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NASA's future spaceborne science missions will require supercomputing capabilities for both near earth and deep space exploration.
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This presentation provides information on the progress the Intelligent Payload Module (IPM) development effort. In addition, a vision is presented on integration of the IPM architecture with the GeoSocial Application Program Interface (API) architecture to enable efficient distribution of satellite data products.
This paper will describe the Entry, Descent, and Landing simulation tradeoffs and techniques that were used to provide the Monte Carlo data required to approve entry during a critical period just before entry for the Genesis Sample Return Capsule.
This paper will describe the Entry, Descent and Landing simulation tradeoffs and techniques that were used to provide the data required for an approval to proceed with entry. This approval had to be made during the critical time following the last maneuver that targeted entry and the next maneuver that prevented the main spacecraft from reentering
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Parallel gap resistance welding was used to attach lead wires to sputtered thin film sensors. Ranges of optimum welding parameters to produce an acceptable weld were determined. The thin film sensors were Pt13Rh/Pt thermocouples; they were mounted on substrates of MCrAlY-coated superalloys, aluminum oxide, silicon carbide and silicon nitride. The entire sensor system is designed to be used on aircraft engine parts. These sensor systems, including the thin-film-to-lead-wire connectors, were tested to 1000 C.
Parallel image processing which is defined as image processing where all points of an image are operated upon simultaneously is discussed. Coherent optical, noncoherent optical, and electronic methods are considered parallel image processing techniques.
Research on the computational fluid dynamics (CFD) code evaluation with emphasis on supercomputing in reacting flows is discussed. Advantages of unstructured grids, multigrids, adaptive methods, improved flow solvers, vector processing, parallel processing, and reduction of memory requirements are discussed. As examples, researchers include applications of supercomputing to reacting flow Navier-Stokes equations including shock waves and turbulence and combustion instability problems associated with solid and liquid propellants. Evaluation of codes developed by other organizations are not included. Instead, the basic criteria for accuracy and efficiency have been established, and some applications on rocket combustion have been made. Research toward an ultimate goal, the most accurate and efficient CFD code, is in progress and will continue for years to come.
Conference on parallel image processing for earth observation systems
An approach to photon detection with a parallel asynchronous signal processor is described. The visible or IR photon-detection capability of the silicon p(+)-n-n(+) detectors and the parallel asynchronous processing are addressed separately. This approach would permit an independent analog processing channel to be dedicated to every pixel. A laminar architecture consisting of a stack of planar arrays of the devices would form a 2D array processor with a 2D array of inputs located directly behind a focal-plane detector array. A 2D image data stream would propagate in neuronlike asynchronous pulse-coded form through the laminar processor. Such systems can integrate image acquisition and image processing. Acquisition and processing would be performed concurrently as in natural vision systems. The possibility of multispectral image processing is addressed.
The concept of using Parallel Distributed Processing (PDP) to enhance automated experiment monitoring and control is explored. Recent very large scale integration (VLSI) advances have made such applications an achievable goal. The PDP machine has demonstrated the ability to automatically organize stored information, handle unfamiliar and contradictory input data and perform the actions necessary. The PDP machine has demonstrated that it can perform inference and knowledge operations with greater speed and flexibility and at lower cost than traditional architectures. In applications where the rule set governing an expert system's decisions is difficult to formulate, PDP can be used to extract rules by associating the information an expert receives with the actions taken.
An examination of the effects of atomicity (higher resolution) on the performance of array processors (data-flow computers) is presented. Data-flow principles are reviewed, noting the reliance on parallel processing using functional languages to specify sequencing of the operations. Techniques are described for eliminating the necessity of copying whole arrays between processing steps, thereby reducing the number of store cycles. The method involves setting whole columns to specific values rather than individual elements. The individual column values can be processed in parallel, i.e., a locally optimized condition exists. A drawback of the system is the need for more low level arguments, to identify the appropriate processing sequences, and high system complexity.
Several recent studies have shown performance decrements with increasing display size when background texture elements are present in a same-different feature discrimination task--a result that challenges the traditional notion that the identities of simple visual features are processed in parallel, preattentively. Four experiments are reported that explore the implications of these results. Experiment 1 replicates the recent studies but limits the generalizability of the results to small target numbers. Experiments 2 and 3 show that the observed performance decrements are not due to a serial or even limited-capacity, parallel process. Experiment 4 suggests that decision factors idiosyncratic to the use of texture elements in a same-different task are responsible for the effect. It is concluded that the identification of simple visual features proceeds in parallel, with unlimited capacity (i.e., preattentively).
This code is designed to reconstruct magnetic field deflections, and thus the path-integrated magnetic field, from sets of proton images at two distinct proton probe energies. A differential evolution (DE) algorithm is used to iteratively update a population of solution candidates of the magnetic deflections of the protons for reconstructing the input images, selecting improved candidates as they are discovered. This algorithm was written using MATLAB (R2019a) and makes use of the Image Processing and Parallel Processing Toolboxes.
The development, fabrication, and testing of a preliminary model of an optical-to-optical (noncoherent-to-coherent) interface device for use in coherent optical parallel processing systems are described. The developed device demonstrates a capability for accepting as an input a scene illuminated by a noncoherent radiation source and providing as an output a coherent light beam spatially modulated to represent the original noncoherent scene. The converter device developed under this contract employs a Pockels readout optical modulator (PROM). This is a photosensitive electro-optic element which can sense and electrostatically store optical images. The stored images can be simultaneously or subsequently readout optically by utilizing the electrostatic storage pattern to control an electro-optic light modulating property of the PROM. The readout process is parallel as no scanning mechanism is required. The PROM provides the functions of optical image sensing, modulation, and storage in a single active material.
Recent advances in parallel methods and algorithms integrated into large-scale codes are presented. Consideration is given to problem decomposition (substructuring), efficient matrix solution algorithms for shared memory architectures, dynamic and transient analysis algorithms for shared memory architectures, and algorithms for distributed and massively parallel architectures. Particular attention is given to partitioning of unstructured problems for parallel processing, parallel-vector computation for linear-structural analysis and nonlinear unconstraint optimization problems, a parallel-vector equation solver for unsymmetric matrices on supercomputers, parallel nonlinear finite element dynamic response, multigrid algorithms for solving structural mechanics problems on supercomputers, structural analysis on massively parallel computers, explicit finite element methods with contact-impact on SIMD computers, and the impact of mapping and sparsity on parallelized finite element method modules.
This paper presents a model to evaluate the performance and overhead of parallelizing sequential code using compiler directives for multiprocessing on distributed shared memory (DSM) systems. With increasing popularity of shared address space architectures, it is essential to understand their performance impact on programs that benefit from shared memory multiprocessing. We present a simple model to characterize the performance of programs that are parallelized using compiler directives for shared memory multiprocessing. We parallelized the sequential implementation of NAS benchmarks using native Fortran77 compiler directives for an Origin2000, which is a DSM system based on a cache-coherent Non Uniform Memory Access (ccNUMA) architecture. We report measurement based performance of these parallelized benchmarks from four perspectives: efficacy of parallelization process; scalability; parallelization overhead; and comparison with hand-parallelized and -optimized version of the same benchmarks. Our results indicate that sequential programs can conveniently be parallelized for DSM systems using compiler directives but realizing performance gains as predicted by the performance model depends primarily on minimizing architecture-specific data locality overhead.