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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 19 records

All sources NASA NTRS DOE OSTI

DOE OSTI2019

System and method for dynamically allocating memory to hold pending write requests

A processing system employs a memory module as a temporary write buffer to store write requests when a write buffer at a memory controller reaches a threshold capacity, and de-allocates the temporary write buffer when the write buffer capacity falls below the threshold. Upon receiving a write request, the memory controller stores the write request in a write buffer until the write request can be written to main memory. The memory controller can temporarily extend the memory controller's write buffer to the memory module, thereby accommodating temporary periods of high memory activity without requiring a large permanent write buffer at the memory controller.

Farahani, Amin Farmahini↗

DOE OSTI2012

Persistent heap Management library

PERM is a C library for persistent heap management and is intended for use with a dynamic-memory allocator (e.g. malloc, free). The PERM memory allocator replaces the standard C dynamic memory allocation functions with compatible versions that provide persistent memory to application programs. Memory allocated with the PERM allocatory will persist between program invocations after a call to a checkpoint function. This function essentially saves the state of the heap and registered global variables to a file which may reside in flash memory or other node local storage. A few other functions are also provided by the library to manage checkpoint files. Global variables in an application can be marked persistent and be included in a checkpoint by using a compiler attribute defined as PERM. The PERM checkpoint methof is not dependent on the programming model ans works with distributed memory or shared memory programs.

Source record↗

DOE OSTI2012

Persistent heap Management library

Source record↗

NASA NTRS2000

Performance of an Optimized Eta Model Code on the Cray T3E and a Network of PCs

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.

Kouatchou, Jules↗

NASA NTRS1990

Inflated speedups in parallel simulations via malloc()

Discrete-event simulation programs make heavy use of dynamic memory allocation in order to support simulation's very dynamic space requirements. When programming in C one is likely to use the malloc() routine. However, a parallel simulation which uses the standard Unix System V malloc() implementation may achieve an overly optimistic speedup, possibly superlinear. An alternate implementation provided on some (but not all systems) can avoid the speedup anomaly, but at the price of significantly reduced available free space. This is especially severe on most parallel architectures, which tend not to support virtual memory. It is shown how a simply implemented user-constructed interface to malloc() can both avoid artificially inflated speedups, and make efficient use of the dynamic memory space. The interface simply catches blocks on the basis of their size. The problem is demonstrated empirically, and the effectiveness of the solution is shown both empirically and analytically.

Nicol, David M.↗

NASA NTRS1992

Preliminary efforts toward development of data handling and analysis software for unsteady flow measurements: An application for aeroelastic transonic flow configurations

A few years ago the Structural Dynamics Division at LaRC started ambitious experimental research efforts known as the Benchmark Models Program. The primary objective of this program was to provide experimental data that may serve as a calibration source for computational fluid dynamics (CFD) efforts that deal with aeroelastic unsteady flow configurations. It also focuses on the understanding of complex flow phenomenon associated with unsteady flow developments. The overall plan for the program has been described by Bennett, including a presentation of initial test results of flutter of a rigid wing mounted on flexible supports. An example of a test model employed to measure the dynamic response along with corresponding pressure distributions is shown. This model incorporates eighty pressure transducers distributed along two spanwise stations. In addition, the models are equipped with four accelerometers and two strain gages. The data handling system for the Benchmark Model Program is under development. Several interactive computer routines designed for the user interface, dynamic memory allocation, unsteady flow measurements data extraction, and further data processing were developed. To present a few examples of measured data, the unsteady pressure distributions and the wing model dynamic response were plotted.

Finaish, Fathi↗

DOE OSTI2010

Feedback-directed page placement for ccNUMA via hardware-generated memory traces

Non-uniform memory architectures with cache coherence (ccNUMA) are becoming increasingly common, not just for large-scale high performance platforms but also in the context of multi-core architectures. Under ccNUMA, data placement may influence overall application performance significantly as references resolved locally to a processor/core impose lower latencies than remote ones. This work develops a novel hardware-assisted page placement paradigm based on automated tracing of the memory references made by application threads. Two placement schemes, modeling both singlelevel and multi-level latencies, allocate pages near processors that most frequently access that memory page. These schemes leverage performance monitoring capabilities of contemporary microprocessors to efficiently extract an approximate trace of memory accesses. This information is used to decide page affinity, i.e., the node to which the page is bound. The method operates entirely in user space, is widely automated, and handles not only static but also dynamic memory allocation. Experiments show that this method, although based on lossy tracing, can efficiently and effectively improve page placement, leading to an average wall-clock execution time saving of over 20% for the tested benchmarks on the SGI Altix with a 2x remote access penalty and 12% on AMD Opterons with a 1.3–2.0x access penalty. This is accompanied by a one-time tracing overhead of 2.7% over the overall original program wallclock time.

