paralellize plots
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@ -1,10 +1,18 @@
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library("mosaic")
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library("dplyr")
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library("foreach")
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library("doParallel")
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#setup parallel backend to use many processors
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cores=detectCores()
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cl <- makeCluster(cores[1]-1) #not to overload your computer
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registerDoParallel(cl)
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args = commandArgs(trailingOnly=TRUE)
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if (length(args)==0) {
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runtype="timedump_253048_1873f6/timedump"
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target="waters"
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runtype="timedump_253048_1873f6_full/timedump"
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target="watersv2"
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outputpath="~/code/FRET/LibAFL/fuzzers/FRET/benchmark/"
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#MY_SELECTION <- c('state', 'afl', 'graph', 'random')
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SAVE_FILE=TRUE
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@ -85,57 +93,94 @@ frame2maxlines <- function(tr) {
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return(tr)
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}
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all_maxlines = c()
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for (bn in BASENAMES) {
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trace2maxpoints <- function(tr) {
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minval = tr[1,1]
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collect = tr[1,]
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for (i in seq_len(dim(tr)[1])) {
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if (minval < tr[i,1]) {
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collect = rbind(collect,tr[i,])
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minval = tr[i,1]
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}
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}
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return(collect)
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}
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sample_maxpoints <- function(tr,po) {
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index = 1
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collect=NULL
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for (p in po) {
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done = FALSE
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while (!done) {
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if (p<=tr[1,2] || (index < dim(tr)[1] && tr[index,2] <= p && p < tr[index+1,2])) {
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tmp = tr[index,]
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tmp[2] = p
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collect = rbind(collect, tmp)
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done = TRUE
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} else { if ( p >= tr[dim(tr)[1],2] ) {
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tmp = tr[dim(tr)[1],]
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tmp[2] = p
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collect = rbind(collect, tmp)
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done = TRUE
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} else {
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index = index + 1
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} }
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}
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}
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return(collect)
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}
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#https://www.r-bloggers.com/2012/01/parallel-r-loops-for-windows-and-linux/
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all_runtypetables <- foreach (bn=BASENAMES) %do% {
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runtypefiles <- list.files(file.path(BENCHDIR,bn),pattern=sprintf(PATTERNS,target),full.names = TRUE)
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if (length(runtypefiles) > 0) {
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runtypetables <- lapply(seq_len(length(runtypefiles)),
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function(i)read.csv(runtypefiles[[i]], col.names=c(sprintf("%s%d",bn,i),"times")))
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runtypetables = trim_data(runtypetables)
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runtypetables = lapply(runtypetables, function(i) i[1])
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list_of_maxlines = data2maxlines(runtypetables)
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tmp_frame <- Reduce(bind_cols, list_of_maxlines)
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statframe <- bind_cols(rowMeans(tmp_frame),apply(tmp_frame, 1, sd),apply(tmp_frame, 1, min),apply(tmp_frame, 1, max))
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runtypetables_reduced <- foreach(i=seq_len(length(runtypefiles))) %dopar% {
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rtable = read.csv(runtypefiles[[i]], col.names=c(sprintf("%s%d",bn,i),sprintf("times%d",i)))
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trace2maxpoints(rtable)
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}
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#runtypetables <- lapply(seq_len(length(runtypefiles)),
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# function(i)read.csv(runtypefiles[[i]], col.names=c(sprintf("%s%d",bn,i),sprintf("times%d",i))))
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#runtypetables_reduced <- lapply(runtypetables, trace2maxpoints)
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runtypetables_reduced
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#all_runtypetables = c(all_runtypetables, list(runtypetables_reduced))
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}
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}
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all_runtypetables = all_runtypetables[lapply(all_runtypetables, length) > 0]
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all_points = sort(unique(Reduce(c, lapply(all_runtypetables, function(v) Reduce(c, lapply(v, function(w) w[[2]]))))))
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all_maxlines <- foreach (rtt=all_runtypetables) %dopar% {
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bn = substr(names(rtt[[1]])[1],1,nchar(names(rtt[[1]])[1])-1)
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runtypetables_sampled = lapply(rtt, function(v) sample_maxpoints(v, all_points)[1])
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tmp_frame <- Reduce(cbind, runtypetables_sampled)
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statframe <- data.frame(rowMeans(tmp_frame),apply(tmp_frame, 1, sd),apply(tmp_frame, 1, min),apply(tmp_frame, 1, max))
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names(statframe) <- c(bn, sprintf("%s_sd",bn), sprintf("%s_min",bn), sprintf("%s_max",bn))
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all_maxlines = c(all_maxlines, list(round(statframe)))
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#all_maxlines = append(all_maxlines, list_of_maxlines)
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#mean_maxline<-Reduce(function(a,b) a+b,list_of_maxlines,0)/length(runtypetables)
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#all_maxlines=append(all_maxlines,mean_maxline)
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#statframe[sprintf("%s_times",bn)] = all_points
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round(statframe)
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#all_maxlines = c(all_maxlines, list(round(statframe)))
