Original script from GATK

biopet-framework/src/main/scripts/org/broadinstitute/gatk/queue/util/queueJobReport.R

+library(gsalib)
+library(ggplot2)
+library(gplots)
+library(tools)
+library(reshape)
+library(plyr)
+
+#
+# Standard command line switch.  Can we loaded interactively for development
+# or executed with RScript
+#
+args = commandArgs(TRUE)
+onCMDLine = ! is.na(args[1])
+if ( onCMDLine ) {
+  inputFileName = args[1]
+  outputPDF = args[2]
+} else {
+  inputFileName = "~/workspaces/scratch/SingleSample_VQSRwNewSOR.jobreport.txt"
+  #inputFileName = "/humgen/gsa-hpprojects/dev/depristo/oneOffProjects/Q-25718@node1149.jobreport.txt"
+  #inputFileName = "/humgen/gsa-hpprojects/dev/depristo/oneOffProjects/rodPerformanceGoals/history/report.082711.txt"
+  outputPDF = NA
+}
+
+RUNTIME_UNITS = "(hours)"
+ORIGINAL_UNITS_TO_RUNTIME_UNITS = 1/1000/60/60
+
+# 
+# Helper function to aggregate all of the jobs in the report across all tables
+#
+allJobsFromReport <- function(report) {
+  names <- c("jobName", "startTime", "analysisName", "doneTime", "exechosts", "runtime")
+  sub <- lapply(report, function(table) table[,names])
+  do.call("rbind", sub)
+}
+
+#
+# Creates segmentation plots of time (x) vs. job (y) with segments for the duration of the job
+#
+plotJobsGantt <- function(gatkReport, sortOverall, title, includeText) {
+  allJobs = allJobsFromReport(gatkReport)
+  if ( sortOverall ) {
+    allJobs = allJobs[order(allJobs$analysisName, allJobs$startTime, decreasing=T), ]
+  } else {
+    allJobs = allJobs[order(allJobs$startTime, decreasing=T), ]
+  }
+  allJobs$index = 1:nrow(allJobs)
+  minTime = min(allJobs$startTime)
+  allJobs$relStartTime = allJobs$startTime - minTime 
+  allJobs$relDoneTime = allJobs$doneTime - minTime
+  allJobs$ganttName = paste(allJobs$jobName, "@", allJobs$exechosts)
+  maxRelTime = max(allJobs$relDoneTime)
+  p <- ggplot(data=allJobs, aes(x=relStartTime, y=index, color=analysisName))
+  p <- p + theme_bw()
+  p <- p + geom_segment(aes(xend=relDoneTime, yend=index), size=1, arrow=arrow(length = unit(0.1, "cm")))
+  if ( includeText )
+    p <- p + geom_text(aes(x=relStartTime, label=ganttName, hjust=0, vjust=-1), size=2)
+  p <- p + xlim(0, maxRelTime * 1.3)
+  p <- p + xlab(paste("Start time, relative to first job", RUNTIME_UNITS))
+  p <- p + ylab("Job number")
+  p <- p + ggtitle(title)
+  print(p)
+}
+
+#
+# Plots scheduling efficiency at job events
+#
+plotProgressByTime <- function(gatkReport) {
+  allJobs = allJobsFromReport(gatkReport)
+  nJobs = dim(allJobs)[1]
+  allJobs = allJobs[order(allJobs$startTime, decreasing=F),]
+  allJobs$index = 1:nrow(allJobs)
+
+  minTime = min(allJobs$startTime)
+  allJobs$relStartTime = allJobs$startTime - minTime
+  allJobs$relDoneTime = allJobs$doneTime - minTime
+
+  times = sort(c(allJobs$relStartTime, allJobs$relDoneTime))
+
+  countJobs <- function(p) {
+    s = allJobs$relStartTime
+    e = allJobs$relDoneTime
+    x = c() # I wish I knew how to make this work with apply
+    for ( time in times )
+      x = c(x, sum(p(s, e, time)))
+    x
+  }
+
+  pending = countJobs(function(s, e, t) s > t)
+  done = countJobs(function(s, e, t) e < t)
+  running = nJobs - pending - done
+
+  d = data.frame(times=times, pending=pending, running=running, done=done)
+  
+  p <- ggplot(data=melt(d, id.vars=c("times")), aes(x=times, y=value, color=variable))
+  p <- p + facet_grid(variable ~ ., scales="free")
+  p <- p + geom_line(size=2)
+  p <- p + xlab(paste("Time since start of first job", RUNTIME_UNITS))
+  p <- p + ggtitle("Job scheduling")
+  print(p)
+}
+
+# 
+# Creates tables for each job in this group
+#
+standardColumns = c("jobName", "startTime", "formattedStartTime", "analysisName", "intermediate", "exechosts", "formattedDoneTime", "doneTime", "runtime")
+plotGroup <- function(groupTable) {
+  name = unique(groupTable$analysisName)[1]
+  groupAnnotations = setdiff(names(groupTable), standardColumns)  
+  sub = groupTable[,c("jobName", groupAnnotations, "runtime")]
+  sub = sub[order(sub$iteration, sub$jobName, decreasing=F), ]
+  
+  # create a table showing each job and all annotations
+  textplot(sub, show.rownames=F)
+  title(paste("Job summary for", name, "full itemization"), cex=3)
+
+  # create the table for each combination of values in the group, listing iterations in the columns
+  sum = cast(melt(sub, id.vars=groupAnnotations, measure.vars=c("runtime")), ... ~ iteration, fun.aggregate=mean)
+  textplot(as.data.frame(sum), show.rownames=F)
+  title(paste("Job summary for", name, "itemizing each iteration"), cex=3)
+
+  # histogram of job times by groupAnnotations
+  if ( length(groupAnnotations) == 1 && dim(sub)[1] > 1 ) {
+    # todo -- how do we group by annotations?
