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Is Acyclic Directed Graph Partitioning Effective for Locality-Aware Scheduling?

Abstract : We investigate efficient execution of computations, modeled as Directed Acyclic Graphs (DAGs), on a single processor with a two-level memory hierarchy, where there is a limited fast memory and a larger slower memory. Our goal is to minimize execution time by minimizing redundant data movement between fast and slow memory. We utilize a DAG partitioner that finds localized, acyclic parts of the whole computation that can fit into fast memory, and minimizes the edge cut among the parts. We propose a new scheduler that executes each part one-by-one, obeying the dependency among parts, aiming at reducing redundant data movement needed by cut-edges. Extensive experimental evaluation shows that the proposed DAG-based scheduler significantly reduces redundant data movement.
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Submitted on : Friday, September 27, 2019 - 12:20:30 PM
Last modification on : Friday, September 30, 2022 - 4:12:22 AM
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  • HAL Id : hal-02273122, version 1



Yusuf M. Özkaya, Anne Benoit, Ümit V. Çatalyürek. Is Acyclic Directed Graph Partitioning Effective for Locality-Aware Scheduling?. PPAM 2019 - 13th International Conference on Parallel Processing and Applied Mathematics, Sep 2019, Bialystok, Poland. ⟨hal-02273122⟩



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