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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 sourcesNASA NTRSDOE OSTI
DOE OSTI2018

FY18 ASC P&EM L2 Milestone 6362: Local Failure Local Recovery (LFLR) Resiliency for Asynchronous Many Task (AMT) Programming and Execution Models: Executive Summary

The overall goal of this work was to perform an in-depth analysis of resilience schemes adapted to the Asynchronous Many-Task (AMT) programming and execution model with the goal of informing the Sandia Advanced Simulation and Computing (ASC) program's application development strategy for next generation platforms (NGPs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

FY18 ASC P&EM L2 Milestone 6362: Local Failure Local Recovery (LFLR) Resiliency for Asynchronous Many Task (AMT) Programming and Execution Models: Executive Summary

The overall goal of this work was to perform an in-depth analysis of resilience schemes adapted to the Asynchronous Many-Task (AMT) programming and execution model with the goal of informing the Sandia Advanced Simulation and Computing (ASC) program's application development strategy for next generation platforms (NGPs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

SCaLeM: A Framework for Characterizing and Analyzing Execution Models

As scalable parallel systems evolve towards more complex nodes with many-core architectures and larger trans-petascale & upcoming exascale deployments, there is a need to understand, characterize and quantify the underlying execution models being used on such systems. Execution models are a conceptual layer between applications & algorithms and the underlying parallel hardware and systems software on which those applications run. This paper presents the SCaLeM (Synchronization, Concurrency, Locality, Memory) framework for characterizing and execution models. SCaLeM consists of three basic elements: attributes, compositions and mapping of these compositions to abstract parallel systems. The fundamental Synchronization, Concurrency, Locality and Memory attributes are used to characterize each execution model, while the combinations of those attributes in the form of compositions are used to describe the primitive operations of the execution model. The mapping of the execution model’s primitive operations described by compositions, to an underlying abstract parallel system can be evaluated quantitatively to determine its effectiveness. Finally, SCaLeM also enables the representation and analysis of applications in terms of execution models, for the purpose of evaluating the effectiveness of such mapping.

Exascale↗
DOE OSTI2015

On the Impact of Execution Models: A Case Study in Computational Chemistry

Efficient utilization of high-performance computing (HPC) platforms is an important and complex problem. Execution models, abstract descriptions of the dynamic runtime behavior of the execution stack, have significant impact on the utilization of HPC systems. Using a computational chemistry kernel as a case study and a wide variety of execution models combined with load balancing techniques, we explore the impact of execution models on the utilization of an HPC system. We demonstrate a 50 percent improvement in performance by using work stealing relative to a more traditional static scheduling approach. We also use a novel semi-matching technique for load balancing that has comparable performance to a traditional hypergraph-based partitioning implementation, which is computationally expensive. Using this study, we found that execution model design choices and assumptions can limit critical optimizations such as global, dynamic load balancing and finding the correct balance between available work units and different system and runtime overheads. With the emergence of multi- and many-core architectures and the consequent growth in the complexity of HPC platforms, we believe that these lessons will be beneficial to researchers tuning diverse applications on modern HPC platforms, especially on emerging dynamic platforms with energy-induced performance variability.

Execution models, computational chemistry, MPI and↗
NASA NTRS1992

Execution models for mapping programs onto distributed memory parallel computers

The problem of exploiting the parallelism available in a program to efficiently employ the resources of the target machine is addressed. The problem is discussed in the context of building a mapping compiler for a distributed memory parallel machine. The paper describes using execution models to drive the process of mapping a program in the most efficient way onto a particular machine. Through analysis of the execution models for several mapping techniques for one class of programs, we show that the selection of the best technique for a particular program instance can make a significant difference in performance. On the other hand, the results of benchmarks from an implementation of a mapping compiler show that our execution models are accurate enough to select the best mapping technique for a given program.

Sussman, Alan↗
NASA NTRS2017

Reconfigurable Model Execution in the OpenMDAO Framework

NASA's OpenMDAO framework facilitates constructing complex models and computing their derivatives for multidisciplinary design optimization. Decomposing a model into components that follow a prescribed interface enables OpenMDAO to assemble multidisciplinary derivatives from the component derivatives using what amounts to the adjoint method, direct method, chain rule, global sensitivity equations, or any combination thereof, using the MAUD architecture. OpenMDAO also handles the distribution of processors among the disciplines by hierarchically grouping the components, and it automates the data transfer between components that are on different processors. These features have made OpenMDAO useful for applications in aircraft design, satellite design, wind turbine design, and aircraft engine design, among others. This paper presents new algorithms for OpenMDAO that enable reconfigurable model execution. This concept refers to dynamically changing, during execution, one or more of: the variable sizes, solution algorithm, parallel load balancing, or set of variables-i.e., adding and removing components, perhaps to switch to a higher-fidelity sub-model. Any component can reconfigure at any point, even when running in parallel with other components, and the reconfiguration algorithm presented here performs the synchronized updates to all other components that are affected. A reconfigurable software framework for multidisciplinary design optimization enables new adaptive solvers, adaptive parallelization, and new applications such as gradient-based optimization with overset flow solvers and adaptive mesh refinement. Benchmarking results demonstrate the time savings for reconfiguration compared to setting up the model again from scratch, which can be significant in large-scale problems. Additionally, the new reconfigurability feature is applied to a mission profile optimization problem for commercial aircraft where both the parametrization of the mission profile and the time discretization are adaptively refined, resulting in computational savings of roughly 10% and the elimination of oscillations in the optimized altitude profile.