Computer Science↗

DOE OSTI2014

A Flexible CUDA LU-based Solver for Small, Batched Linear Systems

This chapter presents the implementation of a batched CUDA solver based on LU factorization for small linear systems. This solver may be used in applications such as reactive flow transport models, which apply the Newton-Raphson technique to linearize and iteratively solve the sets of non linear equations that represent the reactions for ten of thousands to millions of physical locations. The implementation exploits somewhat counterintuitive GPGPU programming techniques: it assigns the solution of a matrix (representing a system) to a single CUDA thread, does not exploit shared memory and employs dynamic memory allocation on the GPUs. These techniques enable our implementation to simultaneously solve sets of systems with over 100 equations and to employ LU decomposition with complete pivoting, providing the higher numerical accuracy required by certain applications. Other currently available solutions for batched linear solvers are limited by size and only support partial pivoting, although they may result faster in certain conditions. We discuss the code of our implementation and present a comparison with the other implementations, discussing the various tradeoffs in terms of performance and flexibility. This work will enable developers that need batched linear solvers to choose whichever implementation is more appropriate to the features and the requirements of their applications, and even to implement dynamic switching approaches that can choose the best implementation depending on the input data.

bached linear solvers↗

DOE OSTI2026

Linear complexity

We present factorization and solution phases for a new linear complexity direct solver designed for concurrent batch operations on fine-grained parallel architectures, for matrices amenable to hierarchical representation. We focus on the strong-admissibility-based $\mathscr{H}^{2}$ format, where strong recursive skeletonization factorization compresses remote interactions. We build upon previous implementations of $\mathscr{H}^{2}$ matrix construction for efficient factorization and solution algorithm design, which are illustrated graphically in stepwise detail. The algorithms are ‘blackbox’ in the sense that the only inputs are the matrix and right-hand side, without analytical or geometrical information about the origin of the system. We demonstrate linear complexity scaling in both time and memory on four representative families of dense matrices up to one million in size. Parallel scaling up to 16 threads is enabled by a multi-level matrix graph coloring and avoidance of dynamic memory allocations thanks to prefix-sum memory management. An experimental backward error analysis is included. We break down the timings of different phases, identify phases that are memory-bandwidth limited, and discuss alternatives for phases that may be sensitive to the trend to employ lower precisions for performance.

Boukaram, Wajih↗

DOE OSTI2020

GPU-Based Static Data-Flow Analysis for Fast and Scalable Android App Vetting

Many popular vetting tools for Android applications use static code analysis techniques. In particular, Interprocedural Data-Flow Graph (IDFG) construction is the computation at the core of Android static data-flow analysis and consumes most of the analysis time. Many analysis tools use a worklist algorithm, an iterative fixed-point approach, to construct the IDFG. In this paper, we observe that a straightforward GPU parallelization of the worklist algorithm leads to significant underutilization of the GPU resources. We identify four performance bottlenecks, namely, frequent dynamic memory allocations, high branch divergence, workload imbalance, and irregular memory access patterns. Accordingly, we propose GDroid, a GPU-based worklist algorithm implementation with multiple fine-grained optimizations tailored to common characteristics of Android applications. The optimizations considered are: matrix-based data structure, memory access-based node grouping, and worklist merging. Our experimental evaluation, performed on 1000 Android applications, shows that the proposed optimizations are beneficial to performance, and GDroid can achieve up to 128X speedups against a plain GPU implementation.

