effective depth (DWED) can improve accuracy of MU calculation for the secondary MU verification. Methods: In-house scripts were written in the primary treatment planning system (TPS) to first convert a VMAT arc beam to a series of static step & shoot beams (every 4 degree). The computed dose and effective depth of each static beam were then used to obtain the dose weighted mean effective depth (DWED) for the VMAT beam. The DWED was used for the secondary MU calculation for VMAT plans. Six lung SBRT VMAT plans, eight head and neck VMAT plans and ten prostate VMAT plans that had > 5% MU deviations (failed MU verification) using the AMED method were recalculated with the DWED. For comparison, same number VMAT plans that had < 5% MU deviations (passed MU verification) using AMED method were also reevaluated with the dose weighted mean effective depth method. Results: For MU verification passed plans, the mean and standard deviation of MU differences between the TPS and the secondary calculation program were 2.2%±1.5% for the AMED and 2.1%±1.7% for the DMED method. For the failed plans, the mean and standard deviation of MU differences of TPS to the secondary calculation program were 9.9%±4.7% and 4.7%±2.6, respectively. Conclusion: The dose weighted mean effective depth improved MU calculation accuracy which can be used for the pre-treatment MU verification of VMAT plans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

A Novel Approach to Realize of All Optical Frequency Encoded Dibit Based XOR and XNOR Logic Gates Using Optical Switches with Simulated Verification

Since last few decades optics has already proved its strong potentiality for conducting parallel logic, arithmetic and algebraic operations due to its super-fast speed in communication and computation. So many different logical and sequential operations using all optical frequency encoding technique have been proposed by several authors. Here, we have keened out all optical dibit representation technique, which has the advantages of high speed operation as well as reducing the bit error problem. Exploiting this phenomenon, we have proposed all optical frequency encoded dibit based XOR and XNOR logic gates using the optical switches like add/drop multiplexer (ADM) and reflected semiconductor optical amplifier (RSOA). Also the operations of these gates have been verified through proper simulation using MATLAB (R2008a).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Highly scalable distributed-memory sparse triangular solution algorithms.

This paper presents a highly efficient distributed-memory parallel sparse triangular solver. The triangular solution phase is often performed following factorization phase in the sparse linear solvers and has become increasingly computa­tionally expensive for direct solvers with many right hand sides (RHSs) or preconditioned iterative solvers. However, the low arithmetic intensity and sequential nature of the triangular solve algorithm pose performance challenges for its large-scale distributed-memory parallelization. In this work, we propose several strategies to enhance scalability of an algorithm with 2D block cyclic process layout. First, an asynchronous binary-tree-based communication scheme implemented via non-blocking MPI functions is leveraged to broadcast partial solutions and reduce partial updates among a subset of processes for each block column and row of the triangular matrix, respectively. This scheme reduces message latency, improves communication load balance and significantly accelerates asynchronous execution of the triangular solve. In addition, efficient BLAS operations and threading implementations are exploited to accelerate local computations and further reduce process idle time. The proposed strategies are implemented in SuperLU_DIST and numerical experiments show up to 4.4x improvement with one right-hand side and up to 6.1x improvement with 50 right- hand sides on 4096 processes, compared to the current release. This is the first time that sparse triangular solution is demonstrated strong scaling on more than 4000 cores.

Liu, Yang↗
DOE OSTI2019

Scalable Matrix-Free Adaptive Product-Convolution Approximation for Locally Translation-Invariant Operators

We present an adaptive grid matrix-free operator approximation scheme based on a "product-convolution" interpolation of convolution operators. This scheme is appropriate for operators that are locally translation-invariant, even if these operators are high rank or full rank. Such operators arise in Schur complement methods for solving partial differential equations (PDEs), as Hessians in PDE-constrained optimization and inverse problems, as integral operators, as covariance operators, and as Dirichlet-to-Neumann maps. Constructing the approximation requires computing the impulse responses of the operator to point sources centered on nodes in an adaptively refined grid of sample points. A randomized a posteriori error estimator drives the adaptivity. Once constructed, the approximation can be efficiently applied to vectors using the fast Fourier transform. The approximation can be efficiently converted to hierarchical matrix (H-matrix) format, then inverted or factorized using scalable H-matrix arithmetic. The quality of the approximation degrades gracefully as fewer sample points are used, allowing cheap lower quality approximations to be used as preconditioners. Furthermore, this yields an automated method to construct preconditioners for locally translation-invariant Schur complements. We directly address issues related to boundaries and prove that our scheme eliminates boundary artifacts. We test the scheme on a spatially varying blurring kernel, on the nonlocal component of an interface Schur complement for the Poisson operator, and on the data misfit Hessian for an advection dominated advection-diffusion inverse problem. Numerical results show that the scheme outperforms existing methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

