Speedup Versus Efficiency in Parallel Systems
IEEE Transactions on Computers
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
A Space-Economical Suffix Tree Construction Algorithm
Journal of the ACM (JACM)
Database indexing for large DNA and protein sequence collections
The VLDB Journal — The International Journal on Very Large Data Bases
Finding Maximal Pairs with Bounded Gap
CPM '99 Proceedings of the 10th Annual Symposium on Combinatorial Pattern Matching
Discovering all frequent trends in time series
WISICT '04 Proceedings of the winter international synposium on Information and communication technologies
Genome-scale disk-based suffix tree indexing
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Pairwise sequence alignment algorithms: a survey
Proceedings of the 2009 conference on Information Science, Technology and Applications
Serial and parallel methods for i/o efficient suffix tree construction
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Abstractions in Process Mining: A Taxonomy of Patterns
BPM '09 Proceedings of the 7th International Conference on Business Process Management
Terms derived from frequent sequences for extractive text summarization
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Bioinformatics
ERA: efficient serial and parallel suffix tree construction for very long strings
Proceedings of the VLDB Endowment
Efficient Maximal Repeat Finding Using the Burrows-Wheeler Transform and Wavelet Tree
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Improving time and space complexity for compressed pattern matching
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
Space-Efficient computation of maximal and supermaximal repeats in genome sequences
SPIRE'12 Proceedings of the 19th international conference on String Processing and Information Retrieval
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A wide range of applications, including bioinformatics, time series, and log analysis, depend on the identification of repetitions in very long sequences. The problem of finding maximal pairs subsumes most important types of repetition-finding tasks. Existing solutions require both the input sequence and its index (typically an order of magnitude larger than the input) to fit in memory. Moreover, they are serial algorithms with long execution time. Therefore, they are limited to small datasets, despite the fact that modern applications demand orders of magnitude longer sequences. In this paper we present RACE, a parallel system for finding maximal pairs in very long sequences. RACE supports parallel execution on stand-alone multicore systems, in addition to scaling to thousands of nodes on clusters or supercomputers. RACE does not require the input or the index to fit in memory; therefore, it supports very long sequences with limited memory. Moreover, it uses a novel array representation that allows for cache-efficient implementation. RACE is particularly suitable for the cloud (e.g., Amazon EC2) because, based on availability, it can scale elastically to more or fewer machines during its execution. Since scaling out introduces overheads, mainly due to load imbalance, we propose a cost model to estimate the expected speedup, based on statistics gathered through sampling. The model allows the user to select the appropriate combination of cloud resources based on the provider's prices and the required deadline. We conducted extensive experimental evaluation with large real datasets and large computing infrastructures. In contrast to existing methods, RACE can handle the entire human genome on a typical desktop computer with 16GB RAM. Moreover, for a problem that takes 10 hours of serial execution, RACE finishes in 28 seconds using 2,048 nodes on an IBM BlueGene/P supercomputer.