  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:
https://workflowsri.org/summits/technical
