This paper 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.

翻译：本文提出贝叶斯优化增强因素自我列表(BOFSS),这是一个新的平行循环列表战略。BOFSS是一个自动调整的变体,它以巴伊西亚优化(BO)为基础,以黑箱优化算法为基础,其核心思想是通过使用BO解决优化问题,自动调整FSS的内部参数。调试程序只要求在线执行时间测量目标循环。为了应用BO,我们用两个高斯进程(GP)的概率机器学习模型来模拟执行时间。值得注意的是,我们提出一个有地方意识的GP模型,假设时间定位效应类似于一个急剧下降的功能。通过精确模拟时间定位效应,我们的地貌定位组合模型加快了BO的趋同。我们在海合会实施OpenMP标准时,并对照其他时间安排算法评估了它的业绩。此外,为了用两个高斯进程(GP)的概率机器学习模型来量化我们的方法的性能变化。值得注意的是,我们用微量的GOPGGPG(G)的GPA(G) 模型来计算出时间偏差效应,而时间效应则用最差的FS(BS) 。