文章基本信息

标题：NUMA-Aware Thread Scheduling for Big Data Transfers over Terabits Network Infrastructure
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作者：Taeuk Kim ; Awais Khan ; Youngjae Kim 等
期刊名称：Scientific Programming
印刷版ISSN：1058-9244
出版年度：2018
卷号：2018
DOI：10.1155/2018/4120561
出版社：Hindawi Publishing Corporation
摘要：The evergrowing trend of big data has led scientists to share and transfer the simulation and analytical data across the geodistributed research and computing facilities. However, the existing data transfer frameworks used for data sharing lack the capability to adopt the attributes of the underlying parallel file systems (PFS). LADS (Layout-Aware Data Scheduling) is an end-to-end data transfer tool optimized for terabit network using a layout-aware data scheduling via PFS. However, it does not consider the NUMA (Nonuniform Memory Access) architecture. In this paper, we propose a NUMA-aware thread and resource scheduling for optimized data transfer in terabit network. First, we propose distributed RMA buffers to reduce memory controller contention in CPU sockets and then schedule the threads based on CPU socket and NUMA nodes inside CPU socket to reduce memory access latency. We design and implement the proposed resource and thread scheduling in the existing LADS framework. Experimental results showed from 21.7% to 44% improvement with memory-level optimizations in the LADS framework as compared to the baseline without any optimization.