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Generating graphics to faithfully represent information can be a computationally intensive task. A way of using the Massively Parallel Processor to generate images by ray tracing is presented. This technique uses sort computation, a method of performing generalized routing interspersed with computation on a single-instruction-multiple-data (SIMD) computer.
A report discusses the technical background and design of the Navigator Global Positioning System (GPS) receiver -- . a radiation-hardened receiver intended for use aboard spacecraft. Navigator is capable of weak signal acquisition and tracking as well as much faster acquisition of strong or weak signals with no a priori knowledge or external aiding. Weak-signal acquisition and tracking enables GPS use in high Earth orbits (HEO), and fast acquisition allows for the receiver to remain without power until needed in any orbit. Signal acquisition and signal tracking are, respectively, the processes of finding and demodulating a signal. Acquisition is the more computationally difficult process. Previous GPS receivers employ the method of sequentially searching the two-dimensional signal parameter space (code phase and Doppler). Navigator exploits properties of the Fourier transform in a massively parallel search for the GPS signal. This method results in far faster acquisition times [in the lab, 12 GPS satellites have been acquired with no a priori knowledge in a Low-Earth-Orbit (LEO) scenario in less than one second]. Modeling has shown that Navigator will be capable of acquiring signals down to 25 dB-Hz, appropriate for HEO missions. Navigator is built using the radiation-hardened ColdFire microprocessor and housing the most computationally intense functions in dedicated field-programmable gate arrays. The high performance of the algorithm and of the receiver as a whole are made possible by optimizing computational efficiency and carefully weighing tradeoffs among the sampling rate, data format, and data-path bit width.
An easy-to-use, intuitive computer program was written to satisfy a need of test operators and data requestors to quickly view and manipulate high-frequency test data recorded at the East and West Test Areas at Marshall Space Flight Center. By enabling rapid analysis, this program makes it possible to reduce times between test runs, thereby potentially reducing the overall cost of test operations. The program can be used to perform quick frequency analysis, using multiple fast- Fourier-transform windowing and amplitude options. The program can generate amplitude-versus-time plots with full zoom capabilities, frequency-component plots at specified time intervals, and waterfall plots (plots of spectral intensity versus frequency at successive small time intervals, showing the changing frequency components over time). There are options for printing of the plots and saving plot data as text files that can be imported into other application programs. The program can perform all of the aforementioned plotting and plot-data-handling functions on a relatively inexpensive computer; other software that performs the same functions requires computers with large amounts of power and memory.
In this paper we report on a search for short-duration gravitational wave bursts in the frequency range 64 Hz-1792 Hz associated with gamma-ray bursts (GRBs), using data from GEO600 and one of the LIGO or Virgo detectors. We introduce the method of a linear search grid to analyze GRB events with large sky localization uncertainties such as the localizations provided by the Fermi Gamma-ray Burst Monitor (GBM). Coherent searches for gravitational waves (GWs) can be computationally intensive when the GRB sky position is not well-localized, due to the corrections required for the difference in arrival time between detectors. Using a linear search grid we are able to reduce the computational cost of the analysis by a factor of O(10) for GBM events. Furthermore, we demonstrate that our analysis pipeline can improve upon the sky localization of GRBs detected by the GBM, if a high-frequency GW signal is observed in coincidence. We use the linear search grid method in a search for GWs associated with 129 GRBs observed satellite-based gamma-ray experiments between 2006 and 2011. The GRBs in our sample had not been previously analyzed for GW counterparts. A fraction of our GRB events are analyzed using data from GEO600 while the detector was using squeezed-light states to improve its sensitivity; this is the first search for GWs using data from a squeezed-light interferometric observatory. We find no evidence for GW signals, either with any individual GRB in this sample or with the population as a whole. For each GRB we place lower bounds on the distance to the progenitor, assuming a fixed GW emission energy of 10(exp -2)Stellar Mass sq c, with a median exclusion distance of 0.8 Mpc for emission at 500 Hz and 0.3 Mpc at 1 kHz. The reduced computational cost associated with a linear search grid will enable rapid searches for GWs associated with Fermi GBM events in the Advanced detector era.
