多核处理器和数据密集型应用的先进系统软件的研究问题和挑战

Xiaodong Zhang
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摘要

与多核处理器的快速技术进步和从计算密集型到高度数据密集型应用程序的快速变化相比,操作系统的发展已经非常缓慢了几十年。在当今的计算环境中,应用程序用户面临着两大挑战。在系统层次结构的顶层,为多个内核并发访问配备了私有和共享缓存,这不可避免地会导致访问冲突,从而降低执行性能。在底层,“内存墙”的性能瓶颈已经转移到“磁盘墙”,这是许多数据密集型应用程序的严重瓶颈。由于处理器缓存和磁盘存储不在操作系统管理的主要范围内,并且它们日益复杂的操作对应用程序用户并不透明,因此上述性能问题在任何级别的计算机系统中都没有得到有效解决。我们一直致力提升操作系统,目的有二:(1)善用多核处理器丰富而复杂的资源,以及(2)尽快存取磁碟资料。在多核处理器级别,我们正在开发新的资源分配管理,以提高每个核心和/或每个线程的有效缓存容量,并通过协调内存带宽共享来最小化片外内存访问中的拥塞。在存储级别,我们使操作系统能够有效地利用“顺序局部性”——对于相同数量的数据,顺序访问要比磁盘中的随机访问快几个数量级。在这次演讲中,将概述相关的研究问题和挑战,并介绍初步结果。
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Research Issues and Challenges to Advance System Software  for Multicore Processors and Data-Intensive Applications
Compared with rapid technology advancements in multicore processors and rapid changes from computing-intensive to highly data-intensive applications, operating systems have been evolved very slowly for several decades. Application users are facing to two major challenges in today's computing environment. On the top level of the system hierarchy, private and shared caches are equipped for many cores to access concurrently, inevitably causing access conflicts to degrade execution performance. On the bottom level, the performance bottleneck of "memory wall" has been shifted to "disk wall" that is a serious bottleneck for many data- intensive applications. Since processor caches and disk storage are not in the major scope of operating system management, and their increasingly complex operations are not transparent to application users, the above mentioned performance issues have not been effectively addressed at any level of computer systems. We have made a continuous effort to enhance operating systems with two objectives: (1) to well utilize rich but complex resources of multicore processors and (2) to access disk data as fast as possible. At the multicore processor level, we are developing new resource allocation management to improve the effective caching capacity per core and/or per thread, and to minimize congestion in off-chip memory accesses by coordinating memory bandwidth sharing. At the storage level, we enable operating systems to effectively exploit "sequential locality" - for the same amount of data, sequential accesses are several orders of magnitude faster than random accesses in disks. In this talk, related research issues and challenges will be overviewed, and preliminary results will be presented.
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