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Munson, Todd

Publications and source records attributed to Munson, Todd.

At least 37 records · Page 2

ECP Software Technology Capability Assessment Report

The Exascale Computing Project (ECP) Software Technology (ST) Focus Area is responsible for developing critical software capabilities that will enable successful execution of ECP applications, and for providing key components of a productive and sustainable Exascale computing ecosystem that will position the US Department of Energy (DOE) and the broader high performance (HPC) community with a firm foundation for future extreme-scale computing capabilities. This ECP ST Capability Assessment Report (CAR) provides an overview and assessment of current ECP ST capabilities and activities, giving stakeholders and the broader HPC community information that can be used to assess ECP ST progress and plan their own efforts accordingly. ECP ST leaders commit to updating this document on regular basis (every six to 12 months).

97 MATHEMATICS AND COMPUTING↗

PyDDA: A Pythonic Direct Data Assimilation Framework for Wind Retrievals

This software assimilates data from an arbitrary number of weather radars together with other spatial wind fields (eg numerical weather forecasting model data) in order to retrieve high resolution three dimensional wind fields. PyDDA uses NumPy and SciPy’s optimization techniques combined with the Python Atmospheric Radiation Measurement (ARM) Radar Toolkit (Py-ART) in order to create wind fields using the 3D variational technique (3DVAR). PyDDA is hosted and distributed on GitHub at https://github.com/openradar/PyDDA. PyDDA has the potential to be used by the atmospheric science community to develop high resolution wind retrievals from radar networks. These retrievals can be used for the evaluation of numerical weather forecasting models and plume modelling. This paper shows how wind fields from 2 NEXt generation RADar (NEXRAD) WSR-88D radars and the High Resolution Rapid Refresh can be assimilated together using PyDDA to create a high resolution wind field inside Hurricane Florence.

54 ENVIRONMENTAL SCIENCES↗

2020 Exascale Computing Project Annual Meeting (Executive Summary Report)

The Exascale Computing Project (ECP) delivers specific applications, software products, and outcomes on DOE computing facilities. Integration across these elements for specific hardware technologies for exascale system instantiations is fundamental to ECP success. The outcome of the ECP is the delivery of a capable exascale computing ecosystem to provide breakthrough solutions addressing our most critical challenges in scientific discovery, energy assurance, economic competitiveness, and national security. This outcome is not a matter of ensuring more powerful computing systems. The ECP is designed to create more valuable and rapid insights from a wide variety of applications (“capable”), which requires a much higher level of inherent efficacy in all methods, software tools, and ECP-enabled computing technologies to be acquired by DOE laboratories (“ecosystem”). The ECP annual meeting provides a unique opportunity for the core technical expertise in the United States focused on achieving this next plateau of computational science and computing performance to engage in direct discussions on project execution. Face-to-face gatherings in technical communities like this are common and needed for the exchange of scientific ideas and technical performance. The ECP annual meeting stands apart from other technical conferences and meetings in the computing community as it is uniquely and solely focused on the execution of the ECP and the integration of technical activities leading to the creation of the exascale computing ecosystem for the future. The direct interaction of key critical technical staff, who are leaders in their respective fields, and the resulting give-and-take between software, applications, and hardware and the technical co-design therein, is unique and essential to the effective execution of the ECP. The first annual meeting was held in Knoxville, Tennessee, January 31 – February 2, 2017 and brought together, for the first time, a diverse collection of researchers from 16 DOE national laboratories as well as university computer and computational science researchers to discuss shared problems and joint solutions for the development of a capable exascale computing ecosystem. These interactions resulted in focused technical plans and an energized community centered on advances for ECP. The second annual meeting was held in Knoxville, Tennessee, February 5–9, 2018. It included 643 individual thought leaders and performers in application development, software research and deployment, and hardware research and integrators, all of whom are part of the multifaceted, billion dollar HPC community. This meeting provided a platform to discuss and disseminate numerous examples where researchers with common goals and synergistic solutions came together for the first time to deliver tangible results. Additionally, at the 2018 meeting, ECP researchers had the opportunity to digest all US HPC vendor R&D product roadmaps pointing to exascale – not only to learn how their research can play a role, but, more importantly, to influence those roadmaps to ensure successful delivery on DOE applications that will contribute to (if not solve) problems of national interest in national security, science, energy, and health, as well as growing security threats. The third annual meeting was held in Houston, Texas, January 14–17, 2019. With a 19% increase in the number of registrations (768 people), and the change in location, the third annual meeting was considered the most impactful of the three at the time. The new website provided a better platform for the dissemination of the content, the new venue as a meeting hotel instead of a conference center facilitated interactions and discussions after event hours, and the addition of an award-winning mobile event conference app (Whova) transformed dramatically the attendee experience at the event. This fourth annual meeting was held in Houston, Texas, February 3-7, 2020. This meeting had an increase in the number of attendees for a total of 824 people registered (782 attendees) and included numerous enhancements based on feedback and lessons learned from previous meetings, some of which are listed here: Improved quality of the sessions, their material and the whole program.; Had more industry participation and addition of external collaborators from overseas.; Published the full agenda earlier to better accommodate attendance and travel plans based on schedule.; Centralized all sessions in one venue.; Provided additional hotels and room blocks for the attendees.; Improved communication with the audience (links, material, directions, notifications, etc.) to go paperless.; Enhanced side meeting scheduling, management and user experience.; Made available additional space and tables for impromptu meetings and side discussions.; Improved IT and A/V solutions for speakers. In addition, our final survey captured the following points as opportunities for improvement in future meetings: consider a different meeting location that is more pedestrian friendly, reduce talks during working meals to allow more collaboration and informal time, adapt the agenda to acknowledge attendees from different timezones, consider recording some of the tutorials and/or sessions to share broadly with the HPC community, have a larger poster room, provide additional power strips, and improve the WiFi.