Here, a new variable Eddington factor (VEF) model is presented for nonlinear problems of thermal radiative transfer (TRT). The VEF model is data-driven and acts on known (a-priori) radiation-diffusion solutions for material temperatures in the TRT problem. A linear auxiliary problem is constructed for the radiative transfer equation (RTE) whose emission source and opacities are evaluated at these known material temperatures. The solution to this RTE approximates the specific intensity distribution in phase-space and time. It is applied as a shape function to define the Eddington tensor for the presented VEF model. The shape function computed via the auxiliary RTE problem will capture some degree of transport effects within the TRT problem. The VEF moment equations closed with this approximate Eddington tensor will thus carry with them these captured transport effects. In this study, the temperature data comes from multigroup P 1 , P 1/3 , and flux-limited diffusion radiative transfer models. The proposed VEF model can be interpreted as a transport-corrected diffusion reduced-order model. Numerical results are presented on the Fleck-Cummings test problem which models a supersonic wavefront of radiation. The VEF model is shown to improve accuracy by 1–2 orders of magnitude compared to the considered radiation-diffusion model solutions to the TRT problem.
The Data Assimilation Office (DAO) has been developing a new generation of ultra-high resolution General Circulation Model (GCM) that is suitable for 4-D data assimilation, numerical weather predictions, and climate simulations. These three applications have conflicting requirements. For 4-D data assimilation and weather predictions, it is highly desirable to run the model at the highest possible spatial resolution (e.g., 55 km or finer) so as to be able to resolve and predict socially and economically important weather phenomena such as tropical cyclones, hurricanes, and severe winter storms. For climate change applications, the model simulations need to be carried out for decades, if not centuries. To reduce uncertainty in climate change assessments, the next generation model would also need to be run at a fine enough spatial resolution that can at least marginally simulate the effects of intense tropical cyclones. Scientific problems (e.g., parameterization of subgrid scale moist processes) aside, all three areas of application require the model's computational performance to be dramatically improved as compared to the previous generation. In this talk, I will present the current and future developments of the "finite-volume dynamical core" at the Data Assimilation Office. This dynamical core applies modem monotonicity preserving algorithms and is genuinely conservative by construction, not by an ad hoc fixer. The "discretization" of the conservation laws is purely local, which is clearly advantageous for resolving sharp gradient flow features. In addition, the local nature of the finite-volume discretization also has a significant advantage on distributed memory parallel computers. Together with a unique vertically Lagrangian control volume discretization that essentially reduces the dimension of the computational problem from three to two, the finite-volume dynamical core is very efficient, particularly at high resolutions. I will also present the computational design of the dynamical core using a hybrid distributed-shared memory programming paradigm that is portable to virtually any of today's high-end parallel super-computing clusters.
The Data Assimilation Office (DAO) has been developing a new generation of ultra-high resolution General Circulation Model (GCM) that is suitable for 4-D data assimilation, numerical weather predictions, and climate simulations. These three applications have conflicting requirements. For 4-D data assimilation and weather predictions, it is highly desirable to run the model at the highest possible spatial resolution (e.g., 55 kin or finer) so as to be able to resolve and predict socially and economically important weather phenomena such as tropical cyclones, hurricanes, and severe winter storms. For climate change applications, the model simulations need to be carried out for decades, if not centuries. To reduce uncertainty in climate change assessments, the next generation model would also need to be run at a fine enough spatial resolution that can at least marginally simulate the effects of intense tropical cyclones. Scientific problems (e.g., parameterization of subgrid scale moist processes) aside, all three areas of application require the model's computational performance to be dramatically improved as compared to the previous generation. In this talk, I will present the current and future developments of the "finite-volume dynamical core" at the Data Assimilation Office. This dynamical core applies modem monotonicity preserving algorithms and is genuinely conservative by construction, not by an ad hoc fixer. The "discretization" of the conservation laws is purely local, which is clearly advantageous for resolving sharp gradient flow features. In addition, the local nature of the finite-volume discretization also has a significant advantage on distributed memory parallel computers. Together with a unique vertically Lagrangian control volume discretization that essentially reduces the dimension of the computational problem from three to two, the finite-volume dynamical core is very efficient, particularly at high resolutions. I will also present the computational design of the dynamical core using a hybrid distributed- shared memory programming paradigm that is portable to virtually any of today's high-end parallel super-computing clusters.
