We study the eects of caches on basic graph and hashing algorithms and show how cache eects inuence the best solutions to these problems. We study the performance of basic data structures for storing lists of values and use these results to design and evaluate algorithms for hashing, Breadth-First-Search (BFS) and Depth-First-Search (DFS).

: This paper investigates the performance of hashing algorithms by both an experimental and an analytical approach. We examine the performance of three classical hashing algorithms: chaining, double hashing and linear probing. Our experimental results show that, despite the theoretical superiority of chaining and double hashing, linear probing outperforms both for random lookups. We explore variations on the data structures used by these traditional algorithms to improve their spatial locality and hence cache performance. Our results also help determine the optimal table size for a given key set size. In addition to time, we also study the average number of probes and cache misses incurred by these algorithms. For most of the algorithms studied in this paper, our analysis agrees with the experimental results. As a supplementary result, we examine the behavior of random lookups to a hash table. This provides a simple way to estimate the cache miss penalties of different machines. Two c...