97 MATHEMATICS AND COMPUTING↗

A survey of nonlinear robust optimization

Robust optimization (RO) has attracted great attention from the optimization community over the past decade. RO is dedicated to solving optimization problems subject to uncertainty: design constraints must be satisfied for all the values of the uncertain parameters within a given uncertainty set. Uncertainty sets may be modeled as deterministic sets (boxes, polyhedra, ellipsoids), in which case the RO problem may be reformulated via worst-case analysis, or as families of distributions. The challenge of RO is to reformulate or approximate robust constraints so that the uncertain optimization problem is transformed into a tractable deterministic optimization problem. Most reformulation methods assume linearity of the robust constraints or uncertainty sets of favorable shape, which represents only a fraction of real-world applications. This survey addresses nonlinear RO and includes problem formulations and applications, solution approaches, and available software with code samples.

97 MATHEMATICS AND COMPUTING↗

ECP Software Technology Capability Assessment Report

The Exascale Computing Project (ECP) Software Technology (ST) Focus Area is responsible for developing critical software capabilities that will enable successful execution of ECP applications, and for providing key components of a productive and sustainable Exascale computing ecosystem that will position the US Department of Energy (DOE) and the broader high performance (HPC) community with a firm foundation for future extreme-scale computing capabilities. This ECP ST Capability Assessment Report (CAR) provides an overview and assessment of current ECP ST capabilities and activities, giving stakeholders and the broader HPC community information that can be used to assess ECP ST progress and plan their own efforts accordingly. ECP ST leaders commit to updating this document on regular basis (every six to 12 months).

97 MATHEMATICS AND COMPUTING↗

ECP Software Technology Capability Assessment Report

The Exascale Computing Project Software Technology (ECP ST) focus area represents the key bridge between Exascale systems and the scientists developing applications that will run on those platforms. ECP offers a unique opportunity to build a coherent set of software (often referred to as the "software stack") that will allow application developers to maximize their ability to write highly parallel applications, targeting multiple Exascale architectures with runtime environments that will provide high performance and resilience. But applications are only useful if they can provide scientific insight, and the unprecedented data produced by these applications require a complete analysis work ow that includes new technology to scalably collect, reduce, organize, curate, and analyze the data into actionable decisions. This requires approaching scientific computing in a holistic manner, encompassing the entire user workflow - from conception of a problem, setting up the problem with validated inputs, performing high-fidelity simulations, to the application of uncertainty quantification to the final analysis. The software stack plan defined here aims to address all of these needs by extending current technologies to Exascale where possible, by performing the research required to conceive of new approaches necessary to address unique problems where current approaches will not suffice, and by deploying high-quality and robust software products on the platforms developed in the Exascale systems project. The ECP ST portfolio has established a set of interdependent projects that will allow for the research, development, and delivery of a comprehensive software stack,

