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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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NASA NTRS2009

Massively Parallel Dantzig-Wolfe Decomposition Applied to Traffic Flow Scheduling

Optimal scheduling of air traffic over the entire National Airspace System is a computationally difficult task. To speed computation, Dantzig-Wolfe decomposition is applied to a known linear integer programming approach for assigning delays to flights. The optimization model is proven to have the block-angular structure necessary for Dantzig-Wolfe decomposition. The subproblems for this decomposition are solved in parallel via independent computation threads. Experimental evidence suggests that as the number of subproblems/threads increases (and their respective sizes decrease), the solution quality, convergence, and runtime improve. A demonstration of this is provided by using one flight per subproblem, which is the finest possible decomposition. This results in thousands of subproblems and associated computation threads. This massively parallel approach is compared to one with few threads and to standard (non-decomposed) approaches in terms of solution quality and runtime. Since this method generally provides a non-integral (relaxed) solution to the original optimization problem, two heuristics are developed to generate an integral solution. Dantzig-Wolfe followed by these heuristics can provide a near-optimal (sometimes optimal) solution to the original problem hundreds of times faster than standard (non-decomposed) approaches. In addition, when massive decomposition is employed, the solution is shown to be more likely integral, which obviates the need for an integerization step. These results indicate that nationwide, real-time, high fidelity, optimal traffic flow scheduling is achievable for (at least) 3 hour planning horizons.

Rios, Joseph Lucio↗

NASA NTRS1995

Constraint-Based Scheduling System

Report describes continuing development of software for constraint-based scheduling system implemented eventually on massively parallel computer. Based on machine learning as means of improving scheduling. Designed to learn when to change search strategy by analyzing search progress and learning general conditions under which resource bottleneck occurs.

Zweben, Monte↗

NASA NTRS1999

A Parallel Compact Multi-Dimensional Numerical Algorithm with Aeroacoustics Applications

In this study we propose a novel method to parallelize high-order compact numerical algorithms for the solution of three-dimensional PDEs (Partial Differential Equations) in a space-time domain. For this numerical integration most of the computer time is spent in computation of spatial derivatives at each stage of the Runge-Kutta temporal update. The most efficient direct method to compute spatial derivatives on a serial computer is a version of Gaussian elimination for narrow linear banded systems known as the Thomas algorithm. In a straightforward pipelined implementation of the Thomas algorithm processors are idle due to the forward and backward recurrences of the Thomas algorithm. To utilize processors during this time, we propose to use them for either non-local data independent computations, solving lines in the next spatial direction, or local data-dependent computations by the Runge-Kutta method. To achieve this goal, control of processor communication and computations by a static schedule is adopted. Thus, our parallel code is driven by a communication and computation schedule instead of the usual "creative, programming" approach. The obtained parallelization speed-up of the novel algorithm is about twice as much as that for the standard pipelined algorithm and close to that for the explicit DRP algorithm.

Povitsky, Alex↗

NASA NTRS2001

Method for resource control in parallel environments using program organization and run-time support

A system and method for dynamic scheduling and allocation of resources to parallel applications during the course of their execution. By establishing well-defined interactions between an executing job and the parallel system, the system and method support dynamic reconfiguration of processor partitions, dynamic distribution and redistribution of data, communication among cooperating applications, and various other monitoring actions. The interactions occur only at specific points in the execution of the program where the aforementioned operations can be performed efficiently.

Ekanadham, Kattamuri↗

NASA NTRS1999

Method for resource control in parallel environments using program organization and run-time support

Ekanadham, Kattamuri↗

DOE OSTI2024

An exploration of online-simulation-driven portfolio scheduling in Workflow Management Systems

Workflow Management Systems used to automate the execution of scientific workflow applications on parallel and distributed computing platforms must make scheduling decisions at runtime. A large number of workflow scheduling algorithms have been proposed in the literature, but often these algorithms are evaluated based on simplifying assumptions that may not hold in practice. Furthermore, published algorithm evaluation and/or comparison results are necessarily only for a subset of all possible scenarios, and thus may not include scenarios relevant to particular use-cases. Consequently, it is difficult for Workflow Management Systems (WMSs) developers to decide which scheduling algorithm should be implemented. To obviate this difficulty, one possible approach is to implement a portfolio of scheduling algorithms and select the most effective algorithm at runtime. One method for performing this selection is to run an online simulation for each algorithm in the portfolio. The algorithm that leads to the best performance, in simulation, is selected for future use. The above simulation-driven portfolio scheduling (SDPS) approach has been proposed in a few parallel and distributed computing contexts. The main objective of this work is to evaluate the feasibility and potential merit of SDPS if implemented in WMSs. Here we perform this evaluation using simulated WMS executions, where the simulations are instantiated from real-world platform and workflow configurations. Our main finding is that SDPS is on par with or outperforms an approach in which a single algorithm is used, where this algorithm is the one that performs best on average across all our experimental scenarios. Furthermore, we find that SDPS remains an attractive proposition even in the presence of high levels of simulation error and for simulators with relatively low levels of sophistication. In many of our experimental scenarios we find that mitigating simulation error at runtime can further improve performance. Finally, we show that simulation overhead can be made sufficiently low for SDPS to be feasible in practice.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1987