+    p <- ggplot(data=sub, aes(x=runtime)) + geom_histogram()
+    p <- p + xlab(paste("runtime", RUNTIME_UNITS)) + ylab("No. of jobs")
+    p <- p + ggtitle(paste("Job runtime histogram for", name))
+    print(p)
+  }
+  
+  # as above, but averaging over all iterations
+  groupAnnotationsNoIteration = setdiff(groupAnnotations, "iteration")
+  if ( dim(sub)[1] > 1 ) {
+    try({ # need a try here because we will fail to reduce when there's just a single iteration
+      sum = cast(melt(sub, id.vars=groupAnnotationsNoIteration, measure.vars=c("runtime")), ... ~ ., fun.aggregate=c(mean, sd))
+      textplot(as.data.frame(sum), show.rownames=F)
+      title(paste("Job summary for", name, "averaging over all iterations"), cex=3)
+    }, silent=T)
+  }
+}
+    
+# print out some useful basic information
+print("Report")
+print(paste("Project          :", inputFileName))
+
+convertUnits <- function(gatkReportData) {
+  convertGroup <- function(g) {
+    g$runtime = g$runtime * ORIGINAL_UNITS_TO_RUNTIME_UNITS
+    g$startTime = g$startTime * ORIGINAL_UNITS_TO_RUNTIME_UNITS
+    g$doneTime = g$doneTime * ORIGINAL_UNITS_TO_RUNTIME_UNITS
+    g
+  }
+  lapply(gatkReportData, convertGroup)
+}
+
+#
+# Plots runtimes by analysis name and exechosts
+#
+# Useful to understand the performance of analysis jobs by hosts, 
+# and to debug problematic nodes
+# 
+plotTimeByHost <- function(gatkReportData) {
+  fields = c("analysisName", "exechosts", "runtime")
+  
+  runtimes = data.frame()
+  for ( report in gatkReportData ) {
+    runtimes = rbind(runtimes, report[,fields])
+  }
+  
+  plotMe <- function(name, vis) {
+    p = ggplot(data=runtimes, aes(x=exechosts, y=runtime, group=exechosts, color=exechosts))
+    p = p + facet_grid(analysisName ~ ., scale="free")
+    p = p + vis()
+    p = p + xlab("Job execution host")
+    p = p + ggtitle(paste(name, "of job runtimes by analysis name and execution host"))
+    p = p + ylab(paste("Distribution of runtimes", RUNTIME_UNITS))
+    p = p + theme(axis.text.x=element_text(angle=45, hjust=1, vjust=1))
+    print(p)
+  }
+  
+  plotMe("Boxplot", geom_boxplot)
+  plotMe("Jittered points", geom_jitter)
+}
+
+mergeScattersForAnalysis <- function(table) {
+  #allJobs$ganttName = paste(allJobs$jobName, "@", allJobs$exechosts)
+  
+  ddply(table, .(analysisName, iteration), summarize,
+        jobName = analysisName[1],
+        exechosts = paste(length(exechosts), "hosts"),
+        formattedStartTime = "NA",
+        formattedDoneTime = "NA",
+        intermediate = intermediate[1],
+        startTime = min(startTime),
+        doneTime = min(startTime) + sum(runtime),
+        runtime = sum(runtime))
+}
+
+mergeScatters <- function(report) {
+  newReport = list()
+  for ( name in names(gatkReportData) ) {
+    newReport[[name]] = mergeScattersForAnalysis(gatkReportData[[name]])
+  }
+  newReport
+}
+  
+# read the table
+gatkReportData <- gsa.read.gatkreport(inputFileName)
+gatkReportData <- convertUnits(gatkReportData)
+#print(summary(gatkReportData))
+
+if ( ! is.na(outputPDF) ) {
+  pdf(outputPDF, height=8.5, width=11)
+} 
+
+plotJobsGantt(gatkReportData, T, "All jobs, by analysis, by start time", F)
+plotJobsGantt(gatkReportData, F, "All jobs, sorted by start time", F)
+plotProgressByTime(gatkReportData)
+
+# plots summarizing overall costs, merging scattered counts
+merged.by.scatter = mergeScatters(gatkReportData) 
+plotJobsGantt(merged.by.scatter, F, "Jobs merged by scatter by start time", T)
+
+merged.as.df = do.call(rbind.data.frame, merged.by.scatter)[,c("analysisName", "runtime")]
+merged.as.df$percent = merged.as.df$runtime / sum(merged.as.df$runtime) * 100
+merged.as.df.formatted = data.frame(analysisName=merged.as.df$analysisName,runtime=prettyNum(merged.as.df$runtime), percent=prettyNum(merged.as.df$percent,digits=2))
+textplot(merged.as.df.formatted[order(merged.as.df$runtime),], show.rownames=F)
+title("Total runtime for each analysis")
+
+plotTimeByHost(gatkReportData)
+for ( group in gatkReportData ) {
+  #print(group)
+  plotGroup(group)
+}
+  
+if ( ! is.na(outputPDF) ) {
+  dev.off()
+  if (exists("compactPDF")) {
+    compactPDF(outputPDF)
+  }
+}