Android security↗

DOE OSTI2016

iTOUGH2 v7.1

iTOUGH2 (inverse TOUGH2) provides inverse modeling capabilities for TOUGH2, a simulator for multi-dimensional, multi-phase, multi-component, non-isothermal flow and transport in fractured porous media. iTOUGH2 performs sensitivity analyses, data-worth analyses, parameter estimation, and uncertainty propagation analyses in geosciences and reservoir engineering and other application areas. iTOUGH2 supports a number of different combinations of fluids and components (equation-of-state (EOS) modules). In addition, the optimization routines implemented in iTOUGH2 can also be used for sensitivity analysis, automatic model calibration, and uncertainty quantification of any external code that uses text-based input and output files using the PEST protocol. iTOUGH2 solves the inverse problem by minimizing a non-linear objective function of the weighted differences between model output and the corresponding observations. Multiple minimization algorithms (derivative-free, gradient-based, and second-order; local and global) are available. iTOUGH2 also performs Latin Hypercube Monte Carlo simulations for uncertainty propagation analyses. A detailed residual and error analysis is provided. This upgrade includes (a) global sensitivity analysis methods, (b) dynamic memory allocation (c) additional input features and output analyses, (d) increased forward simulation capabilities, (e) parallel execution on multicore PCs and Linux clusters, and (f) bug fixes.

Finsterle, Stefan↗

DOE OSTI2015

TOUGH+ v1.5 Core Code

TOUGH+ v1.5 is a numerical code for the simulation of multi-phase, multi-component flow and transport of mass and heat through porous and fractured media, and represents the third update of the code since its first release [Moridis et al., 2008]. TOUGH+ is a successor to the TOUGH2 [Pruess et al., 1991; 2012] family of codes for multi-component, multiphase ﬂuid and heat ﬂow developed at the Lawrence Berkeley National Laboratory. It is written in standard FORTRAN 95/2003, and can be run on any computational platform (workstations, PC, Macintosh). TOUGH+ v1.5 employs dynamic memory allocation, thus minimizing storage requirements. It has a completely modular structure, follows the tenets of Object-Oriented Programming (OOP), and involves the advanced features of FORTRAN 95/2003, i.e., modules, derived data types, the use of pointers, lists and trees, data encapsulation, defined operators and assignments, operator extension and overloading, use of generic procedures, and maximum use of the powerful intrinsic vector and matrix processing operations. TOUGH+ v1.5 is the core code for its family of applications, i.e., the part of the code that is common to all its applications. It provides a description of the underlying physics and thermodynamics of non-isothermal flow, of the mathematical and numerical approaches, as well as a detailed explanation of the general (common to all applications) input requirements, options, capabilities and output specifications. The core code cannot run by itself: it needs to be coupled with the code for the specific TOUGH+ application option that describes a particular type of problem. The additional input requirements specific to a particular TOUGH+ application options and related illustrative examples can be found in the corresponding User's Manual.

Moridis, GeorgeJ↗

DOE OSTI2014

User's Manual of the TOUGH+ Core Code v1.5: A General-Purpose Simulator of Non-Isothermal Flow and Transport through Porous and Fractured Media

TOUGH+ v1.5 is a numerical code for the simulation of multi-phase, multi-component flow and transport of mass and heat through porous and fractured media, and represents the third update of the code since its first release [Moridis et al., 2008]. TOUGH+ is a successor to the TOUGH2 [Pruess et al., 1991] family of codes for multi-component, multiphase fluid and heat flow developed at the Lawrence Berkeley National Laboratory. It is written in standard FORTRAN 95/2003, and can be run on any computational platform (workstations, PC, Macintosh). TOUGH+ v1.5 employs dynamic memory allocation, thus minimizing storage requirements. It has a completely modular structure, follows the tenets of Object-Oriented Programming (OOP), and involves the advanced features of FORTRAN 95/2003, i.e., modules, derived data types, the use of pointers, lists and trees, data encapsulation, defined operators and assignments, operator extension and overloading, use of generic procedures, and maximum use of the powerful intrinsic vector and matrix processing operations. This report presents the core TOUGH+ v1.5 code, i.e., the part of the code that is common to all its applications. It provides a description of the underlying physics and thermodynamics of non-isothermal flow, of the mathematical and numerical approaches, as well as a detailed explanation of the general (common to all applications) input requirements, options, capabilities and output specifications. The core code cannot run by itself: it needs to be coupled with the code for the specific TOUGH+ application option that describes a particular type of problem. The additional input requirements specific to a particular TOUGH+ application options and related illustrative examples can be found in the corresponding User’s Manual.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

YAHFC: A Code Framework to Model Nuclear Reactions and Estimate Correlated Uncertainties

