This work investigates the leverage that can be obtained from compiler optimization techniques for efficient execution of multi-query workloads in data analysis applications. Our approach is to address multi-query optimization at the algorithmic level, by transforming a declarative specification of scientific data analysis queries into a high-level imperative program that can be made more efficient by applying compiler optimization techniques. These techniques - including loop fusion, common subexpression elimination and dead code elimination are employed to allow data and computation reuse across queries. We describe a preliminary experimental analysis on a real remote sensing application that analyzes very large quantities of satellite data. The results show our techniques achieve sizable reductions in the amount of computation and I/O necessary for executing query batches and in average execution times for the individual queries in a given batch. © Springer-Verlag 2004.
CITATION STYLE
Andrade, H., Aryangat, S., Kurc, T., Saltz, J., & Sussman, A. (2004). Efficient execution of multi-query data analysis batches using compiler optimization strategies. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2958, 509–523. https://doi.org/10.1007/978-3-540-24644-2_33
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