Radiation Hardened Intelligent Readout ASIC for Nuclear Physics Experiments
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73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
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Results for “data compression”
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Frameworks, Algorithms, and Scalable Technologies for Mathematics (FASTMath) SciDAC Institute
As computational models scale to larger computers, the rate at which they produce data has far outstripped the same computers ability to write that data and further the file systems ability to store that data. Almost all of the SciDAC applications, but especially those related to fusion solve very large scale PDEs whose scientific output his impacted by this problem. To gain access to dynamics in an exascale simulation that are not identifiable a priori and to make that dynamical data available to machine learning requires fundamental research in the area of in situ data data analytics. Here data analytics includes compression, visualization, uncertainty quantification, and machine learning. This in situ data analytics will enable on-the-fly spatial and temporal compression of solution dynamics, expose that space-time compressed field to machine learning algorithms that have been specialized to work with dynamically evolving data (existing machine learning algorithms treat data sets as static), greatly improving the opportunity for machine learning to provide feedback to the compression, all within an ongoing simulation, without the need to write data to files. The same concepts are also being applied to uncertainty quantification and multi-fidelity modeling which have similar needs for spatial and temporal compression of the ongoing exascale simulation to perform either without the typical, unacceptable writing of data to files.
97 MATHEMATICS AND COMPUTING↗
Ramp compression of tantalum to multiterapascal pressures: Constraints of the thermal equation of state to 2.3 TPa and 5000 K
We report measurements of the compressibility of ramp compressed tantalum to a final stress of 2.3 TPa corresponding to threefold volumetric compression. Using these data, we extended the experimental constraint on the Ta cold compression curve by an order of magnitude in pressure. Furthermore, by combining the resulting data with previous measurements of shock compression and ambient pressure heating, we construct an experimentally bounded and thermodynamically consistent equation of state model for Ta which has 2% uncertainty in pressure at 1 TPa. We therefore propose Ta as an in situ pressure scale for laser-heated static compression experiments which were recently able to reach terapascal pressures and thousands of degrees Kelvin. Our new equation of state of Ta is experimentally constrained at extreme pressures and temperatures relevant to a wide range of planetary interiors and will allow for more accurate comparison between experimental measurements and theory at extreme conditions.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Elastic-plastic and phase transition of zinc oxide single crystal under shock compression
The Hugoniot data for zinc oxide (ZnO) single crystals were measured up to 80 GPa along both the 〈112{sup ¯}0〉 (a-axis) and 〈0001〉 (c-axis) directions using a velocity interferometer system for any reflector and inclined-mirror method combined with a powder gun and two-stage light gas gun. The Hugoniot-elastic limits of ZnO were determined to be 10.5 and 11.5 GPa along the a- and c-axes, respectively. The wurtzite (B4) to rocksalt (B1) phase transition pressures along the a- and c-axes are 12.3 and 14.4 GPa, respectively. Shock velocity (U{sub s}) versus particle velocity (U{sub p}) relation of the final phase is given by the following relationship: U{sub s} (km/s) = 2.76 + 1.51U{sub p} (km/s). Based on the Debye-Grüneisen model and Birch-Murnaghan equation of state (EOS), we discuss the EOS of the B1 phase ZnO. The bulk modulus (K{sub 0}) and its pressure derivative (K{sub 0}′) are estimated to be K{sub 0} = 174 GPa and K{sub 0}′ = 3.9, respectively.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Study of Stark Broadening of Krypton Helium-beta Lines and Estimation of Electron Density and Temperature in NIF Compressed Capsules
Numerical data used to draw the figures in the manuscript
NIF diagnostics x-ray spectroscopy↗
Assimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation system
