Flash memory efficient LTL model checking

As the capacity and speed of flash memories in form of solid state disks grow, they are becoming a practical alternative for standard magnetic drives. Currently, most solid-state disks are based on NAND technology and much faster than magnetic disks in random reads, while in random writes they are g...

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Bibliographic Details
Published inScience of computer programming Vol. 76; no. 2; pp. 136 - 157
Main Authors Edelkamp, S., Sulewski, D., Barnat, J., Brim, L., Šimeček, P.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.02.2011
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Summary:As the capacity and speed of flash memories in form of solid state disks grow, they are becoming a practical alternative for standard magnetic drives. Currently, most solid-state disks are based on NAND technology and much faster than magnetic disks in random reads, while in random writes they are generally not. So far, large-scale LTL model checking algorithms have been designed to employ external memory optimized for magnetic disks. We propose algorithms optimized for flash memory access. In contrast to approaches relying on the delayed detection of duplicate states, in this work, we design and exploit appropriate hash functions to re-invent immediate duplicate detection. For flash memory efficient on-the-fly LTL model checking, which aims at finding any counter-example to the specified LTL property, we study hash functions adapted to the two-level hierarchy of RAM and flash memory. For flash memory efficient off-line LTL model checking, which aims at generating a minimal counterexample and scans the entire state space at least once, we analyze the effect of outsourcing a memory-based perfect hash function from RAM to flash memory. Since the characteristics of flash memories are different to magnetic hard disks, the existing I/O complexity model is no longer sufficient. Therefore, we provide an extended model for the computation of the I/O complexity adapted to flash memories that has a better fit to the observed behavior of our algorithms.
ISSN:0167-6423
1872-7964
DOI:10.1016/j.scico.2010.03.005