Inductive Bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters
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The problem of scheduling of workloads onto heterogeneous processors (e.g., CPUs, GPUs, FPGAs) is of fundamental importance in modern datacenters. Most current approaches rely on building application/system-specific heuristics that have to be reinvented on a case-by-case basis. This can be prohibitively expensive and is untenable going forward. In this paper, we propose a domain-driven reinforcement learning (RL) model for scheduling that can be broadly applied to a large class of heterogeneous processors. The key novelty of our approach is (i) the RL model; and (ii) the significant reduction of training-data (using domain knowledge) and -time (using sampling based end-to-end gradient propagation). We demonstrate the approach using real world GPU and FPGA accelerated applications to produce scheduling policies that significantly outperform hand-tuned heuristics.
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