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Seminar: Parsl: Pervasive Parallel Programming in Python

Dates:20 September 2019
Times:14:00 - 15:00
What is it:Seminar
Organiser:Department of Computer Science
Who is it for:University staff, Adults, Current University students
Speaker:Daniel S. Katz
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  • Department of Computer Science

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  • In category "Seminar"
  • In group "(CS) Computer Science seminar series"
  • In group "(CS) Human centred computing"
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Join us for the next Computer Science Atlas talk with Daniel S Katz from the University of Illinois, USA.

High-level programming languages such as Python are increasingly used to provide intuitive interfaces to libraries written in lower-level languages and for assembling applications from various components. This migration towards orchestration rather than implementation, coupled with the growing need for parallel computing (e.g., due to big data and the end of Moore's law), necessitates rethinking how parallelism is expressed in programs. Here, we present Parsl, a parallel scripting library that augments Python with simple, scalable, and flexible constructs for encoding parallelism. These constructs allow Parsl to construct a dynamic dependency graph of components from a Python program enhanced with a small number of decorators that define the components to be executed asynchronously and in parallel, and then execute it efficiently on one or many processors. Parsl is designed for scalability, with an extensible set of executors tailored to different use cases, such as low-latency, high-throughput, or extreme-scale execution. We show, via experiments on the Blue Waters supercomputer, that Parsl executors can allow Python scripts to execute components with as little as 5 ms of overhead, scale to more than 250000 workers across more than 8000 nodes, and process upward of 1200 tasks per second. Other Parsl features simplify the construction and execution of composite programs by supporting elastic provisioning and scaling of infrastructure, fault-tolerant execution, and integrated wide-area data management. We show that these capabilities satisfy the needs of many-task, interactive, online, and machine learning applications in fields such as biology, cosmology, and materials science.

Speaker

Daniel S. Katz

Organisation: University of Illinois, USA

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