@booklet {, title = {XaaS: Acceleration as a Service to Enable Productive High-Performance Cloud Computing}, year = {2024}, month = {2024-01}, publisher = {arXiv}, abstract = {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.}, url = {https://arxiv.org/abs/2401.04552}, author = {Torsten Hoefler and Marcin Copik and Pete Beckman and Andrew Jones and Ian Foster and Manish Parashar and Daniel Reed and Matthias Troyer and Thomas Schulthess and Dan Ernst and Jack Dongarra} } @booklet {, title = {Earth Virtualization Engines - A Technical Perspective}, year = {2023}, month = {2023-09}, abstract = {Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide interactive and accessible climate simulations and data for a wide range of users. They combine high-resolution physics-based models with machine learning techniques to improve the fidelity, efficiency, and interpretability of climate projections. At their core, EVEs offer a federated data layer that enables simple and fast access to exabyte-sized climate data through simple interfaces. In this article, we summarize the technical challenges and opportunities for developing EVEs, and argue that they are essential for addressing the consequences of climate change.}, url = {https://arxiv.org/abs/2309.09002}, author = {Torsten Hoefler and Bjorn Stevens and Andreas F. Prein and Johanna Baehr and Thomas Schulthess and Thomas F. Stocker and John Taylor and Daniel Klocke and Pekka Manninen and Piers M. Forster and Tobias K{\"o}lling and Nicolas Gruber and Hartwig Anzt and Claudia Frauen and Florian Ziemen and Milan Kl{\"o}wer and Karthik Kashinath and Christoph Sch{\"a}r and Oliver Fuhrer and Bryan N. Lawrence} } @article {1211, title = {Big Data and Extreme-Scale Computing: Pathways to Convergence - Toward a Shaping Strategy for a Future Software and Data Ecosystem for Scientific Inquiry}, journal = {The International Journal of High Performance Computing Applications}, volume = {32}, year = {2018}, month = {2018-07}, pages = {435{\textendash}479}, abstract = {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.}, doi = {https://doi.org/10.1177/1094342018778123}, author = {Mark Asch and Terry Moore and Rosa M. Badia and Micah Beck and Pete Beckman and Thierry Bidot and Fran{\c c}ois Bodin and Franck Cappello and Alok Choudhary and Bronis R. de Supinski and Ewa Deelman and Jack Dongarra and Anshu Dubey and Geoffrey Fox and Haohuan Fu and Sergi Girona and Michael Heroux and Yutaka Ishikawa and Kate Keahey and David Keyes and William T. Kramer and Jean-Fran{\c c}ois Lavignon and Yutong Lu and Satoshi Matsuoka and Bernd Mohr and St{\'e}phane Requena and Joel Saltz and Thomas Schulthess and Rick Stevens and Martin Swany and Alexander Szalay and William Tang and Ga{\"e}l Varoquaux and Jean-Pierre Vilotte and Robert W. Wisniewski and Zhiwei Xu and Igor Zacharov} } @article {1355, title = {MAGMA: A Breakthrough in Solvers for Eigenvalue Problems}, year = {2012}, month = {2012-05}, publisher = {GPU Technology Conference (GTC12), Presentation}, address = {San Jose, CA}, author = {Stanimire Tomov and Jack Dongarra and Azzam Haidar and Ichitaro Yamazaki and Tingxing Dong and Thomas Schulthess and Raffaele Solc{\`a}} }