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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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This report outlines the optimization of the CMDFT code by Xiaoguang Zhang during June-July 2006. The overall improvement in speed is nearly 40%. Possible further optimizatins are also discussed.
Optimal time code system using correlation detection techniques
In the year 2001, NASA will launch the satellite TRIANA that will be the first Earth observing mission to provide a continuous, full disk view of the sunlit Earth. As a part of the HPCC Program at NASA GSFC, we have started a project whose objectives are to develop and implement a 3D cloud data assimilation system, by combining TRIANA measurements with model simulation, and to produce accurate statistics of global cloud coverage as an important element of the Earth's climate. For simulation of the atmosphere within this project we are using the NCEP/NOAA operational Eta model. In order to compare TRIANA and the Eta model data on approximately the same grid without significant downscaling, the Eta model will be integrated at a resolution of about 15 km. The integration domain (from -70 to +70 deg in latitude and 150 deg in longitude) will cover most of the sunlit Earth disc and will continuously rotate around the globe following TRIANA. The cloud data assimilation is supposed to run and produce 3D clouds on a near real-time basis. Such a numerical setup and integration design is very ambitious and computationally demanding. Thus, though the Eta model code has been very carefully developed and its computational efficiency has been systematically polished during the years of operational implementation at NCEP, the current MPI version may still have problems with memory and efficiency for the TRIANA simulations. Within this work, we optimize a parallel version of the Eta model code on a Cray T3E and a network of PCs (theHIVE) in order to improve its overall efficiency. Our optimization procedure consists of introducing dynamically allocated arrays to reduce the size of static memory, and optimizing on a single processor by splitting loops to limit the number of streams. All the presented results are derived using an integration domain centered at the equator, with a size of 60 x 60 deg, and with horizontal resolutions of 1/2 and 1/3 deg, respectively. In accompanying charts we report the elapsed time, the speedup and the Mflops as a function of the number of processors for the non-optimized version of the code on the T3E and theHIVE. The large amount of communication required for model integration explains its poor performance on theHIVE. Our initial implementation of the dynamic memory allocation has contributed to about 12% reduction of memory but has introduced a 3% overhead in computing time. This overhead was removed by performing loop splitting in some of the high demanding subroutines. When the Eta code is fully optimized in order to meet the memory requirement for TRIANA simulations, a non-negligeable overhead may appear that may seriously affect the efficiency of the code. To alleviate this problem, we are considering implementation of a new algorithm for the horizontal advection that is computationally less expensive, and also a new approach for marching in time.
An integrated analytical tool has been developed for use in designing optimized space-based heat pipe radiator systems. This code, SPRITE-1, incorporates the thermal, structural, and reliability aspects of the radiator into a single framework from which a physically consistent design can be obtained. A parametric study of the integral heat pipe panel radiator was performed using SPRITE-1, and a preliminary minimum mass design was obtained. The radiator design is summarized, and the mass minimization method and results are presented.
This Matlab script implements a simple topology optimization algorithm that minimizes the compliance of a structure within a 3D prismatic design domain. It uses a simple linear elastic finite element solver to solve the equilibrium equations, and the optimality criterion (00) method to update the design iteratively. This functionality is identical to, or the simplest possible extension from 2D to 3D of, a 99-line Matlab code released in 2001 in a journal article by Sigmund. What is new in this code is our implementation of simple surrogate models for several truss micro-architectures, which converts this code to a multiscale design tool, whereas the original code is single-scale. The development of these surrogate models and their implementation is described in a journal article we intend to submit. We plan to include the full source code in an Appendix in this paper. The code’s primary function is as a pedagogical tool rather than as a finished software product. Sigmund’s 2001 paper describes the implementation line-by-line. We are including this updated version of the code to allow the reader to understand exactly what changes must be made to a “standard” topology optimization code to implement the surrogate models and thus multiscale design optimization.
A software-hardware complex with the possibility of simulating the functioning of technological processes with APCS in real and accelerated time is being developed as part of Project Breakthrough in order to determine the optimal operating regimes of the technological processes of the units in a closed nuclear fuel cycle. It makes possible monitoring and control functions and the visualization and archiving of the results of simulation, which makes it possible at the development stages of technologies to evaluate the degree of adherence to the requirements of the technological equipment, regulating organs and final-control mechanisms, measurement apparatus, and monitoring channels, to analyze the control and diagnostics algorithms, blocking and protection, and to make when necessary changes in the design being developed. The developed software-hardware complex is a stimulator that will make it possible not only to investigate process schemes with their corresponding algorithms but also to develop training complexes for the training and certification of service personnel.
This project investigated new techniques for compiling stencil and stencil-like computations. Stencils computations are commonly found in applications such as image processing, physical simulations, image processing, and machine learning. In recent years, many high-performance domain-specific languages (DSLs) have been proposed to optimize stencil computations. To leverage such DSLs, however, existing codes often need to be rewritten. Such rewriting is manual, labor intensive, and error-prone. To alleviate such issues, this project investigated the application of program synthesis and artificial learning techniques to enable stencil computations to automatically leverage new high-performance DSLs. Rather than constructing syntax driven rules, verified lifting uses program synthesis to search for a target code fragment to compile the given input code into. In addition, it also searches for a proof that validates how the found target code fragment preserves the semantics of the original input. Thus, the target code fragment is guaranteed to be semantically equivalent to the input.