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}
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}
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min_length <- min(sapply(all_maxlines, function(x) dim(x)[1]))
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all_maxlines=lapply(all_maxlines, function(v) v[1:min_length,])
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one_frame<-data.frame(all_maxlines)
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one_frame[length(one_frame)+1] <- seq_len(length(one_frame[[1]]))
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names(one_frame)[length(one_frame)] <- 'iters'
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one_frame[length(one_frame)+1] <- all_points/(3600 * 1000)
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names(one_frame)[length(one_frame)] <- 'time'
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typenames = names(one_frame)[which(names(one_frame) != 'iters')]
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typenames = names(one_frame)[which(names(one_frame) != 'time')]
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typenames = typenames[which(!endsWith(typenames, "_sd"))]
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ylow=min(one_frame[typenames])
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yhigh=max(one_frame[typenames],worst_case)
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typenames = typenames[which(!endsWith(typenames, "_sd"))]
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typenames = typenames[which(!endsWith(typenames, "_min"))]
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typenames = typenames[which(!endsWith(typenames, "_max"))]
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#yhigh=3400000
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#yhigh=max(one_frame[typenames],405669)
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ml2lines <- function(ml) {
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lines = NULL
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last = -1
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for (i in seq_len(length(ml))) {
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if (ml[[i]] != last || (i >= length(ml))) {
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if (i != 1) {
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lines = rbind(lines, cbind(X=i, Y=last))
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}
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lines = rbind(lines, cbind(X=i, Y=ml[[i]]))
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last=ml[[i]]
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}
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last = 0
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for (i in seq_len(dim(ml)[1])) {
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lines = rbind(lines, cbind(X=last, Y=ml[i,1]))
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lines = rbind(lines, cbind(X=ml[i,2], Y=ml[i,1]))
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last = ml[i,2]
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}
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return(lines)
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}
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plotting <- function(selection, filename, MY_COLORS_) {
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# filter out names of iters and sd cols
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typenames = names(one_frame)[which(names(one_frame) != 'iters')]
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typenames = names(one_frame)[which(names(one_frame) != 'times')]
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typenames = typenames[which(!endsWith(typenames, "_sd"))]
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typenames = typenames[which(!endsWith(typenames, "_min"))]
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typenames = typenames[which(!endsWith(typenames, "_max"))]
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@ -149,16 +194,16 @@ if (SAVE_FILE) {png(file=sprintf("%s%s_%s.png",outputpath,target,filename), widt
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par(mar=c(4,4,1,1))
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par(oma=c(0,0,0,0))
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plot(c(1,length(one_frame[[1]])),c(ylow,yhigh), col='white', xlab="Iters", ylab="WORT", pch='.')
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plot(c(1,max(one_frame['time'])),c(ylow,yhigh), col='white', xlab="Time [h]", ylab="WORT [insn]", pch='.')
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for (t in seq_len(length(typenames))) {
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proj = one_frame[seq(1, dim(one_frame)[1], by=max(1, length(one_frame[[1]])/(10*w_))),]
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#points(proj[c('iters',typenames[t])], col=MY_COLORS_[t], pch='.')
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avglines = ml2lines(one_frame[[typenames[t]]])
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avglines = ml2lines(one_frame[c(typenames[t],'time')])
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lines(avglines, col=MY_COLORS_[t])
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if (exists("RIBBON") && ( RIBBON=='both' || RIBBON=='span')) {
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milines = ml2lines(one_frame[[sprintf("%s_min",typenames[t])]])
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malines = ml2lines(one_frame[[sprintf("%s_max",typenames[t])]])
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milines = ml2lines(one_frame[c(sprintf("%s_min",typenames[t]),'time')])
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malines = ml2lines(one_frame[c(sprintf("%s_max",typenames[t]),'time')])
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lines(milines, col=MY_COLORS_[t], lty='dashed')
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lines(malines, col=MY_COLORS_[t], lty='dashed')
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#points(proj[c('iters',sprintf("%s_min",typenames[t]))], col=MY_COLORS_[t], pch='.')
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@ -167,7 +212,7 @@ for (t in seq_len(length(typenames))) {
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if (exists("RIBBON") && RIBBON != '') {
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for (i in seq_len(dim(proj)[1])) {
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row = proj[i,]
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x_ <- row['iters'][[1]]
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x_ <- row['time'][[1]]
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y_ <- row[typenames[t]][[1]]
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sd_ <- row[sprintf("%s_sd",typenames[t])][[1]]
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min_ <- row[sprintf("%s_min",typenames[t])][[1]]
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@ -203,9 +248,9 @@ if (exists("MY_SELECTION")) {
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#MY_SELECTION=c('state', 'afl', 'random', 'feedlongest', 'feedgeneration', 'feedgeneration10')
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#MY_SELECTION=c('state_int', 'afl_int', 'random_int', 'feedlongest_int', 'feedgeneration_int', 'feedgeneration10_int')
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#MY_SELECTION=c('state', 'frAFL', 'statenohash', 'feedgeneration10')
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#MY_SELECTION=c('state_int', 'frAFL_int', 'statenohash_int', 'feedgeneration10_int')
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MY_SELECTION=c('state_int', 'frAFL_int', 'statenohash_int', 'feedgeneration10_int')
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MY_SELECTION=typenames
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RIBBON='both'
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RIBBON='span'
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for (i in seq_len(length(MY_SELECTION))) {
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n <- MY_SELECTION[i]
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plotting(c(n), n, c(MY_COLORS[i]))
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