Graph analytics power a range of applications in areas as diverse as finance, networking and business logistics. A common property of graphs used in the domain of graph analytics is a power-law distribution of vertex connectivity, wherein a small number of vertices are responsible for a high fraction of all connections in the graph. These richly-connected, hot, vertices inherently exhibit high reuse. However, this work finds that state-of-the-art hardware cache management schemes struggle in capitalizing on their reuse due to highly irregular access patterns of graph analytics. In response, we propose GRASP, domain-specialized cache management at the last-level cache for graph analytics. GRASP augments existing cache policies to maximize reuse of hot vertices by protecting them against cache thrashing, while maintaining sufficient flexibility to capture the reuse of other vertices as needed. GRASP keeps hardware cost negligible by leveraging lightweight software support to pinpoint hot vertices, thus eliding the need for storage-intensive prediction mechanisms employed by state-of-the-art cache management schemes. On a set of diverse graph-analytic applications with large high-skew graph datasets, GRASP outperforms prior domain-agnostic schemes on all datapoints, yielding an average speed-up of 4.2% (max 9.4%) over the best-performing prior scheme. GRASP remains robust on low-/no-skew datasets, whereas prior schemes consistently cause a slowdown.

In the domain of graph analytics, power-law graphs are prevalent. In such graphs, a small fraction of vertices are responsible for a large share of all graph connections. These richly-connected (hot) vertices inherently exhibit high reuse. However, this work finds that the state-of-the-art hardware cache management schemes struggle in capitalizing on their reuse due to highly irregular access patterns of graph analytics. In response, we argue in favor of leveraging software knowledge of graph data structures to accurately pinpoint hot vertices in hardware. To that end, we propose GRASP, a domain-specialized LLC management scheme that enables high cache efficiency for graph analytics with minimal modifications to existing cache structures.

Multi-player online games have become very pop- ular in the last few years. Meanwhile, the IEEE 802.11 wireless networks have been in wide use. In this paper, we present an experimental study on the capability of an IEEE 802.11g network in supporting multi-player online games. In particular, we focus on the highly interactive first-person-shooter games. We describe the test bed we set up and the experiments we performed. Factors such as the number of game clients and the amount of background traffic are examined. Our results show that the amount of background traffic has a significant impact on the latency and the loss ratio of the game traffic between the game clients and the game server, which in turn affect the observed game performance greatly. I. INTRODUCTION