Reaction modeling is a key ingredient in designing experiments and interpreting their results, and is an essential component in the process of evaluating nuclear data and assembling nuclear data libraries used in nuclear technology applications. Typically, experimental data are available only for a handful of reaction channels and theory models are used to fill in the gaps. In addition, theory is often called upon as the arbitrator between discrepant data. Most importantly, theory and modeling are required for an accurate determination of uncertainties in the evaluated data and the correlations between the multiple channels. A fast, accurate, and flexible modeling capability has been developed at LLNL with the code system YAHFC (Yet Another Hauser-Feshbach Code). YAHFC is a Monte Carlo, Hauser-Feshbach code framework, making full use of dynamic memory allocation, derived types, and parallel computing. YAHFC can generate events to simulate experiments and is guiding experiments designed to measure inelastic neutron scattering from actinide targets. YAHFC is also being used to analyze decays from surrogate experiments, thereby enabling the inference of reaction cross sections inaccessible by direct measurement. Finally, by modeling nuclear reactions with constraints from experimental data, YAHFC can deliver complete nuclear data libraries, with evaluated uncertainties, using the modernized Generalized Nuclear Data Structure (GNDS).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1969

ELAS: A general-purpose computer program for the equilibrium problems of linear structures. Volume 2: Documentation of the program

A general purpose digital computer program for the in-core solution of linear equilibrium problems of structural mechanics is documented. The program requires minimum input for the description of the problem. The solution is obtained by means of the displacement method and the finite element technique. Almost any geometry and structure may be handled because of the availability of linear, triangular, quadrilateral, tetrahedral, hexahedral, conical, triangular torus, and quadrilateral torus elements. The assumption of piecewise linear deflection distribution insures monotonic convergence of the deflections from the stiffer side with decreasing mesh size. The stresses are provided by the best-fit strain tensors in the least squares at the mesh points where the deflections are given. The selection of local coordinate systems whenever necessary is automatic. The core memory is used by means of dynamic memory allocation, an optional mesh-point relabelling scheme and imposition of the boundary conditions during the assembly time.

Utku, S.↗

NASA NTRS1991

Using C to build a satellite scheduling expert system: Examples from the Explorer platform planning system

Recently, many expert systems were developed in a LISP environment and then ported to the real world C environment before the final system is delivered. This situation may require that the entire system be completely rewritten in C and may actually result in a system which is put together as quickly as possible with little regard for maintainability and further evolution. With the introduction of high performance UNIX and X-windows based workstations, a great deal of the advantages of developing a first system in the LISP environment have become questionable. A C-based AI development effort is described which is based on a software tools approach with emphasis on reusability and maintainability of code. The discussion starts with simple examples of how list processing can easily be implemented in C and then proceeds to the implementations of frames and objects which use dynamic memory allocation. The implementation of procedures which use depth first search, constraint propagation, context switching and a blackboard-like simulation environment are described. Techniques for managing the complexity of C-based AI software are noted, especially the object-oriented techniques of data encapsulation and incremental development. Finally, all these concepts are put together by describing the components of planning software called the Planning And Resource Reasoning (PARR) shell. This shell was successfully utilized for scheduling services of the Tracking and Data Relay Satellite System for the Earth Radiation Budget Satellite since May 1987 and will be used for operations scheduling of the Explorer Platform in November 1991.

Mclean, David R.↗

NASA NTRS1990

Toward the efficient implementation of expert systems in Ada

Here, the authors describe Ada language issues encountered during the development of ART-Ada, an expert system tool for Ada deployment. ART-Ada is being used to implement several expert system applications for the Space Station Freedom and the U.S. Air Force. Additional information is given on dynamic memory allocation.

Lee, S. Daniel↗

NASA NTRS1991

Using C to build a satellite scheduling expert system: Examples from the Explorer Platform planning system

A C-based artificial intelligence (AI) development effort which is based on a software tools approach is discussed with emphasis on reusability and maintainability of code. The discussion starts with simple examples of how list processing can easily be implemented in C and then proceeds to the implementations of frames and objects which use dynamic memory allocation. The implementation of procedures which use depth first search, constraint propagation, context switching, and blackboard-like simulation environment are described. Techniques for managing the complexity of C-based AI software are noted, especially the object-oriented techniques of data encapsulation and incremental development. Finally, all these concepts are put together by describing the components of planning software called the Planning And Resource Reasoning (PARR) Shell. This shell was successfully utilized for scheduling services of the Tracking and Data Relay Satellite System for the Earth Radiation Budget Satellite since May of 1987 and will be used for operations scheduling of the Explorer Platform in Nov. of 1991.

Mclean, David R.↗