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Uniform tasks distributed uniformly or else assigned to one processor. Investigation in theory of scheduling yielded optimal scheme for allocation of tasks among digital data processors in hypercube ensemble. Applies to tasks that require equal time to execute, performed in any order, and between any two of which equal amounts of communication required. Reduces overall processing time for given set of computational tasks.
A research program was defined that will explore the link between planning and execution systems. A simple scenario was defined in which a very capable off-line planning system interacts with the user and a smaller, less capable, on-line real-time system executing plans and reacting to faults. However, the on-line execution system may have a more flexible representation of the plans it is executing. This imbalance in the capabilities of the two agents involved should clarify some of the research objectives and give an experimental framework for the work. The task is to investigate the knowledge representations and communication protocols needed to link a user stating some requirements for a task to be carried out through a planning system to the (remote) execution agent that can carry out the user's wishes. The notion that a single representation can encapsulate the expression of the user's requirements, the capabilities for action, the communication to the execution agent, the successful or faulty response from the execution agent and the means of keeping the user informed, is examined. Methods of creating plan patches to update the plans separately held by each of the parties involved to keep them in step as they each react to changing circumstances in real-time is investigated. This involves the specification of plan patch attachment points that can be understood by the recipient. Transaction based methods are also investigated for coordinating the activities of the planner with those of the execution agent and user. The trial application area for the research is in the command and control of an advanced Earth Observation Space Platform.
An approach for managing Orion on-orbit burn execution is described for nominal and failure response scenarios. The burn management strategy for Orion takes into account per-burn variations in targeting, timing, and execution; crew and ground operator intervention and overrides; defined burn failure triggers and responses; and corresponding on-board software sequencing functionality. Burn-to- burn variations are managed through the identification of specific parameters that may be updated for each progressive burn. Failure triggers and automatic responses during the burn timeframe are defined to provide safety for the crew in the case of vehicle failures, along with override capabilities to ensure operational control of the vehicle. On-board sequencing software provides the timeline coordination for performing the required activities related to targeting, burn execution, and responding to burn failures.
Clean energy systems including the future power grid are enabled via the development, analysis and application of advanced simulation of complex systems based on large datasets. The ability to handle and enable the simulation of complex systems allows researchers and analysts to address significant problems with greater confidence and greater levels of integrity. Parallel execution of complex systems is paramount to addressing increasing complexity. Programmatic interfaces for software configuration and parallel execution enable handling greater complexity and improving simulation integrity. The Class Object Attribute Data (COAD) software is written as a programmatic interface to the PLEXOS dataset that enables the decomposition of the dataset in time and execution in parallel without loss of integrity. COAD also enable the recomposition of solution datasets that are the output of distributed parallel execution of the core simulator.
The purpose of the research effort is to develop and demonstrate innovative hardware to implement specific rollback and timing functions required for efficient queue management and precision timekeeping in multiprocessor discrete event simulations. The previously completed phase 1 effort demonstrated the technical feasibility of building hardware modules which eliminate the state saving overhead of the Time Warp paradigm used in distributed simulations on multiprocessor systems. The current phase 2 effort will build multiple pre-production rollback hardware modules integrated with a network of Sun workstations, and the integrated system will be tested by executing a Time Warp simulation. The rollback hardware will be designed to interface with the greatest number of multiprocessor systems possible. The authors believe that the rollback hardware will provide for significant speedup of large scale discrete event simulation problems and allow multiprocessors using Time Warp to dramatically increase performance.
The cost of executing a floating point operation has been decreasing for decades at a much higher rate than that of moving data. Bandwidth and latency, two key metrics that determine the cost of moving data, have degraded significantly relative to processor cycle time and execution rate. Despite the limitation of sub-micron processor technology and the end of Dennard scaling, this trend will continue in the short-term making data movement a performance-limiting factor and an energy/power efficiency concern. Even more so in the context of large-scale and data-intensive systems and workloads. This chapter gives an overview of the aspects of moving data across a system, from the storage system to the computing system down to the node and processor level, with case study and contributions from researchers at the San Diego Supercomputer Center, the Oak Ridge National Laboratory, the Pacific Northwest National Laboratory, and the University of Delaware.
The advanced engineering model of the DSIF tracking subsystem (DTS) is being developed by the Deep Space Instrumentation Facility. The DTS will provide effective and reliable tracking and data acquisition support for the complex planetary and interplanetary space flight missions planned for the 1970's. The nucleus of the subsystem is a Honeywell H832 digital computer. The design and capabilities of the real-time operating executive software are described.
