Simultaneous optimization and evaluation of multiple dimensional queries
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient and extensible algorithms for multi query optimization
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Distributed processing of very large datasets with DataCutter
Parallel Computing - Clusters and computational grids for scientific computing
Efficient execution of multiple query workloads in data analysis applications
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Common Subexpression Processing in Multiple-Query Processing
IEEE Transactions on Knowledge and Data Engineering
Parallel Computation in Biological Sequence Analysis
IEEE Transactions on Parallel and Distributed Systems
Scheduling Multiple Data Visualization Query Workloads on a Shared Memory Machine
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Multiple Query Processing in Deductive Databases using Query Graphs
VLDB '86 Proceedings of the 12th International Conference on Very Large Data Bases
Generalized Search Trees for Database Systems
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A component-based implementation of multiple sequence alignment
Proceedings of the 2003 ACM symposium on Applied computing
Pairwise Distance Matrix Computation for Multiple Sequence Alignment on the Cell Broadband Engine
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
MT-clustalW: multithreading multiple sequence alignment
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Parallel multiple sequence alignment with decentralized cache support
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
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This paper is concerned with the efficient execution of multiple sequence alignment methods in a multiple client environment. Multiple sequence alignment (MSA) is a computationally expensive method, which is commonly used in computational and molecular biology. Large databases of protein and gene sequences are available to the scientific community. Oftentimes, these databases are accessed by multiple users to execute MSA queries. The data server has to handle multiple concurrent queries in such situations. We look at the effect of data caching on the performance of the data server. We describe an approach for caching intermediate results for reuse in subsequent or concurrent queries. We focus on progressive alignment-based strategies, in particular the CLUSTAL W algorithm. Our results for 350 sets of sequences show an average speedup of up to 2.5 is obtained by caching intermediate results. Our results also show that the cache-enabled CLUSTAL W program scales well on a SMP machine.