Third Conference on Artificial Intelligence for Space Applications, part 1

The application of artificial intelligence to spacecraft and aerospace systems is discussed. Expert systems, robotics, space station automation, fault diagnostics, parallel processing, knowledge representation, scheduling, man-machine interfaces and neural nets are among the topics discussed.

Denton, Judith S.↗

NASA NTRS1994

Analysis of EDP performance

The objective of this contract was the investigation of the potential performance gains that would result from an upgrade of the Space Station Freedom (SSF) Data Management System (DMS) Embedded Data Processor (EDP) '386' design with the Intel Pentium (registered trade-mark of Intel Corp.) '586' microprocessor. The Pentium ('586') is the latest member of the industry standard Intel X86 family of CISC (Complex Instruction Set Computer) microprocessors. This contract was scheduled to run in parallel with an internal IBM Federal Systems Company (FSC) Internal Research and Development (IR&D) task that had the goal to generate a baseline flight design for an upgraded EDP using the Pentium. This final report summarizes the activities performed in support of Contract NAS2-13758. Our plan was to baseline performance analyses and measurements on the latest state-of-the-art commercially available Pentium processor, representative of the proposed space station design, and then phase to an IBM capital funded breadboard version of the flight design (if available from IR&D and Space Station work) for additional evaluation of results. Unfortunately, the phase-over to the flight design breadboard did not take place, since the IBM Data Management System (DMS) for the Space Station Freedom was terminated by NASA before the referenced capital funded EDP breadboard could be completed. The baseline performance analyses and measurements, however, were successfully completed, as planned, on the commercial Pentium hardware. The results of those analyses, evaluations, and measurements are presented in this final report.

Source record↗

NASA NTRS1998

Reusable Rocket Engine Operability Modeling and Analysis

This paper describes the methodology, model, input data, and analysis results of a reusable launch vehicle engine operability study conducted with the goal of supporting design from an operations perspective. Paralleling performance analyses in schedule and method, this requires the use of metrics in a validated operations model useful for design, sensitivity, and trade studies. Operations analysis in this view is one of several design functions. An operations concept was developed given an engine concept and the predicted operations and maintenance processes incorporated into simulation models. Historical operations data at a level of detail suitable to model objectives were collected, analyzed, and formatted for use with the models, the simulations were run, and results collected and presented. The input data used included scheduled and unscheduled timeline and resource information collected into a Space Transportation System (STS) Space Shuttle Main Engine (SSME) historical launch operations database. Results reflect upon the importance not only of reliable hardware but upon operations and corrective maintenance process improvements.

Christenson, R. L.↗

NASA NTRS2000

Comprehensive Design Reliability Activities for Aerospace Propulsion Systems

This technical publication describes the methodology, model, software tool, input data, and analysis result that support aerospace design reliability studies. The focus of these activities is on propulsion systems mechanical design reliability. The goal of these activities is to support design from a reliability perspective. Paralleling performance analyses in schedule and method, this requires the proper use of metrics in a validated reliability model useful for design, sensitivity, and trade studies. Design reliability analysis in this view is one of several critical design functions. A design reliability method is detailed and two example analyses are provided-one qualitative and the other quantitative. The use of aerospace and commercial data sources for quantification is discussed and sources listed. A tool that was developed to support both types of analyses is presented. Finally, special topics discussed include the development of design criteria, issues of reliability quantification, quality control, and reliability verification.

