Performance evaluation tools enable analysts to shed light on how applications behave both from a general point of view and at concrete execution points, but cannot provide detailed information beyond the monitored regions of code. Having the ability to determine when and which data has to be collected is crucial for a successful analysis. This is particularly true for trace-based tools, which can easily incur either unmanageable large traces or information shortage. In order to mitigate the well-known resolution vs. usability trade-off, we present a procedure that obtains fine grain performance information using coarse grain sampling, projecting performance metrics scattered all over the execution into thoroughly detailed representative areas. This mechanism has been incorporated into the MPItrace tracing suite, greatly extending the amount of performance information gathered from statically instrumented points with further periodic samples collected beyond them. We have applied this solution to the analysis of two applications to introduce a novel performance analysis methodology based on the combination of instrumentation and sampling techniques.

Peer Reviewed