@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 {icl:506,
	title = {Towards Efficient MapReduce Using MPI},
	journal = {Lecture Notes in Computer Science, Recent Advances in Parallel Virtual Machine and Message Passing Interface - 16th European PVM/MPI Users{\textquoteright} Group Meeting},
	volume = {5759},
	year = {2009},
	month = {2009-00},
	pages = {240-249},
	publisher = {Springer Berlin / Heidelberg},
	address = {Espoo, Finland},
	author = {Torsten Hoefler and Yuan-Shun Dai and Jack Dongarra},
	editor = {M. Ropo and J Westerholm and Jack Dongarra}
}
@inbook {875,
	title = {A New Approach to MPI Collective Communication Implementations},
	booktitle = {Distributed and Parallel Systems},
	year = {2007},
	pages = {45-54},
	publisher = {Springer US},
	organization = {Springer US},
	abstract = {Recent research into the optimization of collective MPI operations has resulted in a wide variety of algorithms and corresponding implementations, each typically only applicable in a relatively narrow scope: on a specific architecture, on a specific network, with a specific number of processes, with a specific data size and/or data-type {\textendash} or any combination of these (or other) factors. This situation presents an enormous challenge to portable MPI implementations which are expected to provide optimized collective operation performance on all platforms. Many portable implementations have attempted to provide a token number of algorithms that are intended to realize good performance on most systems. However, many platform configurations are still left without well-tuned collective operations. This paper presents a proposal for a framework that will allow a wide variety of collective algorithm implementations and a flexible, multi-tiered selection process for choosing which implementation to use when an application invokes an MPI collective function.},
	keywords = {Automatic Selection, Collective Operation, Framework, Message Passing (MPI), Open MPI},
	isbn = {978-0-387-69857-1},
	doi = {10.1007/978-0-387-69858-8_5},
	author = {Torsten Hoefler and Jeffrey M. Squyres and Graham Fagg and George Bosilca and Wolfgang Rehm and Andrew Lumsdaine}
}
