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As supercomputer performance approached and then surpassed the petaflop level, I/O performance has become a major performance bottleneck for many scientific applications. Several tools exist to collect I/O traces to assist in the analysis of I/O performance problems. However, these tools either produce extremely large trace files that complicate performance analysis, or sacrifice accuracy to collect high-level statistical information. We propose a multi-level trace generator tool, ScalaIOTrace, that collects traces at several levels in the HPC I/O stack. ScalaIOTrace features aggressive trace compression that generates trace files of near constant size for regular I/O patterns and orders of magnitudes smaller for less regular ones. This enables the collection of I/O and communication traces of applications running on thousands of processors.Our contributions also include automated trace analysis to collect selected statistical information of I/O calls by parsing the compressed trace on-the-fly and time-accurate replay of communication events with MPI-IO calls. We evaluated our approach with

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With today's petascale supercomputers, applications often exhibit low efficiency, such as poor communication and I/O performance, that can be diagnosed by analysis tools. However, these tools either produce extremely large trace files that complicate performance analysis, or sacrifice accuracy to collect high-level statistical information using crude averaging. This work contributes Scala-H-Trace, which features more aggressive trace compression than any previous approach, particularly for applications that do not show strict regularity in SPMD behavior. Scala-H-Trace uses histograms expressing the probabilistic distribution of arbitrary communication and I/O parameters to capture variations. Yet, where other tools fail to scale, Scala-H-Trace guarantees trace files of near constant size, even for variable communication and I/O patterns, producing trace files orders of magnitudes smaller than using prior approaches. We demonstrate the ability to collect traces of applications running on thousands of processors with the potential to scale well beyond this level. We further present the first approach to deterministically replay such probabilistic traces (a) without deadlocks and (b) in a manner closely resembling the original applications.Our results show either near constant sized traces or only sublinear increases in trace file sizes irrespective of the number of nodes utilized. Even with the aggressively compressed histogrambased traces, our replay times are within 12% to 15% of the runtime of original codes. Such concise traces resembling the behavior of production-style codes closely and our approach of deterministic replay of probabilistic traces are without precedence.

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