Published June 9, 2021 | Version v1
Report Open

Workflows Community Summit: Advancing the State-of-the-art of Scientific Workflows Management Systems Research and Development

  • 1. University of Southern California
  • 2. University of Hawaii at Manoa
  • 3. University of Chicago
  • 4. Lawrence Livermore National Laboratory
  • 5. BNL / Rutgers
  • 6. Tweag I/O
  • 7. University of Manchester
  • 8. UC San Diego
  • 9. University of Notre Dame
  • 10. Heriot-Watt University
  • 11. Barcelona Supercomputing Center
  • 12. AGH University of Science and Technology
  • 13. University of Tennessee
  • 14. Southern California Earthquake Center
  • 15. VIB / ELIXIR Belgium
  • 16. Common Workflow Language / VU Amsterdam / ELIXIR-NL
  • 17. Univ. of Texas at Arlington
  • 18. NERSC
  • 19. University of Innsbruck
  • 20. University of Oslo, Norway
  • 21. / cTuning foundation
  • 22. CNRS, France
  • 23. Lawrence Berkeley National Laboratory
  • 24. Universidad Politécnica de Madrid
  • 25. The University of Manchester
  • 26. Uninett Sigma2
  • 27. EPFL
  • 28. University of Illinois
  • 29. Humboldt-Universität zu Berlin
  • 30. University of Illinois, Urbana-Champaign
  • 31. Oak Ridge National Laboratory
  • 32. Rutgers University
  • 33. Argonne National Laboratory
  • 34. Karlsruhe Institute of Technology
  • 35. IBM Research
  • 36. CNRS/CC-IN2P3
  • 37. Parallel Works


Scientific workflows are a cornerstone of modern scientific computing, and they have underpinned some of the most significant discoveries of the last decade. Many of these workflows have high computational, storage, and/or communication demands, and thus must execute on a wide range of large-scale platforms, from large clouds to upcoming exascale HPC platforms. Workflows will play a crucial role in the data-oriented and post-Moore’s computing landscape as they democratize the application of cutting-edge research techniques, computationally intensive methods, and use of new computing platforms. As workflows continue to be adopted by scientific projects and user communities, they are becoming more complex. Workflows are increasingly composed of tasks that perform computations such as short machine learning inference, multi-node simulations, long-running machine learning model training, amongst others, and thus increasingly rely on heterogeneous architectures that include CPUs but also GPUs and accelerators. The workflow management system (WMS) technology landscape is currently segmented and presents significant barriers to entry due to the hundreds of seemingly comparable, yet incompatible, systems that exist. Another fundamental problem is that there are conflicting theoretical bases and abstractions for a WMS. Systems that use the same underlying abstractions can likely be translated between, which is not the case for systems that use different abstractions. More information:



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