We design a parametric multicarrier phase-coded (MCPC) waveform that achieves the optimal performance in detecting an extended target in the presence of signal-dependent interference. Traditional waveform design techniques provide only the optimal energy spectral density of the transmit waveform and suffer a performance loss in the synthesis process of the time-domain signal. Therefore, we opt for directly designing an MCPC waveform in terms of its time-frequency codes to obtain the optimal detection performance. First, we describe the modeling assumptions considering an extended target buried within the signal-dependent clutter with known power spectral density, and deduce the performance characteristics of the optimal detector. Then, considering an MCPC signal transmission, we express the detection characteristics in terms of the phase-codes of the MCPC waveform and propose to optimally design the MCPC signal by maximizing the detection probability. Our numerical results demonstrate that the designed MCPC signal attains the optimal detection performance and requires a lesser computational time than the other parametric waveform design approach.
This technical report encompasses development completed on the Glenn Research Center Communication Analysis Suite (GCAS) as it relates to converting code from the original MATLAB® (The MathWorks, Inc.) GCAS code base to C++. It focuses on a majority of the functions, structures, and intermediate code developed for radiofrequency (RF) links calculations. The report serves as a comprehensive record of the current progress and outlines the technical challenges overcome during this research. Throughout this project, numerous functions and structures were migrated end to end, with validation test cases conducted in accordance with NASA’s verification and validation efforts for standard models and simulations. This report also provides high-level documentation of the completed work; detailed low-level documentation has been compiled to provide a line-by-line explanation in alignment with NASA’s goals. This development has largely been focused on atmospheric effects on calculating RF link budgets for both terrestrial and space links. Some developments outside of atmospheric functions are also covered throughout this report; these are largely related to mathematical operations and MATLAB® structure conversions.
An iterative design algorithm for the joint design of complexity- and entropy-constrained subband quantizers and associated entropy coders is proposed. Unlike conventional subband design algorithms, the proposed algorithm does not require the use of various bit allocation algorithms. Multistage residual quantizers are employed here because they provide greater control of the complexity-performance tradeoffs, and also because they allow efficient and effective high-order statistical modeling. The resulting subband coder exploits statistical dependencies within subbands, across subbands, and across stages, mainly through complexity-constrained high-order entropy coding. Experimental results demonstrate that the complexity-rate-distortion performance of the new subband coder is exceptional.
GeantV is a complex system based on the interaction of different modules needed for detector simulation, which include transport of particles in fields, physics models simulating their interactions with matter and a geometrical modeler library for describing the detector and locating the particles and computing the path length to the current volume boundary. The GeantV project is recasting the classical simulation approach to get maximum benefit from SIMD/MIMD computational architectures and highly massive parallel systems. This involves finding the appropriate balance between several aspects influencing computational performance (floating-point performance, usage of off-chip memory bandwidth, specification of cache hierarchy, etc.) and handling a large number of program parameters that have to be optimized to achieve the best simulation throughput. This optimization task can be treated as a black-box optimization problem, which requires searching the optimum set of parameters using only point-wise function evaluations. Here, the goal of this study is to provide a mechanism for optimizing complex systems (high energy physics particle transport simulations) with the help of genetic algorithms and evolution strategies as tuning procedures for massive parallel simulations. One of the described approaches is based on introducing a specific multivariate analysis operator that could be used in case of resource expensive or time consuming evaluations of fitness functions, in order to speed-up the convergence of the black-box optimization problem.
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Figure 1 shows, schematically, the tokamak device, whose intent is to magnetically confine a high-temperature plasma and produce energy through nuclear fusion. The confined plasma, shown in pink, is toroidal in shape. For several decades now, scientists have been studying and improving these devices, working toward a successful fusion ignition—creating a burning plasma—and sustaining it to generate more power than it took to create it. The next big experimental step will be the International Thermonuclear Experimental Reactor, a twenty billion dollar burning plasma device under construction in France, involving the partnership of seven governments. The human in Figure 2 illustrates the scale of ITER. The magnetic field in the tokamak confines the plasma—ions, electrons, and their heat and momentum. Various kinds of instabilities in the plasma work against that confinement. Turbulent fluctuations can cause transport of particles and energy across the magnetic field lines, toward the outside of the plasma, where it is lost. Loss of plasma and energy, of course, works against confinement and successful fusion. This microturbulence is something the fusion community needs to understand and control. In past and present tokamaks, measurements have shown that the transport of energy and particles caused by microturbulence depends on the size of the tokamak—larger means more transport. This is Bohm scaling. However, theoretical arguments predict that beyond a certain size, which ITER will be beyond, the size dependence goes away, leading to relatively smaller turbulent losses and thus better confinement. This is GyroBohm scaling. The key target of this ESP project is to simulate microturbulent transport for devices of different sizes up through ITER size, and validate and understand GyroBohm scaling.
