Scalability in the Presence of Variability

Supercomputers are used to solve some of the world’s most computationally demanding problems. Exascale systems, to be comprised of over one million cores and capable of 10^18 floating point operations per second, will probably exist by the early 2020s, and will provide unprecedented computational power for parallel computing workloads. Unfortunately, while these machines hold tremendous promise and opportunity for applications in High Performance Computing (HPC), graph processing, and machine learning, it will be a major challenge to fully realize their potential, because to do so requires balanced execution across the entire system and its millions of processing elements. When different processors take different amounts of time to perform the same amount of work, performance imbalance arises, large portions of the system sit idle, and time and energy are wasted. Larger systems incorporate more processors and thus greater opportunity for imbalance to arise, as well as larger performance/energy penalties when it does. This phenomenon is referred to as performance variability and is the focus of this dissertation. In this dissertation, we explain how to design system software to mitigate variability on large scale parallel machines. Our approaches span (1) the design, implementation, and evaluation of a new high performance operating system to reduce some classes of performance variability, (2) a new performance evaluation framework to holistically characterize key features of variability on new and emerging architectures, and (3) a distributed modeling framework that derives predictions of how and where imbalance is manifesting in order to drive reactive operations such as load balancing and speed scaling. Collectively, these efforts provide a holistic set of tools to promote scalability through the mitigation of variability

System-Level Support for Composition of Applications

ABSTRACT Current HPC system software lacks support for emerging application deployment scenarios that combine one or more simulations with in situ analytics, sometimes called multi-component or multi-enclave applications. This paper presents an initial design study, implementation, and evaluation of mechanisms supporting composite multi-enclave applications in the Hobbes exascale operating system. These mechanisms include virtualization techniques isolating application custom enclaves while using the vendor-supplied host operating system and high-performance inter-VM communication mechanisms. Our initial single-node performance evaluation of these mechanisms on multi-enclave science applications, both real and proxy, demonstrate the ability to support multi-enclave HPC job composition with minimal performance overhead

Scheduling Heterogeneous HPC Applications in Next-Generation Exascale Systems

Next generation HPC applications will increasingly time-share system resources with emerging workloads such as in-situ analytics, resilience tasks, runtime adaptation services and power management activities. HPC systems must carefully schedule these co-located codes in order to reduce their impact on application performance. Among the techniques traditionally used to mitigate the performance effects of time- share systems is gang scheduling. This approach, however, leverages global synchronization and time agreement mechanisms that will become hard to support as systems increase in size. Alternative performance interference mitigation approaches must be explored for future HPC systems. This dissertation evaluates the impacts of workload concurrency in future HPC systems. It uses simulation and modeling techniques to study the performance impacts of existing and emerging interference sources on a selection of HPC benchmarks, mini-applications, and applications. It also quantifies the cost and benefits of different approaches to scheduling co-located workloads, studies performance interference mitigation solutions based on gang scheduling, and examines their synchronization requirements. To do so, this dissertation presents and leverages a new Extreme Value Theory- based model to characterize interference sources, and investigate their impact on Bulk Synchronous Parallel (BSP) applications. It demonstrates how this model can be used to analyze the interference attenuation effects of alternative fine-grained OS scheduling approaches based on periodic real time schedulers. This analysis can, in turn, guide the design of those mitigation techniques by providing tools to understand the tradeoffs of selecting scheduling parameters

Vision 2040: A Roadmap for Integrated, Multiscale Modeling and Simulation of Materials and Systems

Over the last few decades, advances in high-performance computing, new materials characterization methods, and, more recently, an emphasis on integrated computational materials engineering (ICME) and additive manufacturing have been a catalyst for multiscale modeling and simulation-based design of materials and structures in the aerospace industry. While these advances have driven significant progress in the development of aerospace components and systems, that progress has been limited by persistent technology and infrastructure challenges that must be overcome to realize the full potential of integrated materials and systems design and simulation modeling throughout the supply chain. As a result, NASA's Transformational Tools and Technology (TTT) Project sponsored a study (performed by a diverse team led by Pratt & Whitney) to define the potential 25-year future state required for integrated multiscale modeling of materials and systems (e.g., load-bearing structures) to accelerate the pace and reduce the expense of innovation in future aerospace and aeronautical systems. This report describes the findings of this 2040 Vision study (e.g., the 2040 vision state; the required interdependent core technical work areas, Key Element (KE); identified gaps and actions to close those gaps; and major recommendations) which constitutes a community consensus document as it is a result of over 450 professionals input obtain via: 1) four society workshops (AIAA, NAFEMS, and two TMS), 2) community-wide survey, and 3) the establishment of 9 expert panels (one per KE) consisting on average of 10 non-team members from academia, government and industry to review, update content, and prioritize gaps and actions. The study envisions the development of a cyber-physical-social ecosystem comprised of experimentally verified and validated computational models, tools, and techniques, along with the associated digital tapestry, that impacts the entire supply chain to enable cost-effective, rapid, and revolutionary design of fit-for-purpose materials, components, and systems. Although the vision focused on aeronautics and space applications, it is believed that other engineering communities (e.g., automotive, biomedical, etc.) can benefit as well from the proposed framework with only minor modifications. Finally, it is TTT's hope and desire that this vision provides the strategic guidance to both public and private research and development decision makers to make the proposed 2040 vision state a reality and thereby provide a significant advancement in the United States global competitiveness

