In this article, we focus on ranking and selection procedures that sequentially allocate replications to systems by applying some acquisition function. We propose an acquisition function, called gCEI, which exploits the gradient of the complete expected improvement with respect to the number of replications. We prove that the gCEI procedure, which adopts gCEI as the acquisition function in a serial computing environment, achieves the asymptotically optimal static replication allocation of Glynn and Juneja in the limit under a normality assumption. We also propose two procedures, called caching and credit, that extend any acquisition-function-based procedure in a serial environment into both synchronous and asynchronous parallel environments. While allocating replications to systems, both procedures use persistence forecasts for the unavailable outputs of the currently running replications, but differ in usage of the available outputs. We prove that, under certain assumptions, the caching procedure achieves the same asymptotic allocation as in the serial environment. A similar result holds for the credit procedure using gCEI as the acquisition function. In terms of efficiency and effectiveness, the credit procedure empirically performs as well as the caching procedure, despite not carefully controlling the output history as the caching procedure does, and is faster than the serial version without any number-of-replications penalty due to using persistence forecasts. Both procedures are designed to solve small-to-medium-sized problems on computers with a modest number of processors, such as laptops and desktops as opposed to high-performance clusters, and are superior to state-of-the-art parallel procedures in this setting.

在本文中，我们重点关注通过应用某些获取函数将复制按顺序分配给系统的排名和选择过程。我们提出了一种称为 gCEI 的采集函数，它利用了相对于重复次数的完整预期改进的梯度。我们证明了在串行计算环境中采用 gCEI 作为采集函数的 gCEI 程序在正态假设下在极限条件下实现了 Glynn 和 Juneja 的渐近最优静态复制分配。我们还提出了两个过程，称为缓存和信用，将串行环境中任何基于采集功能的过程扩展到同步和异步并行环境。在向系统分配复制时，这两个过程都对当前运行的复制的不可用输出使用持久性预测，但在可用输出的使用方面有所不同。我们证明，在某些假设下，缓存过程实现了与串行环境中相同的渐近分配。使用 gCEI 作为获取函数的信用程序也有类似的结果。在效率和有效性方面，信用过程的经验表现与缓存过程一样好，尽管没有像缓存过程那样仔细控制输出历史记录，并且比串行版本更快，并且没有由于使用持久性预测。这两个过程都旨在解决具有适度数量处理器的计算机（例如笔记本电脑和台式机，而不是高性能集群）上的中小型问题，并且优于最先进的并行过程这个设置。