Journal: Computing in Science & Engineering

Abbreviation

Comput. sci. eng.

Publisher

IEEE

Journal Volumes

ISSN

1521-9615
1558-366X

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Publications 1 - 8 of 8
  • Evaluation of a special class of two-body integrals through extension of the integral routine in Maple
    Item type: Journal Article
    Rønnow, Troels F. (2014)
    Computing in Science & Engineering
  • Ubiquitous Fractal Dimension of Optimal Paths
    Item type: Journal Article
    Andrade, José S.; Reis, Saulo D.S.; Oliveira, Erneson A.; et al. (2011)
    Computing in Science & Engineering
  • Reflecting on the Goal and Baseline for Exascale Computing: A Roadmap Based on Weather and Climate Simulations
    Item type: Journal Article
    Schulthess, Thomas C.; Bauer, Peter; Wedi, Nils; et al. (2019)
    Computing in Science & Engineering
  • Earth Virtualization Engines: A Technical Perspective
    Item type: Journal Article
    Hoefler, Torsten; Stevens, Bjorn; Prein, Andreas F.; et al. (2023)
    Computing in Science & Engineering
    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 its 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.
  • Versatile Software-Defined Cluster for HPC Using Cloud Abstractions
    Item type: Journal Article
    Martinasso, Maxime; Klein, Mark; Cumming, Benjamin; et al. (2024)
    Computing in Science & Engineering
    This article introduces the versatile software-defined cluster, a novel framework that integrates high-performance computing (HPC) and cloud technologies offering a service-oriented approach for computing resources instead of a hardware-focused one, maintaining infrastructure independence and avoiding vendor lock-in. It addresses the challenges of rigidity and lack of customizability in conventional HPC systems, facilitating a more efficient use of shared infrastructures. The core concept revolves around a three-tiered structure-infrastructure, service management, and software as a service-ensuring immutable and consistent service deployment for the diverse scientific communities. This integration enhances scientific workflows' adaptability and efficiency, enabling science as a service with cost-effective solutions for shared digital research infrastructure with diverse usage patterns, such as batch, interactive, urgent computing, or machine learning workflows.
  • Simulating future changes in Arctic and subarctic vegetation
    Item type: Journal Article
    Epstein, Howard E.; Yu, Qin; Kaplan, Jed O.; et al. (2007)
    Computing in Science & Engineering
  • The PETSc Community as Infrastructure
    Item type: Journal Article
    Adams, Mark; Balay, Satish; Marin, Oana; et al. (2022)
    Computing in Science & Engineering
    The communities that develop and support open-source scientific software packages are crucial to the utility and success of such packages. Moreover, they form an important part of the human infrastructure that enables scientific progress. This article discusses aspects of the Portable Extensible Toolkit for Scientific Computation community, its organization, and technical approaches that enable community members to help each other efficiently and effectively.
  • XaaS: Acceleration as a Service to Enable Productive High-Performance Cloud Computing
    Item type: Journal Article
    (2024)
    Computing in Science & Engineering
    High-performance computing (HPC) and the 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 computing while supporting long-running and performance-sensitive workloads from HPC.
Publications 1 - 8 of 8