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唐洁, 刘晨, 刘少山, 古志民, . 多核服务分工:Intel SCC下的XML数据解析研究[J]. 计算机科学技术学报, 2013, 28(1): 3-13. DOI: 10.1007/s11390-013-1308-6
引用本文: 唐洁, 刘晨, 刘少山, 古志民, . 多核服务分工:Intel SCC下的XML数据解析研究[J]. 计算机科学技术学报, 2013, 28(1): 3-13. DOI: 10.1007/s11390-013-1308-6
Jie Tang, Pollawat Thanarungroj, Chen Liu, Shao-Shan Liu, Zhi-Min Gu, Jean-Luc Gaudiot. Pinned OS/Services: A Case Study of XML Parsing on Intel SCC[J]. Journal of Computer Science and Technology, 2013, 28(1): 3-13. DOI: 10.1007/s11390-013-1308-6
Citation: Jie Tang, Pollawat Thanarungroj, Chen Liu, Shao-Shan Liu, Zhi-Min Gu, Jean-Luc Gaudiot. Pinned OS/Services: A Case Study of XML Parsing on Intel SCC[J]. Journal of Computer Science and Technology, 2013, 28(1): 3-13. DOI: 10.1007/s11390-013-1308-6

多核服务分工:Intel SCC下的XML数据解析研究

Pinned OS/Services: A Case Study of XML Parsing on Intel SCC

  • 摘要: 随着多核技术的发展,在不久的将来可将数百数千计算核集中在一块芯片上,然而,传统系统软件和中间件并不适应在如此大规模的系统中进行管理和提供服务.为了改善未来多核系统中操作系统和中间件服务的可扩展性和适应性,本章提出了多核架构下的操作系统/中间件层服务分工.通过把特定的操作系统/中间件服务移植到一个专属核上,片上各核执行不同的分工,以解除系统的性能瓶颈,达到性能提高和能耗节省的双重优化目标.在本章中以Intel 48核同构平台上XML解析服务为例探讨了同构多核下服务分工的可行性.实验结果表明移植后的XML专用解析核在能耗上是可行的,但是在解析过程中,存储子系统给系统性能也会造成很大的影响,限制了专用核在性能优化方面的表现.作为延伸工作,进一步提出了一种使用结合数据预取的存储加速方法来改善XML解析的性能,通过专用核裁剪,可达到20%的性能提升和12.27%的能耗节省.

     

    Abstract: Nowadays, we are heading towards integrating hundreds to thousands of cores on a single chip. However, traditional system software and middleware are not well suited to manage and provide services at such large scale. To improve the scalability and adaptability of operating system and middleware services on future many-core platform, we propose the pinned OS/services. By porting each OS and runtime system (middleware) service to a separate core (special hardware acceleration), we expect to achieve maximal performance gain and energy efficiency in many-core environments. As a case study, we target on XML (Extensible Markup Language), the commonly used data transfer/store standard in the world. We have successfully implemented and evaluated the design of porting XML parsing service onto Intel 48-core Single-Chip Cloud Computer (SCC) platform. The results show that it can provide considerable energy saving. However, we also identified heavy performance penalties introduced from memory side, making the parsing service bloated. Hence, as a further step, we propose the memory-side hardware accelerator for XML parsing. With specified hardware design, we can further enhance the performance gain and energy efficiency, where the performance can be improved by 20% with 12.27% energy reduction.

     

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