%0 Generic %D 2024 %T XaaS: Acceleration as a Service to Enable Productive High-Performance Cloud Computing %A Torsten Hoefler %A Marcin Copik %A Pete Beckman %A Andrew Jones %A Ian Foster %A Manish Parashar %A Daniel Reed %A Matthias Troyer %A Thomas Schulthess %A Dan Ernst %A Jack Dongarra %X HPC and Cloud have evolved independently, specializing their innovations into performance or productivity. Acceleration as a Service (XaaS) is a recipe to empower both fields with a shared execution platform that provides transparent access to computing resources, regardless of the underlying cloud or HPC service provider. Bridging HPC and cloud advancements, XaaS presents a unified architecture built on performance-portable containers. Our converged model concentrates on low-overhead, high-performance communication and computing, targeting resource-intensive workloads from climate simulations to machine learning. XaaS lifts the restricted allocation model of Function-as-a-Service (FaaS), allowing users to benefit from the flexibility and efficient resource utilization of serverless while supporting long-running and performance-sensitive workloads from HPC. %I arXiv %8 2024-01 %G eng %U https://arxiv.org/abs/2401.04552 %0 Book Section %B Fog Computing: Theory and Practice %D 2020 %T Harnessing the Computing Continuum for Programming Our World %A Pete Beckman %A Jack Dongarra %A Nicola Ferrier %A Geoffrey Fox %A Terry Moore %A Dan Reed %A Micah Beck %X This chapter outlines a vision for how best to harness the computing continuum of interconnected sensors, actuators, instruments, and computing systems, from small numbers of very large devices to large numbers of very small devices. The hypothesis is that only via a continuum perspective one can intentionally specify desired continuum actions and effectively manage outcomes and systemic properties—adaptability and homeostasis, temporal constraints and deadlines—and elevate the discourse from device programming to intellectual goals and outcomes. Development of a framework for harnessing the computing continuum would catalyze new consumer services, business processes, social services, and scientific discovery. Realizing and implementing a continuum programming model requires balancing conflicting constraints and translating the high‐level specification into a form suitable for execution on a unifying abstract machine model. In turn, the abstract machine must implement the mapping of specification demands to end‐to‐end resources. %B Fog Computing: Theory and Practice %I John Wiley & Sons, Inc. %@ 9781119551713 %G eng %& 7 %R https://doi.org/10.1002/9781119551713.ch7 %0 Generic %D 2019 %T BDEC2 Platform White Paper %A Todd Gamblin %A Pete Beckman %A Kate Keahey %A Kento Sato %A Masaaki Kondo %A Gerofi Balazs %B Innovative Computing Laboratory Technical Report %I University of Tennessee %8 2019-09 %G eng %0 Journal Article %J The International Journal of High Performance Computing Applications %D 2018 %T Big Data and Extreme-Scale Computing: Pathways to Convergence - Toward a Shaping Strategy for a Future Software and Data Ecosystem for Scientific Inquiry %A Mark Asch %A Terry Moore %A Rosa M. Badia %A Micah Beck %A Pete Beckman %A Thierry Bidot %A François Bodin %A Franck Cappello %A Alok Choudhary %A Bronis R. de Supinski %A Ewa Deelman %A Jack Dongarra %A Anshu Dubey %A Geoffrey Fox %A Haohuan Fu %A Sergi Girona %A Michael Heroux %A Yutaka Ishikawa %A Kate Keahey %A David Keyes %A William T. Kramer %A Jean-François Lavignon %A Yutong Lu %A Satoshi Matsuoka %A Bernd Mohr %A Stéphane Requena %A Joel Saltz %A Thomas Schulthess %A Rick Stevens %A Martin Swany %A Alexander Szalay %A William Tang %A Gaël Varoquaux %A Jean-Pierre Vilotte %A Robert W. Wisniewski %A Zhiwei Xu %A Igor Zacharov %X Over the past four years, the Big Data and Exascale Computing (BDEC) project organized a series of five international workshops that aimed to explore the ways in which the new forms of data-centric discovery introduced by the ongoing revolution in high-end data analysis (HDA) might be integrated with the established, simulation-centric paradigm of the high-performance computing (HPC) community. Based on those meetings, we argue that the rapid proliferation of digital data generators, the unprecedented growth in the volume and diversity of the data they generate, and the intense evolution of the methods for analyzing and using that data are radically reshaping the landscape of scientific computing. The most critical problems involve the logistics of wide-area, multistage workflows that will move back and forth across the computing continuum, between the multitude of distributed sensors, instruments and other devices at the networks edge, and the centralized resources of commercial clouds and HPC centers. We suggest that the prospects for the future integration of technological infrastructures and research ecosystems need to be considered at three different levels. First, we discuss the convergence of research applications and workflows that establish a research paradigm that combines both HPC and HDA, where ongoing progress is already motivating efforts at the other two levels. Second, we offer an account of some of the problems involved with creating a converged infrastructure for peripheral environments, that is, a shared infrastructure that can be deployed throughout the network in a scalable manner to meet the highly diverse requirements for processing, communication, and buffering/storage of massive data workflows of many different scientific domains. Third, we focus on some opportunities for software ecosystem convergence in big, logically centralized facilities that execute large-scale simulations and models and/or perform large-scale data analytics. We close by offering some conclusions and recommendations for future investment and