While Monte Carlo Neutron Transport (MCNT) is near-embarrasingly parallel, the effectively unpredictable lifetime of neutrons can lead to divergence when MCNT is evaluated on GPUs. Divergence is the phenomenon of adjacent threads in a warp executing different control flow paths; on GPUS, it reduces performance because each work group may only execute one path at a time. The process of Thread Data Remapping (TDR) resolves these discrepancies by moving data across hardware such that data in the same warp will be processed through similar paths. A common issue among prior implementations of TDR is the synchronous nature of its remapping and processing cycles, which exhaustively sort data produced by prior processing passes and exhaustively evaluate the sorted data. In another work, we defined a method of remapping data through an asynchronous scheduler which allows for work to be stored in shared memory and deferred arbitrarily until that work is a viable option for low-divergence evaluation. This article surveys a wider set of cases, with the goal of characterizing performance trends across a more comprehensive set of parameters. These parameters include cross sections of scattering/capturing/fission, use of implicit capture, source neutron counts, simulation time spans, and tuned memory allocations. Across these cases, we have recorded minimum and average execution times, as well as a heuristically tuned near-optimal memory allocation size for both synchronous and asynchronous scheduling. Across the collected data, it is shown that the asynchronous method is faster and more memory efficient in the majority of cases, and that it requires less tuning to achieve competitive performance.
Equipment failures, operator errors, natural disasters and cyber-attacks can and have caused extended blackouts of the electric grid. Even though such events are rare, preparedness for them is critical because extended power outages endanger human lives, compromise national security, or result in economic losses of billions of dollars. Since most of the generating units cannot restart without connecting to an energized grid, the system operator relies on a few units with the ability to start autonomously, called Black Start (BS) units, to restore the power system. Allocating and maintaining these units is costly and can severely impact the restoration security and time. We formulate an optimization problem to optimally allocate BS units in the grid, while simultaneously optimizing over the restoration sequence. We extend existing optimal allocation models by including grid considerations such as active power nodal balance, transmission switching, nodal reactive power support and voltage limits. In order to aid the branch and bound tree that solves the resulting large scale mixed integer program, we propose a randomized heuristic that is executed multiple times in parallel on a high-performance computing environment to find feasible solutions. Here, we proceed to solve the IEEE-39, the IEEE-118, and a simplified WECC system with 225 nodes and 136 generators to near optimality.
Quandary numerically simulates and optimizes the time-evolution of open quantum systems. The underlying dynamics are modelled by Lindblad's master equation, a linear ordinary differential equation (ODE) describing quantum systems interacting with the environment. Quandary solves this ODE numerically by applying a time-stepping integration scheme, and utilizes a gradient-based optimization approach to determine optimal control pulses that drive the quantum system to a desired target state. Two optimization objectives are considered: (a) Unitary gate optimization that finds controls to realize a unitary gate transformation, and (b) optimal reset that aims to drive the quantum system to the ground states. Gradient-based optimization schemes utilizing Petsc's Tao optimization package are applied to generate control pulses that minimize the respective measure. To evaluate the gradient of the objective function, the discrete adjoint method is used while leveraging techniques from Algorithmic Differentiation to produce exact and consistent gradients. To mitigate excessive execution run times, the software can be build together with the XBraid software library which provides a parallelization strategy to distribute the time-evolution of the underlying dynamics onto multiple processor.
PyARC was initially developed as an open source tool to support fast reactor analyses using the Argonne Reactor Computation (ARC) code suite as a part of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Workbench initiative in FY17. The goal of this initiative is to provide a common user interface for model generation, real-time validation, execution, output processing, and visualization for all integrated codes. This is accomplished through the reliance on tools available in the Workbench framework and runtime environment. While initially developed to support the ARC codes, PyARC was extended in FY22 to wrap other NEAMS and non-ARC codes, including Griffin and OpenMC, in the supported other neutronics workflows, and support users in the adoption of NEAMS-supported high fidelity analysis codes. Most recently, NUBOW-3D, a recently adopted ARC code, was integrated to support reactor bowing calculations as well. Integration of these codes into the NEAMS Workbench directly benefits the advanced reactor modeling community by: • Providing a set of controlled, maintained, documented and validated scripts to generate inputs, which promotes best practices, reduces the learning curve, and facilitates project collaboration. • Improving the user experience: the Workbench interface provides assistance for building an input through auto-completion, real-time validation, document navigation, and geometry and results visualization. • Automating complex calculations and workflows for reactor analysis. • Helping users transition to using high-fidelity NEAMS codes along-side the ARC codes. In FY22, a progress report was published that described the state of each of the tools integrated into PyARC. Since then, there have been many enhancements and upgrades to the existing integrations as well as entirely new code integrations as well. This report details all new integrations and major developments in PyARC since the version 2.0.0 release highlighted in the FY22 report.
