This paper extends earlier research on hash-join algorithms to a multiprocessor architecture. Implementations of a number of centralized join algorithms are described and measured. Evaluation of these algorithms served to verify earlier analytical results. In addition, they demonstrate that bit vector filtering provides dramatic improvement in the performance of all algorithms including the sort merge join algorithm. Multiprocessor configurations of the centralized Grace and Hybrid hash-join algorithms are also presented. Both algorithms are shown to provide linear increases in throughput with corresponding increases in processor and disk resources. 1.

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In this paper, we introduce a new notion of motifs, called masks, that succinctly represent the repeated patterns for an input sequence T of n symbols drawn from an alphabet Σ. We show how to build the set of all maximal masks of length L and quorum q, in O(2 L n) time and space in the worst case. We analytically show that our algorithms perform better than constant-time enumerating and checking all the potential (|Σ | + 1) L candidate patterns in T after a polynomial-time preprocessing of T. Our algorithms are also cache-friendly, attaining O(2 L sort(n)) block transfers, where sort(n) is the cache oblivious complexity of sorting n items. Key words: Motif inference, motifs with don’t care, motif partial order, motifs with masks. 1.

A key decision when developing in-memory computing applications is choice of a mechanism to store and retrieve strings. The most efficient current data structures for this task are the hash table with move-to-front chains and the burst trie, both of which use linked lists as a substructure, and variants of binary search tree. These data structures are computationally efficient, but typical implementations use large numbers of nodes and pointers to manage strings, which is not efficient in use of cache. In this article, we explore two alternatives to the standard representation: the simple expedient of including the string in its node, and, for linked lists, the more drastic step of replacing each list of nodes by a contiguous array of characters. Our experiments show that, for large sets of strings, the improvement is dramatic. For hashing, in the best case the total space overhead is reduced to less than 1 bit per string. For the burst trie, over 300MB of strings can be stored in a total of under 200MB of memory with significantly improved search time. These results, on a variety of data sets, show that cache-friendly variants of fundamental data structures can yield remarkable gains in performance.