论文标题
MPI程序执行时间预测的新方法
New approach to MPI program execution time prediction
论文作者
论文摘要
考虑了MPI程序在某些计算机安装集上执行时间预测的问题。这个问题是通过编排和在云计算环境中的虚拟基础架构在异构计算机安装网络上提供的：超级计算机或服务器群集（例如迷你数据中心）的虚拟基础架构。云计算环境有效性的关键标准之一是该程序在环境内留下的时间。这次由队列中的等待时间和所选物理计算机安装的执行时间组成，虚拟基础架构的计算资源被动态映射到其中。此问题的组成部分之一是在某些计算机安装集上对MPI程序执行时间的估计。这对于确定订单和程序执行的位置的适当选择是必要的。本文提出了两种新方法，以解决程序执行时间预测问题。第一个是基于基于Pearson相关系数的计算机安装组。第二个基于计算机安装和MPI程序的向量表示，即所谓的嵌入。嵌入技术可在推荐系统（例如商品（Amazon），文章（Arxiv.org））中积极使用，用于视频（YouTube，Netflix）。本文展示了嵌入技术如何有助于预测一组计算机安装的MPI程序的执行时间。
The problem of MPI programs execution time prediction on a certain set of computer installations is considered. This problem emerges with orchestration and provisioning a virtual infrastructure in a cloud computing environment over a heterogeneous network of computer installations: supercomputers or clusters of servers (e.g. mini data centers). One of the key criteria for the effectiveness of the cloud computing environment is the time staying by the program inside the environment. This time consists of the waiting time in the queue and the execution time on the selected physical computer installation, to which the computational resource of the virtual infrastructure is dynamically mapped. One of the components of this problem is the estimation of the MPI programs execution time on a certain set of computer installations. This is necessary to determine a proper choice of order and place for program execution. The article proposes two new approaches to the program execution time prediction problem. The first one is based on computer installations grouping based on the Pearson correlation coefficient. The second one is based on vector representations of computer installations and MPI programs, so-called embeddings. The embedding technique is actively used in recommendation systems, such as for goods (Amazon), for articles (Arxiv.org), for videos (YouTube, Netflix). The article shows how the embeddings technique helps to predict the execution time of a MPI program on a certain set of computer installations.