A new solar weather mission has been proposed, involving a dozen or more small spacecraft spaced at regular, constant intervals in a mutual heliocentric circular orbit between the orbits of Earth and Venus. These solar weather buoys (SWBs) would carry instrumentation to detect and measure the material in solar flares, solar energetic particle events, and coronal mass ejections as they flowed past the buoys, serving both as science probes and as a radiation early warning system for the Earth and interplanetary travelers to Mars. The baseline concept involves placing a mothercraft carrying the SWBs into a staging orbit at the Sun-Earth L1 libration point. The mothercraft departs the L1 orbit at the proper time to execute a trailing-edge lunar flyby near New Moon, injecting it into a heliocentric orbit with its perihelion interior to Earth s orbit. An alternative approach would involve the use of a Double Lunar Swingby (DLS) orbit, rather than the L1 orbit, for staging prior to this flyby. After injection into heliocentric orbit, the mothercraft releases the SWBs-all equipped with low-thrust pulsed plasma thrusters (PPTs)-whereupon each SWB executes a multi-day low-thrust finite bum around perihelion, lowering aphelion such that each achieves an elliptical phasing orbit of different orbital period from its companions. The resulting differences in angular rates of motion cause the spacecraft to separate. While the lead SWB achieves the mission orbit following an insertion burn at its second perihelion passage, the remaining SWBs must complete several revolutions in their respective phasing orbits to establish them in the mission orbit with the desired longitudinal spacing. The complete configuration for a 14 SWB scenario using a single mothercraft is achieved in about 8 years, and the spacing remains stable for at least a further 6 years. Flight operations can be simplified, and mission risk reduced, by employing two mothercraft instead of one. In this scenario: the second mothercraft stays in a libration-point or DLS staging orbit until the first mothercraft has achieved nearly 180 separation from the Earth. The timing of the second mothercraft's subsequent lunar flyby is planned such that this spacecraft will be located 180 from the first mothercraft upon completion of its heliocentric circularization maneuvers. Both groups of satellites then only have to spread out over 180 to obtain full 360 coverage around the Sun.
The Workbench initiative was launched in FY-2017 within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Integration Product Line to facilitate the transition from conventional tools to high-fidelity tools. The Workbench provides a common user interface for model creation, real-time validation, execution, output processing, and visualization for integrated codes. The integration of the Argonne Reactor Computation (ARC) suite of codes into the NEAMS Workbench was initiated in FY-2017. The ARC codes contain both legacy codes like DIF3D and REBUS-3 that were developed with over 30 years of experience, and newer NEAMS additions like MC 2 -3 and PERSENT. The ARC integration into the NEAMS Workbench interface relies on the PyARC module which handles the pre- and post-processing of the native ARC codes input, and the runtime environment. The PyARC module together with the NEAMS Workbench interface are both released under Open Source Software licenses. This report describes the ARC capabilities available with the Workbench at the end of FY-2018, and a tutorial is provided together with the automatically-generated code documentation. Integrating the ARC codes into the Workbench benefits directly the Advanced Reactor Technology (ART) Campaign by providing a set of controlled, maintained and validated scripts to generate ARC inputs, which promotes best practices, reduces the learning curve, and facilitates project collaboration. The second benefit from this project is to improve the user experience with the ARC codes: the Workbench interface provides assistance for building an input through auto-completion, real-time validation, access to templates, and visualization. Finally, the ARC integration into the NEAMS Workbench enables new modeling capabilities, those will be extremely useful for advanced reactor design and analyses. The PyARC module facilitates and automatizes complex calculations and workflows for reactor analysis enabling geometrical perturbations, cross-section update through depletion, etc. The Dakota/PyARC coupling in the Workbench was also demonstrated to enable mathematical optimization and sensitivity analysis/uncertainty quantification (SA/UQ) techniques with ARC neutronic simulations. For demonstration purposes, the ARC codes were used through the Workbench for solving the Sodium-cooled Fast Reactor Uncertainty Analysis in Modelling (SFR-UAM) benchmark problems. The ARC codes are currently used at ANL through the Workbench by nuclear engineers for SFR core design analyses. Future efforts will focus on continuously adding modeling capabilities available with the ARC codes, training new users to continue building some experience, and on integrating high-fidelity codes such as PROTEUS into the Workbench.
