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https://github.com/aljazceru/kata-containers.git
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fce2487971
This PR adds the metrics report R files. Signed-off-by: Gabriela Cervantes <gabriela.cervantes.tellez@intel.com>
133 lines
3.7 KiB
R
133 lines
3.7 KiB
R
#!/usr/bin/env Rscript
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# Copyright (c) 2018-2023 Intel Corporation
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#
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# SPDX-License-Identifier: Apache-2.0
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# Analyse the runtime component memory footprint data.
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library(ggplot2) # ability to plot nicely.
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# So we can plot multiple graphs
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library(gridExtra) # together.
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suppressMessages(suppressWarnings(library(ggpubr))) # for ggtexttable.
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suppressMessages(library(jsonlite)) # to load the data.
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testnames=c(
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"cpu-information"
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)
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resultsfilesshort=c(
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"CPU"
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)
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data=c()
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rstats=c()
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rstats_rows=c()
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rstats_cols=c()
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Gdenom = (1000.0 * 1000.0 * 1000.0)
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# For each set of results
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for (currentdir in resultdirs) {
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dirstats=c()
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# For the two different types of memory footprint measures
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for (testname in testnames) {
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# R seems not to like double path slashes '//' ?
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fname=paste(inputdir, currentdir, testname, '.json', sep="")
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if ( !file.exists(fname)) {
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warning(paste("Skipping non-existent file: ", fname))
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next
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}
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# Derive the name from the test result dirname
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datasetname=basename(currentdir)
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datasetvariant=resultsfilesshort[count]
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# Import the data
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fdata=fromJSON(fname)
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fdata=fdata[[testname]]
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# Copy the average result into a shorter, more accesible name
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fdata$ips=fdata$Results$"instructions per cycle"$Result
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fdata$Gcycles=fdata$Results$cycles$Result / Gdenom
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fdata$Ginstructions=fdata$Results$instructions$Result / Gdenom
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fdata$variant=rep(datasetvariant, length(fdata$Result) )
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fdata$Runtime=rep(datasetname, length(fdata$Result) )
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# Store away the bits we need
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data=rbind(data, data.frame(
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Result=fdata$ips,
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Type="ips",
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Runtime=fdata$Runtime,
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variant=fdata$variant ) )
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data=rbind(data, data.frame(
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Result=fdata$Gcycles,
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Type="Gcycles",
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Runtime=fdata$Runtime,
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variant=fdata$variant ) )
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data=rbind(data, data.frame(
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Result=fdata$Ginstructions,
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Type="Ginstr",
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Runtime=fdata$Runtime,
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variant=fdata$variant ) )
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# Store away some stats for the text table
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dirstats=rbind(dirstats, round(fdata$ips, digits=2) )
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dirstats=rbind(dirstats, round(fdata$Gcycles, digits=2) )
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dirstats=rbind(dirstats, round(fdata$Ginstructions, digits=2) )
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}
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rstats=cbind(rstats, dirstats)
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rstats_cols=append(rstats_cols, datasetname)
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}
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rstats_rows=c("IPS", "GCycles", "GInstr")
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unts=c("Ins/Cyc", "G", "G")
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rstats=cbind(rstats, unts)
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rstats_cols=append(rstats_cols, "Units")
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# If we have only 2 sets of results, then we can do some more
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# stats math for the text table
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if (length(resultdirs) == 2) {
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# This is a touch hard wired - but we *know* we only have two
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# datasets...
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diff=c()
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for (n in 1:3) {
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difference = (as.double(rstats[n,2]) - as.double(rstats[n,1]))
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val = 100 * (difference/as.double(rstats[n,1]))
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diff=rbind(diff, round(val, digits=2))
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}
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rstats=cbind(rstats, diff)
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rstats_cols=append(rstats_cols, "Diff %")
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}
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# Build us a text table of numerical results
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stats_plot = suppressWarnings(ggtexttable(data.frame(rstats),
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theme=ttheme(base_size=10),
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rows=rstats_rows, cols=rstats_cols
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))
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# plot how samples varioed over 'time'
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ipsdata <- subset(data, Type %in% c("ips"))
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ips_plot <- ggplot() +
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geom_bar(data=ipsdata, aes(Type, Result, fill=Runtime), stat="identity", position="dodge") +
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xlab("Measure") +
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ylab("IPS") +
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ggtitle("Instructions Per Cycle") +
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ylim(0, NA) +
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theme(axis.text.x=element_text(angle=90))
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cycdata <- subset(data, Type %in% c("Gcycles", "Ginstr"))
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cycles_plot <- ggplot() +
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geom_bar(data=cycdata, aes(Type, Result, fill=Runtime), stat="identity", position="dodge", show.legend=FALSE) +
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xlab("Measure") +
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ylab("Count (G)") +
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ggtitle("Cycles and Instructions") +
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ylim(0, NA) +
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theme(axis.text.x=element_text(angle=90))
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master_plot = grid.arrange(
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ips_plot,
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cycles_plot,
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stats_plot,
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nrow=2,
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ncol=2 )
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