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Aurora: Architecting Argonne's First Exascale Supercomputer for Accelerated Scientific Discovery
Authors:
William E. Allcock,
Benjamin S. Allen,
James Anchell,
Victor Anisimov,
Thomas Applencourt,
Abhishek Bagusetty,
Ramesh Balakrishnan,
Riccardo Balin,
Solomon Bekele,
Colleen Bertoni,
Cyrus Blackworth,
Renzo Bustamante,
Kevin Canada,
John Carrier,
Christopher Chan-nui,
Lance C. Cheney,
Taylor Childers,
Paul Coffman,
Susan Coghlan,
Tanima Dey,
Michael D'Mello,
Ashok Emani,
Murali Emani,
Kyle G. Felker,
Sam Foreman
, et al. (84 additional authors not shown)
Abstract:
Aurora is Argonne National Laboratory's pioneering Exascale supercomputer, designed to accelerate scientific discovery with cutting-edge architectural innovations. Key new technologies include the Intel(TM) Xeon(TM) Data Center GPU Max Series (code-named Sapphire Rapids) with support for High Bandwidth Memory (HBM), alongside the Intel(TM) Data Center GPU Max Series (code-named Ponte Vecchio) on e…
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Aurora is Argonne National Laboratory's pioneering Exascale supercomputer, designed to accelerate scientific discovery with cutting-edge architectural innovations. Key new technologies include the Intel(TM) Xeon(TM) Data Center GPU Max Series (code-named Sapphire Rapids) with support for High Bandwidth Memory (HBM), alongside the Intel(TM) Data Center GPU Max Series (code-named Ponte Vecchio) on each compute node. Aurora also integrates the Distributed Asynchronous Object Storage (DAOS), a novel exascale storage solution, and leverages Intel's oneAPI programming environment. This paper presents an in-depth exploration of Aurora's node architecture, the HPE Slingshot interconnect, the supporting software ecosystem, and DAOS. We provide insights into standard benchmark performance and applications readiness efforts via Aurora's Early Science Program and the Exascale Computing Project.
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Submitted 8 December, 2025; v1 submitted 9 September, 2025;
originally announced September 2025.
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Workflows Community Summit: Tightening the Integration between Computing Facilities and Scientific Workflows
Authors:
Rafael Ferreira da Silva,
Kyle Chard,
Henri Casanova,
Dan Laney,
Dong Ahn,
Shantenu Jha,
William E. Allcock,
Gregory Bauer,
Dmitry Duplyakin,
Bjoern Enders,
Todd M. Heer,
Eric Lancon,
Sergiu Sanielevici,
Kevin Sayers
Abstract:
The importance of workflows is highlighted by the fact that they have underpinned some of the most significant discoveries of the past decades. Many of these workflows have significant computational, storage, and communication demands, and thus must execute on a range of large-scale computer systems, from local clusters to public clouds and upcoming exascale HPC platforms. Historically, infrastruc…
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The importance of workflows is highlighted by the fact that they have underpinned some of the most significant discoveries of the past decades. Many of these workflows have significant computational, storage, and communication demands, and thus must execute on a range of large-scale computer systems, from local clusters to public clouds and upcoming exascale HPC platforms. Historically, infrastructures for workflow execution consisted of complex, integrated systems, developed in-house by workflow practitioners with strong dependencies on a range of legacy technologies. Due to the increasing need to support workflows, dedicated workflow systems were developed to provide abstractions for creating, executing, and adapting workflows conveniently and efficiently while ensuring portability. While these efforts are all worthwhile individually, there are now hundreds of independent workflow systems. The resulting workflow system technology landscape is fragmented, which may present significant barriers for future workflow users due to many seemingly comparable, yet usually mutually incompatible, systems that exist. In order to tackle some of these challenges, the DOE-funded ExaWorks and NSF-funded WorkflowsRI projects have organized in 2021 a series of events entitled the "Workflows Community Summit". The third edition of the ``Workflows Community Summit" explored workflows challenges and opportunities from the perspective of computing centers and facilities. The third summit brought together a small group of facilities representatives with the aim to understand how workflows are currently being used at each facility, how facilities would like to interact with workflow developers and users, how workflows fit with facility roadmaps, and what opportunities there are for tighter integration between facilities and workflows. More information at: https://workflowsri.org/summits/facilities/
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Submitted 19 January, 2022;
originally announced January 2022.
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Scheduling Beyond CPUs for HPC
Authors:
Yuping Fan,
Zhiling Lan,
Paul Rich,
William E. Allcock,
Michael E. Papka,
Brian Austin,
David Paul
Abstract:
High performance computing (HPC) is undergoing significant changes. The emerging HPC applications comprise both compute- and data-intensive applications. To meet the intense I/O demand from emerging data-intensive applications, burst buffers are deployed in production systems. Existing HPC schedulers are mainly CPU-centric. The extreme heterogeneity of hardware devices, combined with workload chan…
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High performance computing (HPC) is undergoing significant changes. The emerging HPC applications comprise both compute- and data-intensive applications. To meet the intense I/O demand from emerging data-intensive applications, burst buffers are deployed in production systems. Existing HPC schedulers are mainly CPU-centric. The extreme heterogeneity of hardware devices, combined with workload changes, forces the schedulers to consider multiple resources (e.g., burst buffers) beyond CPUs, in decision making. In this study, we present a multi-resource scheduling scheme named BBSched that schedules user jobs based on not only their CPU requirements, but also other schedulable resources such as burst buffer. BBSched formulates the scheduling problem into a multi-objective optimization (MOO) problem and rapidly solves the problem using a multi-objective genetic algorithm. The multiple solutions generated by BBSched enables system managers to explore potential tradeoffs among various resources, and therefore obtains better utilization of all the resources. The trace-driven simulations with real system workloads demonstrate that BBSched improves scheduling performance by up to 41% compared to existing methods, indicating that explicitly optimizing multiple resources beyond CPUs is essential for HPC scheduling.
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Submitted 9 December, 2020;
originally announced December 2020.