GPU Monte Carlo Simulation Code with a taste of RASPA We present enhancements in Monte Carlo simulation speed and functionality within an open-source code, gRASPA, which uses graphical processing units (GPUs) to achieve significant performance improvements compared to serial, CPU implementations of Monte Carlo. The code supports a wide range of Monte Carlo simulations, including canonical ensemble (NVT), grand canonical, NVT Gibbs, Widom test particle insertions, and continuous-fractional component Monte Carlo. Implementation of grand canonical transition matrix Monte Carlo (GC-TMMC) and a novel feature to allow different moves for the different components of metal-organic framework (MOF) structures exemplify the capabilities of gRASPA for precise free energy calculations and enhanced adsorption studies, respectively. The introduction of a High-Throughput Computing (HTC) mode permits many Monte Carlo simulations on a single GPU device for accelerated materials discovery. The code can incorporate machine learning (ML) potentials. The open-source nature of gRASPA promotes reproducibility and openness in science, and users may add features to the code and optimize it for their own purposes. The code is written in CUDA/C++ and SYCL/C++ to support different GPU vendors. The gRASPA code is publicly available at https://github.com/snurr-group/gRASPA.
The minimum weight optimization of structural systems, subject to strength and displacement constraints as well as size side constraints, was investigated by the Simultaneous ANalysis and Design (SAND) approach. As an optimizer, the code NPSOL was used which is based on a sequential quadratic programming (SQP) algorithm. The structures were modeled by the finite element method. The finite element related input to NPSOL was automatically generated from the input decks of such standard FEM/optimization codes as NASTRAN or ASTROS, with the stiffness matrices, at present, extracted from the FEM code ANALYZE. In order to avoid ill-conditioned matrices that can be encountered when the global stiffness equations are used as additional nonlinear equality constraints in the SAND approach (with the displacements as additional variables), the matrix displacement method was applied. In this approach, the element stiffness equations are used as constraints instead of the global stiffness equations, in conjunction with the nodal force equilibrium equations. This approach adds the element forces as variables to the system. Since, for complex structures and the associated large and very sparce matrices, the execution times of the optimization code became excessive due to the large number of required constraint gradient evaluations, the Kreisselmeier-Steinhauser function approach was used to decrease the computational effort by reducing the nonlinear equality constraint system to essentially a single combined constraint equation. As the linear equality and inequality constraints require much less computational effort to evaluate, they were kept in their previous form to limit the complexity of the KS function evaluation. To date, the standard three-bar, ten-bar, and 72-bar trusses have been tested. For the standard SAND approach, correct results were obtained for all three trusses although convergence became slower for the 72-bar truss. When the matrix displacement method was used, correct results were still obtained, but the execution times became excessive due to the large number of constraint gradient evaluations required. Using the KS function, the computational effort dropped, but the optimization seemed to become less robust. The investigation of this phenomenon is continuing. As an alternate approach, the code MINOS for the optimization of sparse matrices can be applied to the problem in lieu of the Kreisselmeier-Steinhauser function. This investigation is underway.
A theoretical background was established for the compilation process by dividing it into five phases and explaining the concepts and algorithms that underpin each. The five selected phases were lexical analysis, syntax analysis, semantic analysis, optimization, and code generation. Graph theoretical optimization techniques were presented, and approaches to code generation were described for both one-pass and multipass compilation environments. Following the initial tutorial sections, more than 20 tools that were developed to aid in the process of writing compilers were surveyed. Eight of the more recent compiler development aids were selected for special attention - SIMCMP/STAGE2, LANG-PAK, COGENT, XPL, AED, CWIC, LIS, and JOCIT. The impact of compiler development aids were assessed some of their shortcomings and some of the areas of research currently in progress were inspected.
Testing code for floating-point exceptions is crucial as exceptions can quickly propagate and produce unreliable numerical answers. The state-of-the-art to test for floating-point exceptions in heterogeneous systems is quite limited and solutions require the application’s source code, which precludes their use in accelerated libraries where the source is not publicly available. We present an approach to find inputs that trigger floating-point exceptions in black-box CPU or GPU functions, i.e., functions where the source code and information about input bounds are unavailable. Our approach is the first to use Bayesian optimization (BO) to identify such inputs and uses novel strategies to overcome the challenges that arise in applying BO to this problem. Here, we implement our approach in the XSCOPE framework and demonstrate it on 58 functions from the CUDA Math Library and 81 functions from the Intel Math Library. XSCOPE is able to identify inputs that trigger exceptions in about 73% of the tested functions.
A coded aperture imaging system was designed for the Gamma-Ray imaging spectrometer (GRIS). The system is optimized for imaging 511 keV positron-annihilation photons. For a galactic center 511-keV source strength of 0.001 sq/s, the source location accuracy is expected to be + or - 0.2 deg.