Christenson, R. L.↗

NASA NTRSDate not supplied

A Study of Parallel Scalability and Dynamic Workload Balancing in GlennICE

The Glenn Icing Computational Environment (GlennICE) is a computational tool designed to calculate ice growth on complex three-dimensional geometries. It utilizes user-supplied computational fluid dynamics solutions for the geometry of interest. Key developments include advancements in convergence of collection efficiency, trajectory optimization, and refinement methodology. These improvements have significantly enhanced GlennICE’s efficiency for practical engineering applications. A recent study focused on benchmarking GlennICE’s scalability in a parallel environment using static scheduling. Findings indicated a potential twofold increase in efficiency through workload balance enhancements. This paper presents an analysis of the solver’s new workload balancing improvements, incorporating shared memory and dynamic scheduling routines. Results demonstrate a highly efficient and consistent algorithm across high-performance computing clusters.

Computational Icing↗

NASA NTRSDate not supplied

A Study of Parallel Scalability and Dynamic Workload Balancing in GlennICE

Computational Icing↗

DOE OSTI2024

Enabling Low-Overhead HT-HPC Workflows at Extreme Scale using GNU Parallel

GNU Parallel is a versatile and powerful tool for process parallelization widely used in scientific computing. This paper demonstrates its effective application in high-performance computing (HPC) environments, particularly focusing on its scalability and efficiency in executing large-scale high-throughput high-performance computing (HT-HPC) workflows. Through real-world examples, we highlight GNU Parallel’s performance across various HPC workloads, including GPU computing, container-based workloads, and node-local NVMe storage. Our results on two leading supercomputers, OLCF’s Frontier and NERSC’s Perlmutter, showcase GNU Parallel’s rapid process dispatching ability and its capacity to maintain low overhead even at extreme scales. We explore GNU Parallel’s application in massive parallel file transfers using a scheduled Data Transfer Node (DTN) cluster, emphasizing its broad utility in diverse scientific workflows. Beyond its direct application as a viable workflow manager, GNU Parallel can be employed in conjunction with other workflow systems as a "last-mile" parallelizing driver and as a quick prototyping tool to design and extract parallel profiles from application executions. We then argue that the potential for GNU Parallel to transform workflow management at extreme scales is substantial, paving the way for more efficient and effective scientific discoveries.

Maheshwari, Ketan↗

NASA NTRS1993

Performance analysis of a large-grain dataflow scheduling paradigm

A paradigm for scheduling computations on a network of multiprocessors using large-grain data flow scheduling at run time is described and analyzed. The computations to be scheduled must follow a static flow graph, while the schedule itself will be dynamic (i.e., determined at run time). Many applications characterized by static flow exist, and they include real-time control and digital signal processing. With the advent of computer-aided software engineering (CASE) tools for capturing software designs in dataflow-like structures, macro-dataflow scheduling becomes increasingly attractive, if not necessary. For parallel implementations, using the macro-dataflow method allows the scheduling to be insulated from the application designer and enables the maximum utilization of available resources. Further, by allowing multitasking, processor utilizations can approach 100 percent while they maintain maximum speedup. Extensive simulation studies are performed on 4-, 8-, and 16-processor architectures that reflect the effects of communication delays, scheduling delays, algorithm class, and multitasking on performance and speedup gains.

Young, Steven D.↗

NASA NTRS1991

Intelligent systems technology infrastructure for integrated systems

Significant advances have occurred during the last decade in intelligent systems technologies (a.k.a. knowledge-based systems, KBS) including research, feasibility demonstrations, and technology implementations in operational environments. Evaluation and simulation data obtained to date in real-time operational environments suggest that cost-effective utilization of intelligent systems technologies can be realized for Automated Rendezvous and Capture applications. The successful implementation of these technologies involve a complex system infrastructure integrating the requirements of transportation, vehicle checkout and health management, and communication systems without compromise to systems reliability and performance. The resources that must be invoked to accomplish these tasks include remote ground operations and control, built-in system fault management and control, and intelligent robotics. To ensure long-term evolution and integration of new validated technologies over the lifetime of the vehicle, system interfaces must also be addressed and integrated into the overall system interface requirements. An approach for defining and evaluating the system infrastructures including the testbed currently being used to support the on-going evaluations for the evolutionary Space Station Freedom Data Management System is presented and discussed. Intelligent system technologies discussed include artificial intelligence (real-time replanning and scheduling), high performance computational elements (parallel processors, photonic processors, and neural networks), real-time fault management and control, and system software development tools for rapid prototyping capabilities.