Increasing concern about the power consumption of data centers and computer laboratories, which in some cases matches or exceeds the resources required to power a small city, drive a need for a new, integrated approach to parallel performance diagnosis that integrates traditional application oriented performance data with measurements of the physical runtime environment. We have developed infrastructure for combined evaluation of system, application, and machine room performance in the high end environment. We motivate our approach, with a case study of the performance, power and cooling impact of the choice of physical location for a scientific application within the machine room. We present a new intensity metric for use in automated performance diagnosis tools, and discuss the challenges encountered.
There are many APIs for connecting and exchanging data between network peers. Each interface varies wildly based on metrics including performance, portability, and complexity. Specifically, many interfaces make design or implementation choices emphasizing some of the more desirable metrics (e.g., performance) while sacrificing others (e.g., portability). As a direct result, software developers building large, network-based applications are forced to choose a specific network API based on a complex, multi-dimensional set of criteria. Such trade-offs inevitably result in an interface that fails to deliver some desirable features. In this paper, we introduce a novel interface that both supports many features that have become standard (or otherwise generally expected) in other communication interfaces, and strives to export a small, yet powerful, interface. This new interface draws upon years of experience from network-oriented software development best practices to systems-level implementations. The goal is to create a relatively simple, high-level communication interface with low barriers to adoption while still providing important features such as scalability, resiliency, and performance. The result is the Common Communications Interface (CCI): an intuitive API that is portable, efficient, scalable, and robust to meet the needs of network-intensive applications common in HPC and cloud computing.
In existing theoretical approaches to core-level excitations of transition-metal ions in solids relaxation and polarization effects due to the inner core hole are often ignored or described phenomenologically. Here, we set up an ab initio computational scheme that explicitly accounts for such physics in the calculation of x-ray absorption and resonant inelastic x-ray scattering spectra. Good agreement is found with experimental transition-metal L-edge data for the strongly correlated d 9 cuprate Li 2 CuO 2 , for which we also determine the absolute scattering intensities. The newly developed methodology opens the way for the investigation of even more complex d n electronic structures of group VI B to VIII B correlated oxide compounds.
Electronic and optoelectronic hardware implementations of highly parallel computing architectures address several ill-defined and/or computation-intensive problems not easily solved by conventional computing techniques. The concurrent processing architectures developed are derived from a variety of advanced computing paradigms including neural network models, fuzzy logic, and cellular automata. Hardware implementation technologies range from state-of-the-art digital/analog custom-VLSI to advanced optoelectronic devices such as computer-generated holograms and e-beam fabricated Dammann gratings. JPL's concurrent processing devices group has developed a broad technology base in hardware implementable parallel algorithms, low-power and high-speed VLSI designs and building block VLSI chips, leading to application-specific high-performance embeddable processors. Application areas include high throughput map-data classification using feedforward neural networks, terrain based tactical movement planner using cellular automata, resource optimization (weapon-target assignment) using a multidimensional feedback network with lateral inhibition, and classification of rocks using an inner-product scheme on thematic mapper data. In addition to addressing specific functional needs of DOD and NASA, the JPL-developed concurrent processing device technology is also being customized for a variety of commercial applications (in collaboration with industrial partners), and is being transferred to U.S. industries. This viewgraph p resentation focuses on two application-specific processors which solve the computation intensive tasks of resource allocation (weapon-target assignment) and terrain based tactical movement planning using two extremely different topologies. Resource allocation is implemented as an asynchronous analog competitive assignment architecture inspired by the Hopfield network. Hardware realization leads to a two to four order of magnitude speed-up over conventional techniques and enables multiple assignments, (many to many), not achievable with standard statistical approaches. Tactical movement planning (finding the best path from A to B) is accomplished with a digital two-dimensional concurrent processor array. By exploiting the natural parallel decomposition of the problem in silicon, a four order of magnitude speed-up over optimized software approaches has been demonstrated.