New-Sum: A Novel Online ABFT Scheme For General Iterative Methods

Emerging high-performance computing platforms, with large component counts and lower power margins, are anticipated to be more susceptible to soft errors in both logic circuits and memory subsystems. We present an online algorithm-based fault tolerance (ABFT) approach to efficiently detect and recover soft errors for general iterative methods. We design a novel checksum-based encoding scheme for matrix-vector multiplication that is resilient to both arithmetic and memory errors. Our design decouples the checksum updating process from the actual computation, and allows adaptive checksum overhead control. Building on this new encoding mechanism, we propose two online ABFT designs that can effectively recover from errors when combined with a checkpoint/rollback scheme.

Instantaneous Footprint↗
DOE OSTI2016

Delayed Update Algorithms for Quantum Monte Carlo Simulation on GPU: Extended Abstract

QMCPACK is open source scientific software designed to perform Quantum Monte Carlo (QMC) simulation, a first-principles method for describing many-body systems. The evaluation of each Monte Carlo move requires finding the determinant of a dense matrix in the wave functions. This calculation forms a key computational kernel in QMCPACK. After each accepted event, the wave function matrix undergoes a rank-one update to represent a single particle move within the system. The matrix inverse is updated via the Sherman-Morrison formula; occasionally, the explicit inverse must be recomputed to maintain numerical stability.; We propose an alternate approach to this kernel that utilizes QR factorization to maintain stability without refactorization. In addition, algorithms based on a novel delayed update scheme are explored in this effort. This strategy enables probability evaluation for multiple successive Monte Carlo moves, with application of accepted moves to the wave function matrix delayed until an event is denied, or a predetermined limit on acceptances p is reached. Accepted events grouped in this manner are then applied to the matrix en bloc with enhanced arithmetic intensity and computational efficiency. Delayed update Sherman-Morrison probability evaluation algorithms with CPU and GPU-accelerated implementations are tested, profiled, and analyzed. A QR based delayed update algorithm remains in development. Results are evaluated against existing methods for numerical stability and efficiency; emphasis is placed on large systems, for which acceleration is critical.

Computer Science↗
DOE OSTI2017

A Pattern Language for High-Performance Computing Resilience

High-performance computing systems (HPC) provide powerful capabilities for modeling, simulation, and data analytics for a broad class of computational problems. They enable extreme performance of the order of quadrillion floating-point arithmetic calculations per second by aggregating the power of millions of compute, memory, networking and storage components. With the rapidly growing scale and complexity of HPC systems for achieving even greater performance, ensuring their reliable operation in the face of system degradations and failures is a critical challenge. System fault events often lead the scientific applications to produce incorrect results, or may even cause their untimely termination. The sheer number of components in modern extreme-scale HPC systems and the complex interactions and dependencies among the hardware and software components, the applications, and the physical environment makes the design of practical solutions that support fault resilience a complex undertaking. To manage this complexity, we developed a methodology for designing HPC resilience solutions using design patterns. We codified the well-known techniques for handling faults, errors and failures that have been devised, applied and improved upon over the past three decades in the form of design patterns. In this paper, we present a pattern language to enable a structured approach to the development of HPC resilience solutions. The pattern language reveals the relations among the resilience patterns and provides the means to explore alternative techniques for handling a specific fault model that may have different efficiency and complexity characteristics. Using the pattern language enables the design and implementation of comprehensive resilience solutions as a set of interconnected resilience patterns that can be instantiated across layers of the system stack.

Hukerikar, Saurabh↗
DOE OSTI2020

Algorithms for Efficient Reproducible Floating Point Summation

We define “reproducibility” as getting bitwise identical results from multiple runs of the same program, perhaps with different hardware resources or other changes that should not affect the answer. Many users depend on reproducibility for debugging or correctness. However, dynamic scheduling of parallel computing resources, combined with nonassociative floating point addition, makes reproducibility challenging even for summation, or operations like the BLAS. We describe a “reproducible accumulator” data structure (the “binned number”) and associated algorithms to reproducibly sum binary floating point numbers, independent of summation order. We use a subset of the IEEE Floating Point Standard 754-2008 and bitwise operations on the standard representations in memory. Our approach requires only one read-only pass over the data, and one reduction in parallel, using a 6-word reproducible accumulator (more words can be used for higher accuracy), enabling standard tiling optimization techniques. Summing n words with a 6-word reproducible accumulator requires approximately 9 n floating point operations (arithmetic, comparison, and absolute value) and approximately 3 n bitwise operations. The final error bound with a 6-word reproducible accumulator and our default settings can be up to 2 29 times smaller than the error bound for conventional (recursive) summation on ill-conditioned double-precision inputs.

