Kazuki Ohta, D. Kimpe, Jason Cope, K. Iskra, R. Ross, Y. Ishikawa
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引用次数: 47
摘要
I/O是HPC系统和领先级机器上数据密集型科学应用程序的关键瓶颈。在这些系统上运行的应用程序可能会遇到瓶颈,因为I/O系统无法处理大量的I/O请求。应用程序和系统使用I/O转发来聚合I/O请求并将其委派给存储系统。在本文中,我们提出了在I/O转发层的两种优化技术,以进一步减少领导级计算系统的I/O瓶颈。第一个优化是数据传输管道,使I/O请求在网络和文件系统层重叠。第二个优化合并I/O请求,并将I/O请求委托调度到后端并行文件系统。我们在I/O转发可伸缩性层实现了这些优化,并在东京大学的T2K开放超级计算机和阿贡领导计算设施的Surveyor Blue Gene/P系统上实现了这些优化。在这两个系统上,优化都提高了应用程序的I/O吞吐量,但突出了我们计划解决的I/O转发层的其他I/O争用领域。
Optimization Techniques at the I/O Forwarding Layer
I/O is the critical bottleneck for data-intensive scientific applications on HPC systems and leadership-class machines. Applications running on these systems may encounter bottlenecks because the I/O systems cannot handle the overwhelming intensity and volume of I/O requests. Applications and systems use I/O forwarding to aggregate and delegate I/O requests to storage systems. In this paper, we present two optimization techniques at the I/O forwarding layer to further reduce I/O bottlenecks on leadership-class computing systems. The first optimization pipelines data transfers so that I/O requests overlap at the network and file system layer. The second optimization merges I/O requests and schedules I/O request delegation to the back-end parallel file systems. We implemented these optimizations in the I/O Forwarding Scalability Layer and them on the T2K Open Supercomputer at the University of Tokyo and the Surveyor Blue Gene/P system at the Argonne Leadership Computing Facility. On both systems, the optimizations improved application I/O throughput, but highlighted additional areas of I/O contention at the I/O forwarding layer that we plan to address.