Engine knock remains one of the major barriers to further improve thermal efficiency of Spark Ignition (SI) engines. Knock can be suppressed by lowering the compression ratio, or retarding the spark ignition timing, however, at an expense of efficiency penalty. SI engine is usually operated at knock-limited spark advance (KLSA) to achieve possibly maximum efficiency with given engine hardware and fuel properties, such as Research Octane Number (RON), Motor Octane Number (MON), and heat of vaporization, etc. Co-optimization of engine design and fuel properties is promising to improve the engine efficiency and predictive CFD models can be used to facilitate this optimization process. However, difficulties exist in predicting KLSA in CFD simulations. First, cyclic variability of SI engine demands that multi-cycle results are required to capture the extreme conditions. Secondly, Mach Courant-Friedrichs-Lewy (CFL) number of 1 is desired to accurately predict the knock intensity (KI), resulting in unaffordable computational cost, especially for multi-cycle simulations. In this study, a new approach to numerically predict KLSA using large Mach CFL number of 50 is proposed. This approach is validated against experimental data for a boosted Direct Injection Spark Ignition (DISI) engine at multiple loads and spark timings. G-equation combustion model coupled with well-mixed chemical kinetic model are used to predict the turbulent flame propagation and end-gas auto-ignition, respectively. Simulations run for 10 consecutive engine cycles at each condition. The results show good agreement between model predictions and experiments in terms of cylinder pressure, combustion phasing and cyclic variation. Engine knock is predicted with early spark ignition timing, indicated by significant pressure wave oscillation and end-gas heat release. Maximum Amplitude of Pressure Oscillation (MAPO) analysis is performed to quantify the KI, and the slope change point in KI extrema is used to indicate the KLSA accurately. Using a smaller Mach CFL number of 5 also results in the same conclusions thus demonstrating that this approach is insensitive to the Mach CFL number. The use of large Mach CFL number allows us to achieve fast turn-around time for multi-cycle engine CFD simulations.
The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. Throughout 2021, ESnet and the Office of Fusion Energy Sciences (FES) of the DOE SC organized an ESnet requirements review of FES-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about their relationship to the FES program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.
The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. On May 1, 2021, ESnet and the DOE Office of Energy Efficiency and Renewable Energy (EERE), organized an ESnet requirements review of the ARIES (Advanced Research on Integrated Energy Systems) platform. Preparation for this event included identification of key stakeholders to the process: program and facility management, research groups, technology providers, and a number of external observers. These individuals were asked to prepare formal case study documents in order to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.
The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. Between March and September 2022, ESnet and the Office of Basic Energy Sciences (BES) of the DOE SC organized an ESnet requirements review of BES-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the BES program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task, along with guidance on how the review would proceed in a virtual fashion.
Analytical algorithms developed and optimized for quantifying the wake behind in-stream hydrokinetic turbines are presented. These algorithms are based on wake expressions originally developed for wind turbines. Unlike previous related studies, the optimization of empirical coefficients contained in these algorithms is conducted using centerline velocity data from multiple published experimental studies of the wake velocities behind in-stream hydrokinetic turbine models or porous disks and not using computational fluid dynamics. Empirical coefficients are first individually optimized based on each set of experimental data, and then empirically based coefficient expressions are created using all of the data sets collectively, such that they are functions of ambient turbulence intensity. This expands the applicability of the created algorithms to cover the expected range of operating conditions for in-stream hydrokinetic turbines. Wind turbine wake model expressions are also modified to characterize the dependence of wake velocities on radial location from the centerline of in-stream hydrokinetic turbines. Thus, expressions with empirically optimized coefficients for calculating wake velocities behind in-stream hydrokinetic turbines are described in terms of both centerline and radial positions. Wake predictions made using the Larsen model for radial dependence are shown to diverge from experimental measurements near the wake radius defined by the Jensen model, suggesting that this is a good indication of the cutoff point beyond which numerical estimations no longer apply. Results suggest that using a combined Larsen/Ainslie approach or combined Jensen/Ainslie approach for characterizing wake have similar mean errors to using only a Larsen approach.
The method of three-dimensional stylus profilometry is presented together with a computer-based system for transforming the surface topography of a specimen into an intensity image. Examples are given in which intensity images obtained by stylus profilometry are compared with scanning electron micrographs and two other representations of the stylus data. The comparisons are made for three types of surface irregularities: a discrete feature, a periodic profile surface, and a random profile surface. It is shown that stylus profilometry provides high vertical resolution for low sloped surfaces and thus can be useful in revealing features not easily detectable with other methods.
Techniques were developed or improved to calibrate, enhance, and geometrically correct LANDSAT-4 satellite data. Statistical techniques to correct data radiometry were evaluated and were found to minimize striping and banding. Conventional techniques cause striping even with perfect calibration parameters. Intensity enhancement techniques were improved to display image data with large variation in intensity or brightness. Data were geometrically corrected to conform to a 1:100,000 map reference and image products produced with the map overlay. It is shown that these products can serve as accurate map products. A personal computer was experimentally used for digital image processing.