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Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling

Nathan Grinsztajn 1 Olivier Beaumont 2 Emmanuel Jeannot 3 Philippe Preux 1
1 Scool - Scool
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
2 HiePACS - High-End Parallel Algorithms for Challenging Numerical Simulations
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest
3 TADAAM - Topology-Aware System-Scale Data Management for High-Performance Computing
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest
Abstract : In practice, it is quite common to face combinatorial optimization problems which contain uncertainty along with non-determinism and dynamicity. These three properties call for appropriate algorithms; reinforcement learning (RL) is dealing with them in a very natural way. Today, despite some efforts, most real-life combinatorial optimization problems remain out of the reach of reinforcement learning algorithms. In this paper, we propose a reinforcement learning approach to solve a realistic scheduling problem, and apply it to an algorithm commonly executed in the high performance computing community, the Cholesky factorization. On the contrary to static scheduling, where tasks are assigned to processors in a predetermined ordering before the beginning of the parallel execution, our method is dynamic: task allocations and their execution ordering are decided at runtime, based on the system state and unexpected events, which allows much more flexibility. To do so, our algorithm uses graph neural networks in combination with an actor-critic algorithm (A2C) to build an adaptive representation of the problem on the fly. We show that this approach is competitive with state-of-the-art heuristics used in high-performance computing runtime systems. Moreover, our algorithm does not require an explicit model of the environment, but we demonstrate that extra knowledge can easily be incorporated and improves performance. We also exhibit key properties provided by this RL approach, and study its transfer abilities to other instances.
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Submitted on : Tuesday, January 19, 2021 - 9:42:18 PM
Last modification on : Tuesday, January 4, 2022 - 6:17:10 AM
Long-term archiving on: : Tuesday, April 20, 2021 - 6:03:48 PM


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  • HAL Id : hal-03028981, version 1
  • ARXIV : 2011.04333


Nathan Grinsztajn, Olivier Beaumont, Emmanuel Jeannot, Philippe Preux. Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling. IEEE SSCI 2020 - Symposium Series on Computational Intelligence, Dec 2020, Canberra / Virtual, Australia. ⟨hal-03028981⟩



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