Hwang, John T.↗
NASA NTRS2008

Design of a Model Execution Framework: Repetitive Object-Oriented Simulation Environment (ROSE)

The ROSE framework was designed to facilitate complex system analyses. It completely divorces the model execution process from the model itself. By doing so ROSE frees the modeler to develop a library of standard modeling processes such as Design of Experiments, optimizers, parameter studies, and sensitivity studies which can then be applied to any of their available models. The ROSE framework accomplishes this by means of a well defined API and object structure. Both the API and object structure are presented here with enough detail to implement ROSE in any object-oriented language or modeling tool.

Gray, Justin S.↗
DOE OSTI2018

FY18 ASC CSSE L2 Milestone 6362: Local Failure Local Recovery (LFLR) Resiliency for Asynchronous Many Task (AMT) Programming and Execution Models

The overall goal of this work was to perform an in-depth analysis of resilience schemes adapted to the Asynchronous Many-Task (AMT) programming and execution model with the goal of informing the Sandia Advanced Simulation and Computing (ASC) program's application development strategy for next generation platforms (NGPs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

PERKS: a Locality-Optimized Execution Model for Iterative Memory-bound GPU Applications

Iterative memory-bound solvers commonly occur in HPC codes. Typical GPU implementations have a loop on the host side that invokes the GPU kernel as much as time/algorithm steps there are. The termination of each kernel implicitly acts the barrier required after advancing the solution every time step. We propose an execution model for running memory-bound iterative GPU kernels: PERsistent KernelS (PERKS). In this model, the time loop is moved inside persistent kernel, and device-wide barriers are used for synchronization. We then reduce the traffic to device memory by caching subset of the output in each time step in the unused registers and shared memory. PERKS can be generalized to any iterative solver: they largely independent of the solver's implementation. We explain the design principle of PERKS and demonstrate effectiveness of PERKS for a wide range of iterative 2D/3D stencil benchmarks (geomean speedup of 2.12x for 2D stencils and 1.24x for 3D stencils over state-of-art libraries), and a Krylov subspace conjugate gradient solver (geomean speedup of 4.86x in smaller SpMV datasets from SuiteSparse and 1.43x in larger SpMV datasets over a state-of-art library). All PERKS-based implementations available at: https://github.com/neozhang307/PERKS.

Zhang, Lingqi↗
NASA NTRS2016

Creating Systems Engineering Products with Executable Models in a Model-Based Engineering Environment

Applying systems engineering across the life-cycle results in a number of products built from interdependent sources of information using different kinds of system level analysis. This paper focuses on leveraging the Executable System Engineering Method (ESEM) which automates requirements verification (e.g. power and mass budget margins and duration analysis of operational modes) using executable SysML models. The particular value proposition is to integrate requirements, and executable behavior and performance models for certain types of system level analysis. The models are created with modeling patterns that involve structural, behavioral and parametric diagrams, and are managed by an open source Model Based Engineering Environment (named OpenMBEE). This paper demonstrates how the ESEM is applied in conjunction with OpenMBEE to create key engineering products (e.g. operational concept document) for the Alignment and Phasing System (APS) within the Thirty Meter Telescope (TMT) project, which is under development by the TMT International Observatory (TIO).

MBSE↗
DOE OSTI2019

Toward A High-Performance Emulation Platform for Brain-Inspired Intelligent Systems Exploring Dataflow-Based Execution Model and Beyond

Brain-inspired computing is a novel computing technology based on neural morphological engineering, which draws lessons from methods of human brain information processing and storage. Combining with the high-performance computing (HPC) platform, they constitute the foundation of general artificial intelligence. However, current brain HPC platforms generally suffer from slow speed, poor scalability, and high energy consumption, which severely restrain its potential and circumscribe the development of general artificial intelligence. The dataflow model was first proposed in the 1970s, providing a novel idea for the development of HPC. In addition, the dataflow model shares similar information processing mechanisms with human's neural system, which makes dataflow models naturally suit the emulation of brain-inspired computing. Based on the contemporary progress of the dataflow model, the Codelet model was proposed. Through a fine-grained asynchronous program execution and resource allocation, the Codelet model successfully realized the distributed computing on the heterogeneous system, effectively improved the computing power and speed, and open up a new path to overcome the shortcomings of the existing high-performance computing technology. We propose a dataflow-based emulation platform, aiming at providing high-performance computing technology support for general brain-inspired intelligent system, as well as using characteristics of dataflow models to fully explore the potential of brain-inspired intelligence. As an example, we will select a convolutional neural network (LeNet5) that already has a spectacular user base to initially verify the superiority and feasibility of our proposal.

brain-inspired intelligent system↗
NASA NTRS1980

Advanced flight design systems subsystem performance models. Executive summary

Subsystem performance analysis required in flight design to assess the capability of the environmental control and life support system (ECLSS) to support the flight requirements and define operational procedures under contingency flight conditions is described. Current ECLSS modeling techniques are limited in the variety of configurations and they employ batch mode computer programs execution methods. Improvements in the variety of configurations that are modeled and a reduction in effort required for modeling and analysis is accomplished by developing a modular computer program which operates interactively.

Parker, K. C.↗