This study introduces the Weather Research and Forecasting Model with chemistry/Data Assimilation Research Testbed (WRF-Chem/DART) chemical transport forecasting/data assimilation system together with the assimilation of ''compact phase space retrievals'' of satellite-derived atmospheric composition products. WRF-Chem is a state-of-the-art chemical transport model. DART is a flexible software environment for researching ensemble data assimilation with different assimilation and forecast model options. DART's primary assimilation tool is the ensemble adjustment Kalman filter. WRF-Chem/DART is applied to the assimilation of Terra/Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) trace gas retrieval profiles. Those CO observations are first assimilated as quasi-optimal retrievals (QORs). Our results show that assimilation of the CO retrievals (i) reduced WRF-Chem's CO bias in retrieval and state space, and (ii) improved the CO forecast skill by reducing the Root Mean Square Error (RMSE) and increasing the Coefficient of Determination ($R^2$). Those CO forecast improvements were significant at the 95 % level. Trace gas retrieval data sets contain (i) large amounts of data with limited information content per observation, (ii) error covariance cross-correlations, and (iii) contributions from the retrieval prior profile that should be removed before assimilation. Those characteristics present challenges to the assimilation of retrievals. This paper addresses those challenges by introducing the assimilation of compact phase space retrievals (CPSRs). CPSRs are obtained by preprocessing retrieval data sets with an algorithm that (i) compresses the retrieval data, (ii) diagonalizes the error covariance, and (iii) removes the retrieval prior profile contribution. Most modern ensemble assimilation algorithms can efficiently assimilate CPSRs. Our results show that assimilation of MOPITT CO CPSRs reduced the number of observations (and assimilation computation costs) by ~ 35 %, while providing CO forecast improvements comparable to or better than with the assimilation of MOPITT CO QORs.
58 GEOSCIENCES↗
1.2 Mfps standalone X-ray detector for Time-Resolved Experiments
We present a standalone and autonomous X-ray detector capable of operation with the speed of up to1.2Mfps. The detector utilizes UFXC32k hybrid pixel detectors for sensing X-rays, Spartan-6 LX45 FPGA placed in commercially available sbRIO 9628 controller for data acquisition and processing including a compression with zero-suppression algorithm. A Linux-RT system working on the 400 MHz Dual-Core CPU is used for FPGA control and data streaming to the higher-level system over 1 Gbps Ethernet connection. 1.2 M frames per second is achieved in so-called burst mode of operation while in zerodead-time mode 70 kfps is possible. Due to efficient data compression in FPGA there’s no need of using high-speed transceivers and Frame-Grabber cards on the data server side and the detector can stream the data infinitely over standard 1 Gbps network connection. Operation modes were tested at Advanced Photon Source Synchrotron at Argonne National Laboratory.
47 OTHER INSTRUMENTATION↗
High-Pressure Structural Response of an Insensitive Energetic Crystal: Dihydroxylammonium 5,5'-Bistetrazole-1,1'-diolate (TKX-50)
The structural response of a novel, insensitive energetic crystal—dihydroxylammonium 5,5'-bistetrazole-1,1'-diolate (TKX-50)—was examined under high pressure. Using synchrotron single-crystal X-ray diffraction measurements, details of molecular, intermolecular, and crystal changes were determined to ~10 GPa to understand its structural stability. The experimental results showed that TKX-50 exhibits highly anisotropic compression and significantly lower volume compressibility than currently known energetic crystals. These results are found to be in general agreement with our previous predictions from the DFT calculations. Additionally, the experimental data revealed anomalous compression—an expansion of the unit cell along the a axis (negative linear compressibility, NLC) upon compression to ~3 GPa. The structural analyses demonstrated that this unusual effect, the first such observation in an energetic crystal, is a consequence of the highly anisotropic response of 3D motifs, comprised of two parallel anions [(C 2 N 8 O 2 ) 2– ] linked with two cations [(NH 3 OH) + ] through four strong hydrogen bonds. The present results demonstrate that the structural stability of TKX-50 is controlled by the strong and highly anisotropic intermolecular interactions, and these may contribute to its shock insensitivity.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
ZFP: A compressed array representation for numerical computations
HPC trends favor algorithms and implementations that reduce data motion relative to FLOPS. We investigate the use of lossy compressed data arrays in place of traditional IEEE floating point arrays to store the primary data of calculations. Simulation is fundamentally an exercise in controlled approximation, and error introduced by finite-precision arithmetic (or lossy compression) is just one of several sources of error that need to be managed to ensure sufficient accuracy in a computed result. We describe ZFP, a compressed numerical format designed for in-memory storage of multidimensional arrays, and summarize theoretical results that demonstrate that the error of repeated lossy compression can be bounded and controlled. Furthermore, we establish a relationship between grid resolution and compression-induced errors and show that, contrary to conventional floating point, ZFP reduces finite-difference errors with finer grids. We present example calculations that demonstrate data reduction by 4x or more with negligible impact on solution accuracy. Our results further demonstrate several orders-of-magnitude increase in accuracy using ZFP over IEEE floating point and Posits for the same storage budget.
