DataSys: Data-Intensive Distributed Systems LaboratoryData-Intensive Distributed Systems Laboratory

Illinois Institute of Technology
Department of Computer Science

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7th Workshop on Many-Task Computing on Clouds, Grids, and Supercomputers (MTAGS) 2014

Co-located with Supercomputing/SC 2014
In cooperation with ACM SIGHPC 
New Orleans, Louisiana -- November 16th, 2014


Michael Wilde

Dataflow approaches to many-task computing: past, present and future

Abstract: Dataflow-driven programming and execution models can trace their origins to advanced LISP work in the 1970's and some efforts that were even earlier. Since then, dataflow has been frequently revisited - in large part because of the belief that it holds the key to transparent, easier parallelism. We trace currently active work on this problem back to its roots, assess the current state-of-the-art, and suggest areas for future research in solving problems in many-task applications at scales ranging from the personal to the extreme.

Michael Wilde is a software architect in the Mathematics and Computer Science Division, Argonne National Laboratory, and a Senior Fellow of the University of Chicago/Argonne National Laboratory Computation Institute. His research focus is the application of parallel scripting to enhance scientific productivity by making parallel and distributed computing systems easier to use. He also conducts research into data provenance to record and query the history and metadata of scientific computations and datasets. His work centers on development and application of the Swift parallel scripting language,