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Maximum Flows in Parametric Graph Templates

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Algorithms and Complexity (CIAC 2023)

Abstract

Execution graphs of parallel loop programs exhibit a nested, repeating structure. We show how such graphs that are the result of nested repetition can be represented by succinct parametric structures. This parametric graph template representation allows us to reason about the execution graph of a parallel program at a cost that only depends on the program size. We develop structurally-parametric polynomial-time algorithm variants of maximum flows. When the graph models a parallel loop program, the maximum flow provides a bound on the data movement during an execution of the program. By reasoning about the structure of the repeating subgraphs, we avoid explicit construction of the instantiation (e.g., the execution graph), potentially saving an exponential amount of memory and computation. Hence, our approach enables graph-based dataflow analysis in previously intractable settings.

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Acknowledgements

This work received support from the PASC project DaCeMI and from the European Research Council under the European Union’s Horizon 2020 programme (Project PSAP, No. 101002047), as well as funding from EuroHPC-JU under grant DEEP-SEA, No. 955606.

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Correspondence to Lukas Gianinazzi .

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Ben-Nun, T., Gianinazzi, L., Hoefler, T., Oltchik, Y. (2023). Maximum Flows in Parametric Graph Templates. In: Mavronicolas, M. (eds) Algorithms and Complexity. CIAC 2023. Lecture Notes in Computer Science, vol 13898. Springer, Cham. https://doi.org/10.1007/978-3-031-30448-4_8

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  • DOI: https://doi.org/10.1007/978-3-031-30448-4_8

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