Lindstrom, Peter↗
Integrated photonic encoder for low power and high-speed image processing
Abstract Modern lens designs are capable of resolving greater than 10 gigapixels, while advances in camera frame-rate and hyperspectral imaging have made data acquisition rates of Terapixel/second a real possibility. The main bottlenecks preventing such high data-rate systems are power consumption and data storage. In this work, we show that analog photonic encoders could address this challenge, enabling high-speed image compression using orders-of-magnitude lower power than digital electronics. Our approach relies on a silicon-photonics front-end to compress raw image data, foregoing energy-intensive image conditioning and reducing data storage requirements. The compression scheme uses a passive disordered photonic structure to perform kernel-type random projections of the raw image data with minimal power consumption and low latency. A back-end neural network can then reconstruct the original images with structural similarity exceeding 90%. This scheme has the potential to process data streams exceeding Terapixel/second using less than 100 fJ/pixel, providing a path to ultra-high-resolution data and image acquisition systems.
47 OTHER INSTRUMENTATION↗
A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data
The move toward exascale computing for scientific simulations is placing new demands on compression techniques. It is expected that the I/O system will not be able to support the volume of data that is expected to be written out. To enable quantitative analysis and scientific discovery, we are interested in techniques that compress high-dimensional simulation data and can provide perfect or near-perfect reconstruction. In this paper, we explore the use of compressed sensing (CS) techniques to reduce the size of the data before they are written out. Using large-scale simulation data, we investigate how the sufficient sparsity condition and the contrast in the data affect the quality of reconstruction and the degree of compression. Also, we provide suggestions for the practical implementation of CS techniques and compare them with other sparse recovery methods. Finally, our results show that despite longer times for reconstruction, compressed sensing techniques can provide near perfect reconstruction over a range of data with varying sparsity.
97 MATHEMATICS AND COMPUTING↗
Deep compressed seismic learning for fast location and moment tensor inferences with natural and induced seismicity
Fast detection and characterization of seismic sources is crucial for decision-making and warning systems that monitor natural and induced seismicity. However, besides the laying out of ever denser monitoring networks of seismic instruments, the incorporation of new sensor technologies such as Distributed Acoustic Sensing (DAS) further challenges our processing capabilities to deliver short turnaround answers from seismic monitoring. In response, this work describes a methodology for the learning of the seismological parameters: location and moment tensor from compressed seismic records. In this method, data dimensionality is reduced by applying a general encoding protocol derived from the principles of compressive sensing. The data in compressed form is then fed directly to a convolutional neural network that outputs fast predictions of the seismic source parameters. Thus, the proposed methodology can not only expedite data transmission from the field to the processing center, but also remove the decompression overhead that would be required for the application of traditional processing methods. An autoencoder is also explored as an equivalent alternative to perform the same job. We observe that the CS-based compression requires only a fraction of the computing power, time, data and expertise required to design and train an autoencoder to perform the same task. Implementation of the CS-method with a continuous flow of data together with generalization of the principles to other applications such as classification are also discussed.
