October 22 – 27 , 2017, Dagstuhl Seminar 17431

Performance Portability in Extreme Scale Computing: Metrics, Challenges, Solutions


Anshu Dubey (Argonne National Laboratory, US)
Paul H. J. Kelly (Imperial College London, GB)
Bernd Mohr (Jülich Supercomputing Centre, DE)
Jeffrey S. Vetter (Oak Ridge National Laboratory, US)

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Performance Portability is a critical new challenge in extreme-scale computing. In essence, performance-portable applications can be efficiently executed on a wide variety of HPC architectures without significant manual modifications. For nearly two decades, HPC architectures and programming models remained relatively stable, which allowed growth of complex multidisciplinary applications whose lifecycles span multiple generations of HPC platforms.

Recently, however, platforms are growing much more complex, diverse, and heterogeneous - both within a single system and across systems and generations. Details already known from planned future systems indicate that this trend will continue (at least for the foreseeable future). Current and planned future large-scale HPC systems consist of complex configurations with a massive number of components. Each node has multiple multi-core sockets and often one or more additional accelerator units in the form of many-core nodes or GPGPUs, resulting in a heterogeneous system architecture. Memory hierarchies including caches, memory, and storage are also diversifying in order to meet multiple constraints: power, latency, bandwidth, persistence, reliability, and capacity. These factors are reducing portability, and forcing applications teams to either spend considerable effort porting and optimizing their applications for each specific platform, or risk owning applications that perform well on perhaps only one architecture.

This Dagstuhl Seminar represents a unique opportunity to bring together international experts from the three research communities essential to tackling this performance portability challenge: developers of large-scale computational science software projects whose lifetime will span multiple generations of systems, researchers developing relevant parallel programming or system software technologies, and specialists in profiling, understanding, and modelling performance. The major research questions for the seminar are:

  • To understand challenges, design metrics, and prioritize potential solutions for performance portability: Solutions will need to synthesize existing concepts across multiple fields, including performance and productivity modeling, programming models and compilation, architectures, system software.
  • Management of data movement in complex applications: Diverse data movement patterns dictated by different devices form one of the largest impediments to portable performance. Addressing it will require cross-cutting solutions supporting more than one abstraction, and will allow scientists to balance tradeoffs in these factors prior to design, development, or procurement of an architecture, software stack, or application.
  • Composability: Many applications require flexibility and composability because they address different physical regimes either within the same simulation, or in different instances of simulations.
  • Pathways to impact on the research community: As the community becomes more reliant on both more complex architectures and software stacks, it is especially important that we develop the conceptual tools to facilitate research and practical solutions for performance portability. The impact of ignoring this topic could be potentially devastating to the quality and sustainability of computational science software, and consequently on the science and engineering research they support. Thus a key element of the seminar will be to tackle this challenge in major science community software projects.

  Creative Commons BY 3.0 DE
  Anshu Dubey, Paul H. J. Kelly, Bernd Mohr, and Jeffrey S. Vetter


  • Modelling / Simulation
  • Optimization / Scheduling
  • Software Engineering


  • Performance portability
  • Productivity
  • Parallel programming
  • Scientific computing

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