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Path Grammar Guided Trace Compression and Trace Approximation

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3 Author(s)

Trace-driven simulation is an important technique used in the evaluation of computer architecture innovations. However using it for studying parallel computers and applications is at best very challenging. Acquiring, representing and storing the traces are among the major issues. In this paper, we introduce path grammar guided trace compression (PGGTC) and effective address trace approximation (TA) to speedup compression and reduce trace sizes. PGGTC relies on static analysis to build rules and determine actions to guide online trace compression. Combined with gzip, PGGTC can compresses control flow traces over 330 times smaller than using gzip alone. Compared to the widely popular Sequitur algorithm alone, PGGTC with gzip is on average 40 times faster, while the traces are only 3 times bigger. PGGTC can be also used with Sequitur to double the compression ratios of Sequitur by itself and do it 14 times faster than Sequitur by itself. Address traces of parallel applications with significant randomness are often impossibly large even after being compressed with any lossless scheme including PGGTC. For effective address trace reduction, we introduce trace approximation (TA). Performance-wise similar effective addresses are generated based on very compact summaries of how the memory is accessed during each structure instance instead of compressing them. We demonstrate two approaches: selective dumping and memory signatures, to summarize the properties of effective address sequences. Both approaches are validated by feeding the generated approximate trace to cache simulators of 25 different configurations. The simulated results are very close to the simulation results based on full effective traces while the selective dumped address or memory signatures require several order of magnitude less disk space to store. In summary, we move trace-driven simulation into the realm of the feasible for larger parallel machines and applications

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2006 15th IEEE International Conference on High Performance Distributed Computing

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