论文标题
高性能数据范围的混合云和HPC方法
Hybrid Cloud and HPC Approach to High-Performance Dataframes
论文作者
论文摘要
数据预处理是任何数据驱动应用程序中的基本组件。随着数据处理操作和数据量的增加,Cylon(一种分布式数据框系统)的开发是为了促进数据处理作为独立应用程序和库,尤其是对于Python应用程序。尽管Cylon显示出令人鼓舞的性能结果,但我们遇到了与传统消息传递接口(MPI)不符的框架集成的困难。尽管MPI实施涵盖了可扩展和高效的通信例程,但它们的过程启动机制与主流HPC系统非常有效,但与采用自己的资源管理系统的某些环境不相容。在这项工作中,我们通过直接整合统一的通信X(UCX)框架来缓解这个问题,该框架支持各种经典的HPC和非HPC Process-bootstagping机制作为我们的通信框架。当我们尝试使用Cylon的方法时,可以使用相同的技术将MPI通信带到其他不采用MPI内置流程管理方法的应用程序。
Data pre-processing is a fundamental component in any data-driven application. With the increasing complexity of data processing operations and volume of data, Cylon, a distributed dataframe system, is developed to facilitate data processing both as a standalone application and as a library, especially for Python applications. While Cylon shows promising performance results, we experienced difficulties trying to integrate with frameworks incompatible with the traditional Message Passing Interface (MPI). While MPI implementations encompass scalable and efficient communication routines, their process launching mechanisms work well with mainstream HPC systems but are incompatible with some environments that adopt their own resource management systems. In this work, we alleviated this issue by directly integrating the Unified Communication X (UCX) framework, which supports a variety of classic HPC and non-HPC process-bootstrapping mechanisms as our communication framework. While we experimented with our methodology on Cylon, the same technique can be used to bring MPI communication to other applications that do not employ MPI's built-in process management approach.