Labeling Schemes with Queries

We study the question of ``how robust are the known lower bounds of labeling schemes when one increases the number of consulted labels''. Let $f$ be a function on pairs of vertices. An $f$-labeling scheme for a family of graphs \cF labels the vertices of all graphs in \cF such that for every graph G\in\cF and every two vertices $u,v\in G$, the value $f(u,v)$ can be inferred by merely inspecting the labels of $u$ and $v$. This paper introduces a natural generalization: the notion of $f$-labeling schemes with queries, in which the value $f(u,v)$ can be inferred by inspecting not only the labels of $u$ and $v$ but possibly the labels of some additional vertices. We show that inspecting the label of a single additional vertex (one {\em query}) enables us to reduce the label size of many labeling schemes significantly

Reducing Network Latency Using Subpages in a Global Memory Environment

New high-speed networks greatly encourage the use of network memory as a cache for virtual memory and file pages, thereby reducing the need for disk access. Becausepages are the fundamental transfer and access units in remote memory systems, page size is a key performance factor. Recently, page sizes of modern processors have been increasing in order to provide more TLB coverage and amortize disk access costs. Unfortunately, for high-speed networks, small transfers are needed to provide low latency. This trend in page size is thus at odds with the use of network memory on high-speed networks. This paper studies the use of subpages as a means of reducing transfer size and latency in a remote-memory environment. Using trace-driven simulation, we show how and why subpages reduce latency and improve performance of programs using network memory. Our results show that memory-intensive applications execute up to 1.8 times faster when executing with 1K-byte subpages, when compared to the same..