Computer Science↗
DOE OSTI2022

Factorization of Binary Matrices: Rank Relations, Uniqueness and Model Selection of Boolean Decomposition

The application of binary matrices are numerous. Representing a matrix as a mixture of a small collection of latent vectors via low-rank decomposition is often seen as an advantageous method to interpret and analyze data. In this work, we examine the factorizations of binary matrices using standard arithmetic (real and nonnegative) and logical operations (Boolean and $\mathbb{Z}$ 2 ). We examine the relationships between the different ranks, and discuss when factorization is unique. In particular, we characterize when a Boolean factorization X = W$\land$H has a unique W, a unique H (for a fixed W), and when both W and H are unique, given a rank constraint. We introduce a method for robust Boolean model selection, called BMFk, and show on numerical examples that BMFk not only accurately determines the correct number of Boolean latent features but reconstruct the pre-determined factors accurately.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

QASMBench: A Low-Level Quantum Benchmark Suite for NISQ Evaluation and Simulation

The rapid development of quantum computing (QC) in the NISQ era urgently demands a low-level benchmark suite and insightful evaluation metrics for characterizing the properties of prototype NISQ devices, the efficiency of QC programming compilers, schedulers and assemblers, and the capability of quantum system simulators in a classical computer. In this work, we fill this gap by proposing a low-level, easy-to-use benchmark suite called QASMBench based on the OpenQASM assembly representation. It consolidates commonly used quantum routines and kernels from a variety of domains including chemistry, simulation, linear algebra, searching, optimization, arithmetic, machine learning, fault tolerance, cryptography, and so on, trading-off between generality and usability. To analyze these kernels in terms of NISQ device execution, in addition to circuit width and depth, we propose four circuit metrics including gate density, retention lifespan, measurement density, and entanglement variance, to extract more insights about the execution efficiency, the susceptibility to NISQ error, and the potential gain from machine-specific optimizations. Applications in QASMBench can be launched and verified on several NISQ platforms, including IBM-Q, Rigetti, IonQ and Quantinuum. For evaluation, we measure the execution fidelity of a subset of QASMBench applications on 12 IBM-Q machines through density matrix state tomography, comprising 25K circuit evaluations. In addition we also compare the fidelity of executions among the IBM-Q machines, the IonQ QPU and the Rigetti Aspen M-1 system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Mixed-Precision S/DGEMM Using the TF32 and TF64 Frameworks on Low-Precision AI Tensor Cores

Using NVIDIA graphics processing units (GPUs) equipped with Tensor Cores has enabled the significant acceleration of general matrix multiplication (GEMM) for applications in machine learning (ML) and artificial intelligence (AI) and in high-performance computing (HPC) generally. The use of such power-efficient, specialized accelerators can provide a performance increase between 8 × and 20 ×, albeit with a loss in precision. However, a high level of precision is required in many large scientific and HPC applications, and computing in single or double precision is still necessary for many of these applications to maintain accuracy. Fortunately, mixed-precision methods can be employed to maintain a higher level of numerical precision while also taking advantage of the performance increases from computing with lower-precision AI cores. With this in mind, we extend the state of the art by using NVIDIA’s new TF32 framework. This new framework not only burdens some constraints of the previous frameworks, such as costly 32 16-bit castings but also provides an equivalent precision and performance by using a much simpler approach. We also propose a new framework called TF64 that attempts double-precision arithmetic with low-precision Tensor Cores. Although this framework does not exist yet, we validated the correctness of this idea and achieved an equivalent of 64-bit precision on 32-bit hardware.

Valero Lara, Pedro↗
DOE OSTI2019

STEMDL

The code "STEMDL" provides highly-optimized multi-gpu implementations of DL models of applicability in electron microscopy via the use of mixed-precision arithmetic, native graph construction and evaluation in Tensorflow, and adaptive scaling of learning parameters to avoid numerical instabilities due to reduced precision. STEMDL also uses low-level methods from the Horovod library for gradient communication and reduction and adds an additional optimization by allowing the user to scheduling the gradient reduction operations based on their position within the layers of a neural net. STEMDL provides optimized I/O routines for data staging and data reading/writing in both Tensorflow and Pytorch using multi-processing and asynchronous transfer between host and device. .

Laanait, Nouamane↗