Internet of Things (IoT) platforms have been deployed in several domains to enhance efficiency of business process and improve productivity. Most IoT platforms comprise of heterogeneous software and hardware components which can potentially introduce security and privacy challenges. Blockchain technology has been proposed as one of the solutions to realize IoT security by leveraging the (a) Immutable ledger, (b) Decentralized architecture and (c) Strong cryptography primitives. However, integrating blockchain platforms with IoT based applications presents several challenges due to lack of (a) acceptable performance on resource-constrained devices, (b) high transaction throughput, (c) keyword-based search and retrieve, (d) transaction back pressure operations, and (e) real-time response. In this paper, we propose a lightweight blockchain platform, “Tikiri”, for resource-constrained IoT devices. Tikiri uses Apache Kafka for the consensus and proposes new blockchain architecture to handle real-time transaction execution on the blockchain. Tikiri is characterized by functional programming and actor-based smart contract platform that realizes concurrent execution of transactions in the blockchain. Tikiri realizes a lightweight and scalable blockchain that can provides performance on the resource-constrained IoT devices.
Reduced-order models (ROMs) are a widely used and powerful approach to reducing the complexity of predictive physics-based numerical simulations for a wide range of applications, including electronics and fluid mechanics such as geologic CO 2 sequestration (GCS). ROMs are critical for optimization, sensitivity analysis, model calibration and uncertainty quantification where full-order models cannot be feasibly executed many times. Traditional approaches generate a single ROM for each simulated response (e.g., CO 2 injection rates, pH changes) based on a set of training simulations. Here, we demonstrate that a single ROM can display excellent overall predictive statistics, but have predictions that dramatically and unacceptably deviate from simulator responses especially when the response variable has a large range (i.e., vary over multiple orders of magnitude). For example, we show that a traditional statistically-high-performing GCS ROM (coefficient of determination R 2 of 0.99) can have average absolute relative errors of over 200%. To address this, we propose a new and novel approach where a set of sub-ROMs are generated to overcome the potential pitfalls in traditional single ROM development. The effectiveness of the proposed approach—the ROMster framework—is demonstrated using a case study of predicted CO 2 injection rates for GCS. We find our approach is a robust and general framework for ROM development, reducing the average “error” from 200% to only 4% in our case study.
Here, extended Lagrangian Born–Oppenheimer molecular dynamics (XL-BOMD) in its most recent shadow potential energy version has been implemented in the semiempirical PyTorch-based software PySeQM. The implementation includes finite electronic temperatures, canonical density matrix perturbation theory, and an adaptive Krylov subspace approximation for the integration of the electronic equations of motion within the XL-BOMB approach (KSA-XL-BOMD). The PyTorch implementation leverages the use of GPU and machine learning hardware accelerators for the simulations. The new XL-BOMD formulation allows studying more challenging chemical systems with charge instabilities and low electronic energy gaps. The current public release of PySeQM continues our development of modular architecture for large-scale simulations employing semi-empirical quantum-mechanical treatment. Applied to molecular dynamics, simulation of 840 carbon atoms, one integration time step executes in 4 s on a single Nvidia RTX A6000 GPU.
Motivation: Sensitivity analysis and parameter tuning are important processes in large-scale image analysis. They are very costly because the image analysis workflows are required to be executed several times to systematically correlate output variations with parameter changes or to tune parameters. An integrated solution with minimum user interaction that uses effective methodologies and high performance computing is required to scale these studies to large imaging datasets and expensive analysis workflows. Results: The experiments with two segmentation workflows show that the proposed approach can (i) quickly identify and prune parameters that are non-influential; (ii) search a small fraction (about 100 points) of the parameter search space with billions to trillions of points and improve the quality of segmentation results (Dice and Jaccard metrics) by as much as 1.42 x compared to the results from the default parameters; (iii) attain good scalability on a high performance cluster with several effective optimizations. Conclusions: Our work demonstrates the feasibility of performing sensitivity analyses, parameter studies and auto-tuning with large datasets. The proposed framework can enable the quantification of error estimations and output variations in image segmentation pipelines.
System for converting DESIRE (Direct Executing Simulation in Real Time) simulation models to executable code models (C# / C++). These converted models are used as the basis for 2 products, one of which is the focus of this review: an engineering design interface for Fissile Solution Systems. ModelConverter.exe (C#) converts DESIRE model inputs to a C++ plug-in subclass. SimApp.exe (C# / C++) has a C++ dll that is a numerical integration engine that the Converted models plug into. The engineering design interface is a C# GUI that calls on the C++ dll to execute a particular converted model. The GUI allows the user to specify the length of a simulation, run the simulation, view data plots and data tables. Stability plots may also be produced and manipulated. The engineering design interface will be released as source code and executable; the model converter source is also intended for release.