ddn-ime-evaluation/benchmark/eval_analysis.R

#!/usr/bin/env Rscript

library(sqldf)
library(plyr)
library(plot3D)
library(ggplot2)


args = commandArgs(trailingOnly=TRUE)
print(args)
if (2 != length(args)) {
	print("Requires 2 parameters)")
	q()
}

file_db = args[1]
folder_out = args[2]
print(file_db)

make_facet_label <- function(variable, value){
  return(paste0(value, " KiB"))
}


#connection = dbConnect(SQLite(), dbname='results.ddnime.db')
print(file_db)
connection = dbConnect(SQLite(), dbname=file_db)

#dbdata = dbGetQuery(connection,'select mnt, siox, avg(duration) as ad, app, procs, blocksize from p group by mnt, siox, procs, blocksize, app')
#dbdata = dbGetQuery(connection,'select *  from p where tag=="mpio-individual"')
#dbdata = dbGetQuery(connection,'select *, (x*y*z) as blocksize from p where count=8')
#dbdata = dbGetQuery(connection,'select * from p where count<5')
dbdata = dbGetQuery(connection,'select * from p where (ppn==1 or ppn=4 or ppn=8) and count=1' )
dbdata[,"blocksize"] = dbdata$tsize


summary(dbdata)

nn_lab <- sprintf(fmt="NN=%d", unique(dbdata$nn))
names(nn_lab) <- unique(dbdata$nn)
ppn_lab <- sprintf(fmt="PPN=%d", unique(dbdata$ppn))
names(ppn_lab) <- unique(dbdata$ppn)
breaks <- c(unique(dbdata$blocksize))


dbdata$lab_access <- dbdata$type
dbdata$lab_access[dbdata$lab_access == "write"] = "Write"
dbdata$lab_access[dbdata$lab_access == "read"] = "Read"



#fig_w = 4
#fig_h = 4

#w = c(4, 6, 4) 
#h = c(4, 4, 4) 
#event = c("paper", "isc-pres", "poster")
#dims_list = data.frame(h, w, event)       # df is a data frame



for (scale in c("linear", "logarithmic")) {



fss = unique(dbdata$fs)
for (fs in fss) {
data1 = dbdata[fs == dbdata$fs, ]
#apis = unique(data1$api)

print(fs)

#for (api in apis) {
#data2 = data1[api == data1$api, ]
apis = unique(data1$api)

#print(api)

for (api in apis) {
data3 = data1[api == data1$api, ]
types = unique(data3$type)

#print(api)

for (type in types) {
data = data3[type == data3$type, ]

print(type)

 p = ggplot(data=data, aes(x=nn, y=bwMiB, colour=as.factor(t/1024), group=t), ymin=0) +
	 #ggtitle("Write") +
	 facet_grid(ppn ~  api + type + iteration , labeller = labeller(nn = as_labeller(nn_lab), ppn = as_labeller(ppn_lab))) +
	 xlab("Nodes") +
	 ylab("Performance in MiB/s") +
	 theme(axis.text.x=element_text(angle=90, hjust=0.95, vjust=0.5)) +
	 theme(legend.position="bottom") +
	 #scale_x_continuous(breaks = c(unique(data$nn))) +
	 scale_x_log10(breaks = c(unique(data$nn))) +
	 scale_color_manual(name="Blocksize in KiB: ", values=c('#999999','#E69F00', '#56B4E9', '#000000'), breaks=sort(unique(data$t)/1024)) +
	 #stat_summary(fun.y="median", geom="line", aes(group=factor(t))) +
	 stat_summary(fun.y="mean", geom="line", aes(group=factor(t))) +
	 #geom_boxplot()
	 geom_point()

 if ( "logarithmic" == scale ) {
	 p = p + scale_y_log10()
 }

 filename_eps = sprintf("%s/performance_%s_%s_%s_%s.eps", folder_out, api, fs, type, scale)
 filename_png = sprintf("%s/performance_%s_%s_%s_%s.png", folder_out, api, fs, type, scale)
 ggsave(filename_png, width = 10, height = 8)
 ggsave(filename_eps, width = 10, height = 8)
 #system(sprintf("epstopdf %s", filename_eps))
 system(sprintf("rm %s", filename_eps))

}}}}