Lum, Henry, Jr.↗

NASA NTRS2013

Unique Challenges Testing SDRs for Space

This paper describes the approach used by the Space Communication and Navigation (SCaN) Testbed team to qualify three Software Defined Radios (SDR) for operation in space and the characterization of the platform to enable upgrades on-orbit. The three SDRs represent a significant portion of the new technologies being studied on board the SCAN Testbed, which is operating on an external truss on the International Space Station (ISS). The SCaN Testbed provides experimenters an opportunity to develop and demonstrate experimental waveforms and applications for communication, networking, and navigation concepts and advance the understanding of developing and operating SDRs in space. Qualifying a Software Defined Radio for the space environment requires additional consideration versus a hardware radio. Tests that incorporate characterization of the platform to provide information necessary for future waveforms, which might exercise extended capabilities of the hardware, are needed. The development life cycle for the radio follows the software development life cycle, where changes can be incorporated at various stages of development and test. It also enables flexibility to be added with minor additional effort. Although this provides tremendous advantages, managing the complexity inherent in a software implementation requires a testing beyond the traditional hardware radio test plan. Due to schedule and resource limitations and parallel development activities, the subsystem testing of the SDRs at the vendor sites was primarily limited to typical fixed transceiver type of testing. NASA s Glenn Research Center (GRC) was responsible for the integration and testing of the SDRs into the SCaN Testbed system and conducting the investigation of the SDR to advance the technology to be accepted by missions. This paper will describe the unique tests that were conducted at both the subsystem and system level, including environmental testing, and present results. For example, test waveforms were developed to measure the gain of the transmit system across the tunable frequency band. These were used during thermal vacuum testing to enable characterization of the integrated system in the wide operational temperature range of space. Receive power indicators were used for Electromagnetic Interference tests (EMI) to understand the platform s susceptibility to external interferers independent of the waveform. Additional approaches and lessons learned during the SCaN Testbed subsystem and system level testing will be discussed that may help future SDR integrators

Chelmins, David↗

NASA NTRS2013

Unique Challenges Testing SDRs for Space

This paper describes the approach used by the Space Communication and Navigation (SCaN) Testbed team to qualify three Software Defined Radios (SDR) for operation in space and the characterization of the platform to enable upgrades on-orbit. The three SDRs represent a significant portion of the new technologies being studied on board the SCAN Testbed, which is operating on an external truss on the International Space Station (ISS). The SCaN Testbed provides experimenters an opportunity to develop and demonstrate experimental waveforms and applications for communication, networking, and navigation concepts and advance the understanding of developing and operating SDRs in space. Qualifying a Software Defined Radio for the space environment requires additional consideration versus a hardware radio. Tests that incorporate characterization of the platform to provide information necessary for future waveforms, which might exercise extended capabilities of the hardware, are needed. The development life cycle for the radio follows the software development life cycle, where changes can be incorporated at various stages of development and test. It also enables flexibility to be added with minor additional effort. Although this provides tremendous advantages, managing the complexity inherent in a software implementation requires a testing beyond the traditional hardware radio test plan. Due to schedule and resource limitations and parallel development activities, the subsystem testing of the SDRs at the vendor sites was primarily limited to typical fixed transceiver type of testing. NASA's Glenn Research Center (GRC) was responsible for the integration and testing of the SDRs into the SCaN Testbed system and conducting the investigation of the SDR to advance the technology to be accepted by missions. This paper will describe the unique tests that were conducted at both the subsystem and system level, including environmental testing, and present results. For example, test waveforms were developed to measure the gain of the transmit system across the tunable frequency band. These were used during thermal vacuum testing to enable characterization of the integrated system in the wide operational temperature range of space. Receive power indicators were used for Electromagnetic Interference tests (EMI) to understand the platform's susceptibility to external interferers independent of the waveform. Additional approaches and lessons learned during the SCaN Testbed subsystem and system level testing will be discussed that may help future SDR integrators.

transmitters receivers↗

NASA NTRS1995

Job Management Requirements for NAS Parallel Systems and Clusters

A job management system is a critical component of a production supercomputing environment, permitting oversubscribed resources to be shared fairly and efficiently. Job management systems that were originally designed for traditional vector supercomputers are not appropriate for the distributed-memory parallel supercomputers that are becoming increasingly important in the high performance computing industry. Newer job management systems offer new functionality but do not solve fundamental problems. We address some of the main issues in resource allocation and job scheduling we have encountered on two parallel computers - a 160-node IBM SP2 and a cluster of 20 high performance workstations located at the Numerical Aerodynamic Simulation facility. We describe the requirements for resource allocation and job management that are necessary to provide a production supercomputing environment on these machines, prioritizing according to difficulty and importance, and advocating a return to fundamental issues.

Saphir, William↗