Novel hybrid scalable scientific algorithms are needed with the advent of variety of novel accelerators including graphics processing units (GPUs), field-programmable gate arrays (FPGAs) as well as with the growth of the size of quantum computing devices and neuromorphic chips and various artificial intelligence (AI) specific processors. This myriad of devices requires an unified hybrid approach that allows efficient and scalable hybrid approaches combining classical and novel computing paradigms to be implemented at scale. These extreme-scale heterogeneous systems require novel scientific algorithms to hide the complexity, hide network and memory latency, have advanced communication, and have no synchronization points where possible. With the advent of AI in the past few years the need of such scalable mathematical methods and algorithms for such hybrid architectures that are able to handle data and compute intensive applications at scale becomes even more important.

Scientific algorithms for multi-petaflop and exa-flop systems also need to be fault tolerant and fault resilient, since the probability of faults increases with scale. Resilience at the system software and at the algorithmic level is needed as a crosscutting effort. Key science applications require novel mathematics and mathematical models and system software that address the scalability and resilience challenges of current- and future-generation extreme-scale heterogeneous high performance computing (HPC) systems.


Workshop Chairs

Vassil Alexandrov, Hartree Centre, Science and Technology Facilities Council, UK

Jack Dongarra, University of Tennessee, Knoxville, USA

Al Geist, Oak Ridge National Laboratory, USA

Dieter Kranzlmueller, Leibniz Supercomputing Centre and Ludwig-Maximilians-University Munich, Germany

Ivano Tavernelli, IBM Zurich, Switzerland

Program Committee

Hartwig Anzt, University of Tennessee, Knoxville, USA, and Karlsruher Institute for Technology (KIT), Germany

Rick Archibald, Oak Ridge National Laboratory, USA

Hans-Joachim Bungartz, Technical University of Munich, Germany

James Elliott, Sandia National Laboratories, USA

Nahid Emad, University of Versailles SQ, France

Wilfried Gansterer, University of Vienna, Austria

Yasuhiro Idomura, Japan Atomic Energy Agency, Japan

Kirk E. Jordan, IBM T.J. Watson Research, USA

Sriram Krishnamoorthy, Google, USA

Ignacio Laguna, Lawrence Livermore National Laboratory, USA

Paul Lin, Lawrence Berkeley National Laboratory, USA

Kengo Nakajima, RIKEN, Japan

Ron Perrot, University of Oxford, UK

Yves Robert, ENS Lyon, France

Stuart Slattery, Oak Ridge National Laboratory, USA

Call for paper

Important date

Abstract submission deadline
Abstract notification of acceptance

Submission Topics

  • Novel scientific algorithms that improve performance, scalability, resilience and power efficiency on hybrid architectures
  • Porting scientific algorithms and applications to hybrid and heterogeneous architectures (with different accelerators, hybrid classical/quantum, classical/AI accelerated, etc.)
  • Crosscutting approaches (system software and applications) in addressing scalability challenges on hybrid architectures
  • Naturally fault tolerant, self-healing or fault oblivious scientific algorithms for hybrid architectures
  • Methods and algorithms for silent data corruption with systems at scale
  • Ensuring algorithms scalability over various accelerator partitions/islands, and taking advantage where the system itself has different kinds of specialized compute nodes
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Important Date
  • Conference Date

    Nov 13



    Nov 18


  • Aug 09 2022

    Abstract Submission Deadline

  • Sep 09 2022

    Abstract Notification of Acceptance

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