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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >A Probabilistic Machine Learning Approach to Scheduling Parallel Loops With Bayesian Optimization
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A Probabilistic Machine Learning Approach to Scheduling Parallel Loops With Bayesian Optimization

机译：概率机器学习方法，调度与贝叶斯优化的平行环路

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摘要

This article proposes Bayesian optimization augmented factoring self-scheduling (BO FSS), a new parallel loop scheduling strategy. BO FSS is an automatic tuning variant of the factoring self-scheduling (FSS) algorithm and is based on Bayesian optimization (BO), a black-box optimization algorithm. Its core idea is to automatically tune the internal parameter of FSS by solving an optimization problem using BO. The tuning procedure only requires online execution time measurement of the target loop. In order to apply BO, we model the execution time using two Gaussian process (GP) probabilistic machine learning models. Notably, we propose a locality-aware GP model, which assumes that the temporal locality effect resembles an exponentially decreasing function. By accurately modeling the temporal locality effect, our locality-aware GP model accelerates the convergence of BO. We implemented BO FSS on the GCC implementation of the OpenMP standard and evaluated its performance against other scheduling algorithms. Also, to quantify our method's performance variation on different workloads, or workload-robustness in our terms, we measure the minimax regret. According to the minimax regret, BO FSS shows more consistent performance than other algorithms. Within the considered workloads, BO FSS improves the execution time of FSS by as much as 22% and 5% on average.
机译：本文提出了贝叶斯优化增强分解自我调度（BO FSS），新的并行循环调度策略。 Bo FSS是一种自动调整变体，可分解自调度（FSS）算法，并基于贝叶斯优化（BO），黑盒优化算法。其核心思想是通过使用BO解决优化问题来自动调整FSS的内部参数。调谐过程仅需要在线执行时间测量目标循环。为了应用BO，我们使用两个高斯过程（GP）概率机器学习模型来模拟执行时间。值得注意的是，我们提出了一个地方感知GP模型，该模型假设时间局部效应类似于指数递减函数。通过准确地建模时间局部效果，我们的地区感知GP模型加速了博的收敛。我们在OpenMP标准的GCC实现上实施了Bo FSS，并评估其对其他调度算法的性能。此外，为了量化我们的方法对不同工作负载的性能变化，或在我们的条款中的工作负载 - 鲁棒性，我们测量Minimax后悔。根据Minimax的遗憾，Bo FSS显示比其他算法更一致的性能。在COMED工作负载中，BO FSS将FSS的执行时间与平均水平的执行时间提高了22％和5％。

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