局部性
Locality
摘要：
现代硬件上的程序性能以\emph{locality of reference}为特征，即访问地址空间中与最近访问过的数据接近的数据比访问随机位置中的数据更快。这是由于许多体系结构特征，包括高速缓存、预取、虚拟地址转换和硬盘驱动器的物理属性;试图对构成现代机器性能的所有组件进行建模是不可能的，特别是对于一般算法设计目的。如果有人能证明一个算法在所有奖励引用局部性的系统上都是渐近最优的，而不管它在合理的范围内如何表现自己，那会怎么样呢？我们表明，这是可能的，并排除一些病态的情况下，缓存无关的算法是渐近最优的理想缓存模型是渐近最优的任何合理的设置，奖励引用的局部性。这是令人惊讶的，因为高速缓存无关框架设想了一种特定的架构模型，该架构模型涉及阻塞的存储器传输到不同大小的高速缓存的多级层次结构中，并且没有被设计为直接对引用位置相关性能进行建模。
英文摘要：
The program performance on modern hardware is characterized by \emph{locality of reference}, that is, it is faster to access data that is close in address space to data that has been accessed recently than data in a random location. This is due to many architectural features including caches, prefetching, virtual address translation and the physical properties of a hard disk drive; attempting to model all the components that constitute the performance of a modern machine is impossible, especially for general algorithm design purposes. What if one could prove an algorithm is asymptotically optimal on all systems that reward locality of reference, no matter how it manifests itself within reasonable limits? We show that this is possible, and that excluding some pathological cases, cache-oblivious algorithms that are asymptotically optimal in the ideal-cache model are asymptotically optimal in any reasonable setting that rewards locality of reference. This is surprising as the cache-oblivious framework envisions a particular architectural model involving blocked memory transfer into a multi-level hierarchy of caches of varying sizes, and was not designed to directly model locality-of-reference correlated performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
