ddn-ime-evaluation/benchmark/eval_analysis_v2.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 * from p where count!=1 and fs=="gpfs"' )
dbdata[,"blocksize"] = dbdata$t


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




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

	p = ggplot(data=dbdata, aes(x=nn, y=bwMiB, colour=as.factor(blocksize/1024), group=blocksize), ymin=0) +
        #aes(x=nn, y=bwMiB) +
		ggtitle("Independent random access to a shared file with IOR") +
		facet_grid(fs + ppn ~  lab_access + count, labeller = labeller(nn = as_labeller(nn_lab), ppn = as_labeller(ppn_lab))) +
		#facet_grid(fs + ppn ~  lab_access, 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$nn))) +
		scale_x_log10(breaks = c(unique(dbdata$nn))) +
		scale_color_manual(name="Blocksize in KiB: ", values=c('#999999','#E69F00', '#56B4E9', '#000000'), breaks=sort(unique(dbdata$blocksize)/1024)) +
		#stat_summary(fun.y="median", geom="line", aes(group=factor(blocksize))) +
		stat_summary(fun.y="mean", geom="line", aes(group=factor(blocksize))) +
		#geom_boxplot()
        geom_point()
		#geom_point(data=dbdata, aes(x=nn, y=PortRcvData), colour='red') +
		#geom_point(data=dbdata, aes(x=nn, y=PortXmitData), colour='blue')

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

	filename_eps = sprintf("%s/performance_%s.eps", folder_out, scale)
	filename_png = sprintf("%s/performance_%s.png", folder_out, 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))

	p = ggplot(data=dbdata, ymin=0) +
        aes(x=nn, y=(PortXmitData + PortRcvData) * 4 / ppn, colour=as.factor(blocksize/1024), group=blocksize) +
        #aes(x=nn, y=bwMiB) +
        ggtitle('Infiniband throughput (PortRcvData and PortXmitData by "perfquery -x")') +
		facet_grid(fs + ppn ~  lab_access + count, labeller = labeller(nn = as_labeller(nn_lab), ppn = as_labeller(ppn_lab))) +
		#facet_grid(fs + ppn ~  lab_access, 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$nn))) +
		scale_x_log10(breaks = c(unique(dbdata$nn))) +
		scale_color_manual(name="Blocksize in KiB: ", values=c('#999999','#E69F00', '#56B4E9', '#000000'), breaks=sort(unique(dbdata$blocksize)/1024)) +
		stat_summary(fun.y="mean", geom="line", aes(group=factor(blocksize))) +
        #geom_point(data=dbdata, aes(x=nn, y=PortXmitData), colour='blue')
        geom_point()

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

	filename_eps = sprintf("%s/ib_%s.eps", folder_out, scale)
	filename_png = sprintf("%s/ib_%s.png", folder_out, 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))

}