Parallel Multiscale Contact Dynamics for Rigid Non-spherical Bodies

The simulation of large numbers of rigid bodies of non-analytical shapes or vastly varying sizes which collide with each other is computationally challenging. The fundamental problem is the identification of all contact points between all particles at every time step. In the Discrete Element Method (DEM), this is particularly difficult for particles of arbitrary geometry that exhibit sharp features (e.g. rock granulates). While most codes avoid non-spherical or non-analytical shapes due to the computational complexity, we introduce an iterative-based contact detection method for triangulated geometries. The new method is an improvement over a naive brute force approach which checks all possible geometric constellations of contact and thus exhibits a lot of execution branching. Our iterative approach has limited branching and high floating point operations per processed byte. It thus is suitable for modern Single Instruction Multiple Data (SIMD) CPU hardware. As only the naive brute force approach is robust and always yields a correct solution, we propose a hybrid solution that combines the best of the two worlds to produce fast and robust contacts. In terms of the DEM workflow, we furthermore propose a multilevel tree-based data structure strategy that holds all particles in the domain on multiple scales in grids. Grids reduce the total computational complexity of the simulation. The data structure is combined with the DEM phases to form a single touch tree-based traversal that identifies both contact points between particle pairs and introduces concurrency to the system during particle comparisons in one multiscale grid sweep. Finally, a reluctant adaptivity variant is introduced which enables us to realise an improved time stepping scheme with larger time steps than standard adaptivity while we still minimise the grid administration overhead. Four different parallelisation strategies that exploit multicore architectures are discussed for the triad of methodological ingredients. Each parallelisation scheme exhibits unique behaviour depending on the grid and particle geometry at hand. The fusion of them into a task-based parallelisation workflow yields promising speedups. Our work shows that new computer architecture can push the boundary of DEM computability but this is only possible if the right data structures and algorithms are chosen

Proceedings, MSVSCC 2015

The Virginia Modeling, Analysis and Simulation Center (VMASC) of Old Dominion University hosted the 2015 Modeling, Simulation, & Visualization Student capstone Conference on April 16th. The Capstone Conference features students in Modeling and Simulation, undergraduates and graduate degree programs, and fields from many colleges and/or universities. Students present their research to an audience of fellow students, faculty, judges, and other distinguished guests. For the students, these presentations afford them the opportunity to impart their innovative research to members of the M&S community from academic, industry, and government backgrounds. Also participating in the conference are faculty and judges who have volunteered their time to impart direct support to their students’ research, facilitate the various conference tracks, serve as judges for each of the tracks, and provide overall assistance to this conference. 2015 marks the ninth year of the VMASC Capstone Conference for Modeling, Simulation and Visualization. This year our conference attracted a number of fine student written papers and presentations, resulting in a total of 51 research works that were presented. This year’s conference had record attendance thanks to the support from the various different departments at Old Dominion University, other local Universities, and the United States Military Academy, at West Point. We greatly appreciated all of the work and energy that has gone into this year’s conference, it truly was a highly collaborative effort that has resulted in a very successful symposium for the M&S community and all of those involved. Below you will find a brief summary of the best papers and best presentations with some simple statistics of the overall conference contribution. Followed by that is a table of contents that breaks down by conference track category with a copy of each included body of work. Thank you again for your time and your contribution as this conference is designed to continuously evolve and adapt to better suit the authors and M&S supporters. Dr.Yuzhong Shen Graduate Program Director, MSVE Capstone Conference Chair John ShullGraduate Student, MSVE Capstone Conference Student Chai