97 MATHEMATICS AND COMPUTING↗

A Codesign Framework for Online Data Analysis and Reduction

In this paper we discuss our design of a toolset for automating performance studies of composed HPC applications that perform online data reduction and analysis. We describe Cheetah, a new framework for performing parametric studies on coupled applications. Cheetah facilitates understanding the impact of various factors such as process placement, synchronicity of algorithms, and storage vs. compute requirements for online analysis of large data. Ultimately, we aim to create a catalog of performance results that can help scientists understand tradeoffs when designing next-generation simulations that make use of online processing techniques. We illustrate the design choices of Cheetah by using a reaction-diffusion simulation (Gray-Scott) paired with an analysis application to demonstrate initial results of fine-grained process placement on Summit, a pre-exascale supercomputer at Oak Ridge National Laboratory.

Mehta, Kshitij↗

PyDDA: A New Pythonic Wind Retrieval Package

PyDDA (Pythonic Direct Data Assimilation) is a new community framework aimed at wind retrievals that depends only upon utilities in the SciPy ecosystem such as scipy, numpy, and dask. It can support retrievals of winds using information from weather radar networks constrained by high resolution forecast models over grids that cover thousands of kilometers at kilometer-scale resolution. Unlike past wind retrieval packages, this package can be installed using anaconda for easy installation and, with a focus on ease of use can retrieve winds from gridded radar and model data with just a few lines of code. The package is currently available for download at https://github.com/openradar/PyDDA.

Radar↗

A Codesign Framework for Online Data Analysis and Reduction at the Exascale

In this paper we discuss our design of a toolset for automating performance studies of composed HPC applications that perform online data reduction and analysis. We describe Cheetah, a new framework for performing parametric studies on coupled applications. Cheetah facilitates understanding the impact of various factors such as process placement, synchronicity of algorithms, and storage vs. compute requirements for online analysis of large data. Ultimately, we aim to create a catalog of performance results that can help scientists understand tradeos when designing next-generation simulations that make use of online processing techniques. We illustrate the design choices of Cheetah by using a reaction-diusion simulation (Gray-Scott) paired with an analysis application to demonstrate initial results of fine-grained process placement on Summit, a pre-exascale supercomputer at Oak Ridge National Laboratory

Cheetah↗

Coupling Exascale Multiphysics Applications: Methods and Lessons Learned

With the growing computational complexity of science and the complexity of new and emerging hardware, it is time to re-evaluate the traditional monolithic design of computational codes. One new paradigm is constructing larger scientific computational experiments from the coupling of multiple individual scientific applications, each targeting their own physics, characteristic lengths, and/or scales. We present a framework constructed by leveraging capabilities such as in-memory communications, workflow scheduling on HPC resources, and continuous performance monitoring. This code coupling capability is demonstrated by a fusion science scenario, where differences between the plasma at the edges and at the core of a device have different physical descriptions. This infrastructure not only enables the coupling of the physics components, but it also connects in situ or online analysis, compression, and visualization that accelerate the time between a run and the analysis of the science content. Results from runs on Titan and Cori are presented as a demonstration.

Choi, Jong Youl↗

TAO 3.10 Users Manual

The Toolkit for Advanced Optimization (TAO) focuses on the development of algorithms and software for the solution of large-scale optimization problems on high-performance architectures. Areas of interest include unconstrained and bound-constrained optimization, nonlinear least squares problems, optimization problems with partial differential equation constraints, and variational inequalities and complementarity constraints.

97 MATHEMATICS AND COMPUTING↗

Coordinated platooning with multiple speeds

In a platoon, vehicles travel one after another with small intervehicle distances; trailing vehicles in a platoon save fuel because they experience less aerodynamic drag. This work presents a coordinated platooning model with multiple speed options that integrates scheduling, routing, speed selection, and platoon formation/dissolution in a mixed-integer linear program that minimizes the total fuel consumed by a set of vehicles while traveling between their respective origins and destinations. The performance of this model is numerically tested on a grid network and the Chicago-area highway network. We find that the fuel-savings factor of a multivehicle system significantly depends on the time each vehicle is allowed to stay in the network; this time affects vehicles’ available speed choices, possible routes, and the amount of time for coordinating platoon formation. For problem instances with a large number of vehicles, we propose and test a heuristic decomposed approach that applies a clustering algorithm to partition the set of vehicles and then routes each group separately. When the set of vehicles is large and the available computational time is small, the decomposed approach finds significantly better solutions than does the full model.

