MicrobenchmarkClusteringKernel {RHPCBenchmark}

Performs microbenchmarking of a clustering for machine learning kernel

Description

MicrobenchmarkClusteringKernel performs microbenchmarking of a clustering for machine learning kernel for a given data set

Usage

MicrobenchmarkClusteringKernel(benchmarkParameters, numberOfThreads,
  resultsDirectory, runIdentifier)

Arguments

benchmarkParameters

an object of type ClusteringMicrobenchmark specifying the data set to be read in or generated and the number of performance trials to perform with the data set.

numberOfThreads

the number of threads the microbenchmark is being performed with. The value is for informational purposes only and does not effect the number threads the kernel is executed with.

resultsDirectory

a character string specifying the directory where all of the CSV performance results files will be saved

runIdentifier

a character string specifying the suffix to be appended to the base of the file name of the output CSV format files

Details

This function performs microbenchmarking of a clustering for machine learning kernel for a given data set and a given number of threads. The kernel to be performance tested and other parameters specifying how the kernel is to be benchmarked are given in the input object benchmarkParameters which is an instance of the class ClusteringMicrobenchmark. The performance results are averaged over the number of performance trials and written to a CSV file. The results of the individual performance trials are retained in a data frame that is returned upon completion of the microbenchmark. The kernel can be executed with multiple threads if the kernel supports multithreading. See ClusteringMicrobenchmark for more details on the benchmarking parameters.

Value

a dataframe containing the performance trial times for the given kernel and data set being tested, that is the raw performance data before averaging. The columns of the data frame are the following:

BenchmarkName

The name of the microbenchmark

NumberOfFeatures

The number of features in each feature vector

NumberOfFeatureVectors

The number of features in the data set

NumberOfClusters

The number of clusters in the data set

UserTime

The amount of time spent in user-mode code within the microbenchmarked code

SystemTime

The amount of time spent in the kernel within the process

WallClockTime

The total time spent to complete the performance trial

DateStarted

The date and time the performance trial was commenced

DateFinished

The date and time the performance trial ended