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ACMP06 - Finding Optimistic Upper Bounds for Task Graph Throughput on Heterogeneous Systems Using Linear Programming
Description
In this extended abstract, we present a model -- inspired by previous work in the data flow community -- for finding optimistic upper bounds on the throughput of task graphs executed on heterogeneous systems. This model interprets the execution of such graphs as flow networks with additional resource constraints. We show that such flow networks can be optimised as linear programs, and we present a Python interface for the representation and the finding of solutions to such programs. Finally, we provide some brief examples of how such models can be used to describe the performance of existing task graph application, and how they can be used to guide the optimisation and development of future software.
Presenter(s)
Presenter
Stephen Nicholas Swatman is a PhD student at the University of Amsterdam, working on computing challenges in the field of high-energy physics at the European Organization for Nuclear Research (CERN). Stephen studies the ways in which massively parallel compute architectures can be leveraged in complex, irregular scientific applications and aims to develop reproducible methods and models that can be used in a wide range of computational scientific domains. He is also interested into the applications of functional programming methods in high-performance computing in order to ensure correctness and performance.