33 ADVANCED PROPULSION SYSTEMS↗

Exacution: Enhancing Scientific Data Management for Exascale

As we continue toward exascale, scientific data volume is continuing to scale and becoming more burdensome to manage. In this paper, we lay out opportunities to enhance state of the art data management techniques. We emphasize well-principled data compression, and using it to achieve progressive refinement. This can both accelerate I/O and afford the user increased flexibility when she interacts with the data. The formulation naturally maps onto enabling partitioning of the progressively improving-quality representations of a data quantity into different media-type destinations, to keep the highest priority information as close as possible to the computation, and take advantage of deepening memory/storage hierarchies in ways not previously possible. Careful monitoring is requisite to our vision, not only to verify that compression has not eliminated salient features in the data, but also to better understand the performance of massively parallel scientific applications. Increased mathematical rigor would be ideal,to help bring compression on a better-understood theoretical footing, closer to the relevant scientific theory, more aware of constraints imposed by the science, and more tightly error-controlled. Throughout, we highlight pathfinding research we have begun exploring related these topics, and comment toward future work that will be needed.

97 MATHEMATICS AND COMPUTING↗

Maximization of Platoon Formation Through Centralized Routing and Departure Time Coordination

Platooning allows vehicles to travel with a small intervehicle distance in a coordinated fashion because of vehicle-to-vehicle connectivity. When applied at a larger scale, platooning creates significant opportunities for energy savings because of reduced aerodynamic drag, as well as increased road capacity and a reduction in congestion resulting from shorter vehicle headways. These potential savings are maximized, however, if platooning-capable vehicles spend most of their travel time within platoons. Ad hoc platoon formation may not ensure a high rate of platoon driving. This paper considers here the problem of central coordination of platooning-capable vehicles. Coordination of their routes and departure times can maximize the fuel savings afforded by platooning vehicles. The resulting problem is a combinatorial optimization problem that considers the platoon coordination and vehicle routing problems simultaneously. The methodology is demonstrated through evaluation of the benefits of a coordinated solution and comparison with the uncoordinated case when platoons form only in an ad hoc manner. The coordinated and uncoordinated scenarios are compared on a grid network with various assumptions about demand and the time vehicles are willing to wait.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Optimal Execution of Co-analysis for Large-scale Molecular Dynamics Simulations

The analysis of scientific simulation data enables scientists to derive insights from their simulations. This analysis of the simulation output can be performed at the same execution site as the simulation using the same resources or can be done at a different site. The optimal output frequency is challenging to decide and is often chosen empirically. We propose a mathematical formulation for choosing the optimal frequency of data transfer for analysis and the feasibility of performing the analysis, under the given resource constraints such as network bandwidth, disk space, available memory, and computation time. We propose formulations for two cases of co-analysis -- local and remote. We consider various analyses features such as computation time, input data and memory requirement, importance of the analysis and minimum frequency required for performing the analysis. We demonstrate the effectiveness of our approach using molecular dynamics applications on the Mira and Edison supercomputers.

analysis↗

Single-view phase retrieval of an extended sample by exploiting edge detection and sparsity

We propose a new approach to robustly retrieve the exit wave of an extended sample from its coherent diffraction pattern by exploiting sparsity of the sample's edges. This approach enables imaging of an extended sample with a single view, without ptychography. We introduce nonlinear optimization methods that promote sparsity, and we derive update rules to robustly recover the sample's exit wave. We test these methods on simulated samples by varying the sparsity of the edge-detected representation of the exit wave. Finally, our tests illustrate the strengths and limitations of the proposed method in imaging extended samples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Coordinated Platoon Routing in a Metropolitan Network

Platooning vehicles—connected and automated vehicles traveling with small intervehicle distances—use less fuel because of reduced aerodynamic drag. Given a network de- fined by vertex and edge sets and a set of vehicles with origin/destination nodes/times, we model and solve the combinatorial optimization problem of coordinated routing of vehicles in a manner that routes them to their destination on time while using the least amount of fuel. Common approaches decompose the platoon coordination and vehicle routing into separate problems. Our model addresses both problems simultaneously to obtain the best solution. We use modern modeling techniques and constraints implied from analyzing the platoon routing problem to address larger numbers of vehicles and larger networks than previously considered. While the numerical method used is unable to certify optimality for candidate solutions to all networks and parameters considered, we obtain excellent solutions in approximately one minute for much larger networks and vehicle sets than previously considered in the literature.

Larson, Jeffrey↗