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ELAPS Report

Reports are loaded from report files (extension: .elr) through the elaps.io routine load_report(filename). A Report contains a copy of the executed Experiment as well as the resulting measurements.

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Measurement results

The Sampling measurements are available in three data structures: rawdata, fulldata, and data.

rawdata

contains the raw data as produced by the Sampler in the form of a list, each entry of which corresponds to one line in the Sampler's output. Each split by white spaces, where numbers are parsed as such where possible.

###fulldata is a nested data structure that organizes the data according to the Experiment's setup in the following hierarchy:

fulldata[range_val][rep][sumrange_val][callid][counter]
  • range_val: The range value (or None).
  • rep: The repetition number.
  • sumrange_val: the sum-range value (or None).
  • callid: The call number.
  • counterid: Number of the PAPI counter (0 for cycle count).

When sumrange_parallel or calls_parallel are set, the levels sumrange_val and callid are left out accordingly.

###data is a nested data structure containing reduced data according to the Experiment setup in the following hierarchy:

data[range_val][rep][callid][counter]
  • range_val: The range value (or None).
  • rep: The repetition number.
  • callid: The call number or None for a sum across all calls.
  • counter: The PAPI event name, "cycles" or "flops". The latter contains the number of floating point operations involved in the operation, provided that the Call's Signature has its complexity attribute set.

If calls_parallel is set, the level callid is left out.

Dropping first repetitions

The first repetition of an experiment is often an outlier due to, e.g., library or cache initialization. discard_first_repetitions() will discard each first repetition within a Report, resulting in a derived Report object.

Applying metrics

Metrics turn raw measurement data (i.e., cycle counts) into more meaningful quantities, such as "Execution time in seconds" (seconds) or "floating point operations per cycle" (ipc). A set of predefined metrics can be loaded using elaps.io's load_metric(name). Such a metric, which is essentially a python function, can be applied to an experiment (or a subset of its calls) through apply_metric(metric, callid). In addition to valid call numbers, callid can be None to access the sum of all calls.

apply_metric() returns a data structure with the following hierarchy:

metric_data[range_val][rep]
  • range_val: The range value (or None).
  • rep: The repetition number.

Computing statistics

The method apply_stat(stat_name, data) applies a statistics to a data structure as returned by a Report's apply_metric(). The following stat_name's are available:

  • "min": minimum,
  • "med": median,
  • "max": maximum,
  • "avg": average or mean, and
  • "std": standard deviation (square root of the variance).