CSR - AES: An Integrated Approach to Improving Communication Performance in Clusters

CSR - AES：提高集群通信性能的综合方法

• 批准号: 0509170
• 负责人: Douglas Swany
• 金额: $ 45万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0509170&HistoricalAwards=false
• 关键词: CSR; AES; Integrated; Approach; Improving

The project will develop an integrated aproach to improving communication performance in clusters. Cluster computing has become a common, cost-effective means of parallel computing. Although adding more CPUs increases the cluster's maximum processing power, real applications often can not efficiently use very large numbers of CPUs, due to lack of scalability. In regular codes the main impediment to achieving scalability is the communication overhead which increases as the number of CPUs increases. Most of these optimization methods proposed target specialized hardware or programming languages, and require specialized knowledge from the domain scientist, or are not enough to provide a comprehensive solution on their own, and do not adequately address the challenges of the layers of communication software between the sender processes and the receiver processes. Improved performance overall for these applications, it remains largely untapped due to (1) the need for the knowledge of the context of the communication operations to exploit the sophisticated network technology fully, and the (2) the low level nature of programming needed within the application program context to achieve that potential. In particular, performance can often be improved through increasing the use of lightweight asynchronous communication. Unfortunately, programming with asynchronous communication is difficult and error prone, even for the most experienced programmers.The project will pursue a vertically integrated approach, where a set of optimizations in the compiler, network and operating system, can enable legacy parallel applications to scale to a much larger number of CPUs, even if written without any knowledge of our techniques. An experimental prototype and preliminary experiments with real scientific applications, show that significant performance improvements are possible with a vertically integrated approach where knowledge of the context of communication operations is joined with knowledge of the network and cluster details to provide a fine-grained strategy for overlapping communication and computation. Based on these initial promising results, the overall goal of this proposed research is to create a means for scalable cluster computing through enabling integrated knowledge and cooperation between the source optimizer, operating system, and network technology of the cluster, without relying on the programmer to learn about the low level details of the cluster communications system.

该项目将制定一种综合办法，以改善集群的通信性能。集群计算已经成为一种常见的、经济有效的并行计算方法。虽然增加更多的cpu可以提高集群的最大处理能力，但由于缺乏可伸缩性，实际应用程序通常无法有效地使用大量的cpu。在常规代码中，实现可伸缩性的主要障碍是随着cpu数量的增加而增加的通信开销。这些优化方法大多针对专门的硬件或编程语言，需要领域科学家的专业知识，或者不足以提供一个全面的解决方案，也不能充分解决发送方进程和接收方进程之间通信软件层的挑战。这些应用程序的总体性能得到了提高，但由于(1)需要了解通信操作的上下文以充分利用复杂的网络技术，以及(2)在应用程序上下文中实现这种潜力所需的编程的低层次性质，它在很大程度上仍未得到开发。特别是，通常可以通过增加轻量级异步通信的使用来提高性能。不幸的是，使用异步通信进行编程是困难的，而且容易出错，即使对于最有经验的程序员也是如此。该项目将采用垂直集成的方法，在编译器、网络和操作系统中进行一系列优化，可以使遗留并行应用程序扩展到更多数量的cpu，即使是在不了解我们技术的情况下编写的。实验原型和真实科学应用的初步实验表明，通过垂直集成的方法可以显著提高性能，其中通信操作上下文的知识与网络和集群细节的知识相结合，为重叠通信和计算提供细粒度策略。基于这些初步的有希望的结果，本研究的总体目标是通过支持集群的源优化器、操作系统和网络技术之间的集成知识和合作，创建一种可扩展集群计算的方法，而不依赖于程序员学习集群通信系统的底层细节。