PrintClusteringMicrobenchmarkResults {RHPCBenchmark}

Prints results of a clustering for machine learning microbenchmark

Description

PrintClusteringMicrobenchmarkResults prints performance results for a clustering for machine learning microbenchmark to standard output in a format that is easily human readable

Usage

PrintClusteringMicrobenchmarkResults(benchmarkName, numberOfThreads,
  numberOfFeatures, numberOfFeatureVectors, numberOfClusters,
  numberOfSuccessfulTrials, trialTimes, averageWallClockTimes,
  standardDeviations)

Arguments

benchmarkName

character string specifying the name of the microbenchmark

numberOfThreads

the number of threads all of the performance trials were conducted with

numberOfFeatures

the number of features, i.e. the dimension of the feature vector

numberOfFeatureVectors

the number of feature vectors in the data set

numberOfClusters

the number of clusters in the data set

numberOfSuccessfulTrials

an integer vector specifying the number of performance trials that were successfully performed for each data set

trialTimes

a real matrix with each column containing the run times of all of the successful performance trials associated with a particular data set. The number of valid entries in each column are specified by the entries in the numberOfSuccessfulTrials vector

averageWallClockTimes

a vector of average wall clock times computed for each matrix tested during the performance trials

standardDeviations

a vector of standard deviations of the wall clock times obtained for each matrix tested during the performance trials

Details

This function prints the performance results obtained by a clustering for machine learning microbenchmark. Summary run time performance statistics for each clustering data set tested are computed and printed. The summary statistics include the minimum, maximum, average, and standard deviation of the wall clock times obtained by the performance trials with respect to each data tested.