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Scientific workflows are a cornerstone of modern scientific computing, andthey have underpinned some of the most significant discoveries of the lastdecade. Many of these workflows have high computational, storage, and/orcommunication demands, and thus must execute on a wide range of large-scaleplatforms, from large clouds to upcoming exascale HPC platforms. Workflows willplay a crucial role in the data-oriented and post-Moore's computing landscapeas they democratize the application of cutting-edge research techniques,computationally intensive methods, and use of new computing platforms. Asworkflows continue to be adopted by scientific projects and user communities,they are becoming more complex. Workflows are increasingly composed of tasksthat perform computations such as short machine learning inference, multi-nodesimulations, long-running machine learning model training, amongst others, andthus increasingly rely on heterogeneous architectures that include CPUs butalso GPUs and accelerators. The workflow management system (WMS) technologylandscape is currently segmented and presents significant barriers to entry dueto the hundreds of seemingly comparable, yet incompatible, systems that exist.Another fundamental problem is that there are conflicting theoretical bases andabstractions for a WMS. Systems that use the same underlying abstractions canlikely be translated between, which is not the case for systems that usedifferent abstractions. More information:https://workflowsri.org/summits/technical

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