54 ENVIRONMENTAL SCIENCES↗
Big Data and Deep data in scanning and electron microscopies: functionality from multidimensional data sets
The development of electron, and scanning probe microscopies in the second half of the twentieth century have produced spectacular images of internal structure and composition of matter with, at nanometer, molecular, and atomic resolution. Largely, this progress was enabled by computer-assisted methods of microscope operation, data acquisition and analysis. The progress in imaging technologies in the beginning of the twenty first century has opened the proverbial floodgates of high-veracity information on structure and functionality. High resolution imaging now allows information on atomic positions with picometer precision, allowing for quantitative measurements of individual bond length and angles. Functional imaging often leads to multidimensional data sets containing partial or full information on properties of interest, acquired as a function of multiple parameters (time, temperature, or other external stimuli). Here, we review several recent applications of the big and deep data analysis methods to visualize, compress, and translate this data into physically and chemically relevant information from imaging data.
36 MATERIALS SCIENCE↗
Lifting MGARD: Construction of (pre)wavelets on the interval using polynomial predictors of arbitrary order
MGARD (MultiGrid Adaptive Reduction of Data) is an algorithm for compressing and refactoring scientific data, based on the theory of multigrid methods. The core algorithm is built around stable multilevel decompositions of conforming piecewise linear $C^0$ finite element spaces, enabling accurate error control in various norms and derived quantities of interest. In this work, we extend this construction to arbitrary order Lagrange finite elements $\mathbb{Q}_p$, $p \geq 0$, and propose a reformulation of the algorithm as a lifting scheme with polynomial predictors of arbitrary order. Additionally, a new formulation using a compactly supported wavelet basis is discussed, and an explicit construction of the proposed wavelet transform for uniform dyadic grids is described.
Reshniak, Viktor [Oak Ridge National Laboratory (O↗
Byte select cache compression
Techniques are disclosed for designing cache compression algorithms that control how data in caches are compressed. The techniques generate a custom “byte select algorithm” by applying repeated transforms applied to an initial compression algorithm until a set of suitability criteria is met. The suitability criteria include that the “cost” is below a threshold and that a metadata constraint is met. The “cost” is the number of blocks that can be compressed by an algorithm as compared with the “ideal” algorithm. The metadata constraint is the number of bits required for metadata.
Das, Shomit N.↗
Identifying and discriminating phase transitions along decaying shocks with line imaging Doppler interferometric velocimetry and streaked optical pyrometry [Identify phase transition along decaying shocks with line imaging VISAR and streaked optical pyrometry]
Ultrafast line-imaging velocity interferometer system for any reflector and streaked optical pyrometry are now commonly used to obtain high precision equation of state and electronic transport data under dynamic compression at major high energy density science facilities. In this work, we describe a simple way to improve distinguishing phase transformation signatures from other signals when monitoring decaying shock waves. The line-imaging capability of these optical diagnostics offers additional supporting evidence to the assignment of particular anomalies—such as plateaus or reversals—to the occurrence of a phase transition along the Hugoniot. Finally, we illustrate the discussion with two example datasets collected during laser driven shock compression of quartz and stishovite.
47 OTHER INSTRUMENTATION↗
Compressed Natural Gas Component Leak Frequency Estimation
The frequency of unintended releases in a compressed natural gas system is an important aspect of the system quantitative risk assessment. The frequencies for possible release scenarios, along with engineering models, are utilized to quantify the risks for compressed natural gas facilities. This report documents component leakage frequencies representative of compressed natural gas components that were estimated as a function of the normalized leak size. A Bayesian statistical method was used which results in leak frequency distributions for each component which represent variation and uncertainty in the leak frequency. The analysis shows that there is high uncertainty in the estimated leak frequencies due to sparsity in compressed natural gas data. These leak frequencies may still be useful in compressed natural gas system risk assessments, as long as this high uncertainty is acknowledged and considered appropriately.
03 NATURAL GAS↗