Scalable Observation, Analysis, and Tuning for Parallel Portability in HPC

It is desirable for general productivity that high-performance computing applications be portable to new architectures, or can be optimized for new workflows and input types, without the need for costly code interventions or algorithmic re-writes. Parallel portability programming models provide the potential for high performance and productivity, however they come with a multitude of runtime parameters that can have significant impact on execution performance. Selecting the optimal set of parameters, so that HPC applications perform well in different system environments and on different input data sets, is not trivial.This dissertation maps out a vision for addressing this parallel portability challenge, and then demonstrates this plan through an effective combination of observability, analysis, and in situ machine learning techniques. A platform for general-purpose observation in HPC contexts is investigated, along with support for its use in human-in-the-loop performance understanding and analysis. The dissertation culminates in a demonstration of lessons learned in order to provide automated tuning of HPC applications utilizing parallel portability frameworks

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

No abstract available

A geo-informatics approach to sustainability assessments of floatovoltaic technology in South African agricultural applications

South African project engineers recently pioneered the first agricultural floating solar photovoltaic tech nology systems in the Western Cape wine region. This effort prepared our country for an imminent large scale diffusion of this exciting new climate solver technology. However, hydro-embedded photovoltaic sys tems interact with environmentally sensitive underlying aquatic ecosystems, causing multiple project as sessment uncertainties (energy, land, air, water) compared to ground-mounted photovoltaics. The dissimi lar behaviour of floatovoltaic technologies delivers a broader and more diversified range of technical advan tages, environmental offset benefits, and economic co-benefits, causing analytical modelling imperfections and tooling mismatches in conventional analytical project assessment techniques. As a universal interna tional real-world problem of significance, the literature review identified critical knowledge and methodology gaps as the primary causes of modelling deficiencies and assessment uncertainties. By following a design thinking methodology, the thesis views the sustainability assessment and modelling problem through a geo graphical information systems lens, thus seeing an academic research opportunity to fill critical knowledge gaps through new theory formulation and geographical knowledge creation. To this end, this philosophi cal investigation proposes a novel object-oriented systems-thinking and climate modelling methodology to study the real-world geospatial behaviour of functioning floatovoltaic systems from a dynamical system thinking perspective. As an empirical feedback-driven object-process methodology, it inspired the thesis to create new knowledge by postulating a new multi-disciplinary sustainability theory to holistically characterise agricultural floatovoltaic projects through ecosystems-based quantitative sustainability profiling criteria. The study breaks new ground at the frontiers of energy geo-informatics by conceptualising a holistic theoretical framework designed for the theoretical characterisation of floatovoltaic technology ecosystem operations in terms of the technical energy, environmental and economic (3E) domain responses. It campaigns for a fully coupled model in ensemble analysis that advances the state-of-the-art by appropriating the 3E theo retical framework as underpinning computer program logic blueprint to synthesise the posited theory in a digital twin simulation. Driven by real-world geo-sensor data, this geospatial digital twin can mimic the geo dynamical behaviour of floatovoltaics through discrete-time computer simulations in real-time and lifetime digital project enactment exercises. The results show that the theoretical 3E framing enables project due diligence and environmental impact assessment reporting as it uniquely incorporates balanced scorecard performance metrics, such as the water-energy-land-food resource impacts, environmental offset benefits and financial feasibility of floatovoltaics. Embedded in a geoinformatics decision-support platform, the 3E theory, framework and model enable numerical project decision-supporting through an analytical hierarchy process. The experimental results obtained with the digital twin model and decision support system show that the desktop-based parametric floatovoltaic synthesis toolset can uniquely characterise the broad and diverse spectrum of performance benefits of floatovoltaics in a 3E sustainability profile. The model uniquely predicts important impact aspects of the technology’s land, air and water preservation qualities, quantifying these impacts in terms of the water, energy, land and food nexus parameters. The proposed GIS model can quantitatively predict most FPV technology unknowns, thus solving a contemporary real-world prob lem that currently jeopardises floating PV project licensing and approvals. Overall, the posited theoretical framework, methodology model, and reported results provide an improved understanding of floating PV renewable energy systems and their real-world behaviour. Amidst a rapidly growing international interest in floatovoltaic solutions, the research advances fresh philosophical ideas with novel theoretical principles that may have far-reaching implications for developing electronic, photovoltaic performance models worldwide.GeographyPh. D. (Geography