policy review. %B The International Journal of High Performance Computing Applications %V 32 %P 435–479 %8 2018-07 %G eng %N 4 %R https://doi.org/10.1177/1094342018778123 %0 Journal Article %J IEEE Transactions on Parallel and Distributed Systems %D 2017 %T Argobots: A Lightweight Low-Level Threading and Tasking Framework %A Sangmin Seo %A Abdelhalim Amer %A Pavan Balaji %A Cyril Bordage %A George Bosilca %A Alex Brooks %A Philip Carns %A Adrian Castello %A Damien Genet %A Thomas Herault %A Shintaro Iwasaki %A Prateek Jindal %A Sanjay Kale %A Sriram Krishnamoorthy %A Jonathan Lifflander %A Huiwei Lu %A Esteban Meneses %A Mar Snir %A Yanhua Sun %A Kenjiro Taura %A Pete Beckman %K Argobots %K context switch %K I/O %K interoperability %K lightweight %K MPI %K OpenMP %K stackable scheduler %K tasklet %K user-level thread %X In the past few decades, a number of user-level threading and tasking models have been proposed in the literature to address the shortcomings of OS-level threads, primarily with respect to cost and flexibility. Current state-of-the-art user-level threading and tasking models, however, are either too specific to applications or architectures or are not as powerful or flexible. In this paper, we present Argobots, a lightweight, low-level threading and tasking framework that is designed as a portable and performant substrate for high-level programming models or runtime systems. Argobots offers a carefully designed execution model that balances generality of functionality with providing a rich set of controls to allow specialization by the user or high-level programming model. We describe the design, implementation, and optimization of Argobots and present integrations with three example high-level models: OpenMP, MPI, and co-located I/O service. Evaluations show that (1) Argobots outperforms existing generic threading runtimes; (2) our OpenMP runtime offers more efficient interoperability capabilities than production OpenMP runtimes do; (3) when MPI interoperates with Argobots instead of Pthreads, it enjoys reduced synchronization costs and better latency hiding capabilities; and (4) I/O service with Argobots reduces interference with co-located applications, achieving performance competitive with that of the Pthreads version. %B IEEE Transactions on Parallel and Distributed Systems %8 2017-10 %G eng %U http://ieeexplore.ieee.org/document/8082139/ %R 10.1109/TPDS.2017.2766062 %0 Journal Article %J International Journal of High Performance Computing %D 2011 %T The International Exascale Software Project Roadmap %A Jack Dongarra %A Pete Beckman %A Terry Moore %A Patrick Aerts %A Giovanni Aloisio %A Jean-Claude Andre %A David Barkai %A Jean-Yves Berthou %A Taisuke Boku %A Bertrand Braunschweig %A Franck Cappello %A Barbara Chapman %A Xuebin Chi %A Alok Choudhary %A Sudip Dosanjh %A Thom Dunning %A Sandro Fiore %A Al Geist %A Bill Gropp %A Robert Harrison %A Mark Hereld %A Michael Heroux %A Adolfy Hoisie %A Koh Hotta %A Zhong Jin %A Yutaka Ishikawa %A Fred Johnson %A Sanjay Kale %A Richard Kenway %A David Keyes %A Bill Kramer %A Jesus Labarta %A Alain Lichnewsky %A Thomas Lippert %A Bob Lucas %A Barney MacCabe %A Satoshi Matsuoka %A Paul Messina %A Peter Michielse %A Bernd Mohr %A Matthias S. Mueller %A Wolfgang E. Nagel %A Hiroshi Nakashima %A Michael E. Papka %A Dan Reed %A Mitsuhisa Sato %A Ed Seidel %A John Shalf %A David Skinner %A Marc Snir %A Thomas Sterling %A Rick Stevens %A Fred Streitz %A Bob Sugar %A Shinji Sumimoto %A William Tang %A John Taylor %A Rajeev Thakur %A Anne Trefethen %A Mateo Valero %A Aad van der Steen %A Jeffrey Vetter %A Peg Williams %A Robert Wisniewski %A Kathy Yelick %X Over the last 20 years, the open-source community has provided more and more software on which the world’s high-performance computing systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. However, although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has also been lost because of the lack of planning, coordination, and key integration of technologies necessary to make them work together smoothly and efficiently, both within individual petascale systems and between different systems. It seems clear that this completely uncoordinated development model will not provide the software needed to support the unprecedented parallelism required for peta/ exascale computation on millions of cores, or the flexibility required to exploit new hardware models and features, such as transactional memory, speculative execution, and graphics processing units. This report describes the work of the community to prepare for the challenges of exascale computing, ultimately combing their efforts in a coordinated International Exascale Software Project. %B International Journal of High Performance Computing %V 25 %P 3-60 %8 2011-01 %G eng %R https://doi.org/10.1177/1094342010391989 %0 Generic %D 2010 %T International Exascale Software Project Roadmap v1.0 %A Jack Dongarra %A Pete Beckman %B University of Tennessee Computer Science Technical Report, UT-CS-10-654 %8 2010-05 %G eng %0 Journal Article %J International Journal of High Performance Computing Applications (to appear) %D 2009 %T The International Exascale Software Project: A Call to Cooperative Action by the Global High Performance Community %A Jack Dongarra %A Pete Beckman %A Patrick Aerts %A Franck Cappello %A Thomas Lippert %A Satoshi Matsuoka %A Paul Messina %A Terry Moore %A Rick Stevens %A Anne Trefethen %A Mateo Valero %B International Journal of High Performance Computing Applications (to appear) %8 2009-07 %G eng %0 Journal Article %J DOE SciDAC Review (to appear) %D 2007 %T Creating Software Technology to Harness the Power of Leadership-class Computing Systems %A John Mellor-Crummey %A Pete Beckman %A Jack Dongarra %A Barton Miller %A Katherine Yelick %B DOE SciDAC Review (to appear) %8 2007-06 %G eng