Automatic trace analysis is an effective method of identifying complex performance phenomena in parallel applications. To simplify the development of complex trace-analysis algorithms, the EARL library interface offers high-level access to individual events contained in a global trace file. However, as the size of parallel systems grows further and the number of processors used by individual applications is continuously raised, the traditional approach of analyzing a single global trace file becomes increasingly constrained by the large number of events. To enable scalable trace analysis, we present a new design of the aforementioned EARL interface that accesses multiple local trace files in parallel while offering means to conveniently exchange events between processes. This article describes the modified view of the trace data as well as related programming abstractions provided by the new PEARL library interface and discusses its application in performance analysis.
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