diff --git a/eval_analysis.R b/eval_analysis.R
new file mode 100755
index 000000000..228812a2f
--- /dev/null
+++ b/eval_analysis.R
@@ -0,0 +1,143 @@
+#!/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')
+dbdata[,"blocksize"] = dbdata$x * dbdata$y * dbdata$z * 4
+
+
+summary(dbdata)
+
+nn_lab <- sprintf(fmt="NN=%d", unique(dbdata$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))
+
+
+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
+
+
+
+
+fss = unique(dbdata$fs)
+for (fs in fss) {
+data1 = dbdata[fs == dbdata$fs, ]
+ifaces = unique(data1$iface)
+
+for (iface in ifaces) {
+data2 = data1[iface == data1$iface, ]
+apps = unique(data2$app)
+
+for (app in apps) {
+data3 = data2[app == data2$app, ]
+types = unique(data3$type)
+
+for (type in types) {
+data4 = data3[type == data3$type, ]
+chunkeds = unique(data4$chunked)
+
+for (chunked in chunkeds) {
+data5 = data4[chunked == data4$chunked, ]
+filleds = unique(data4$filled)
+
+for (filled in filleds) {
+data6 = data5[filled == data5$filled, ]
+unlimiteds = unique(data5$unlimited)
+
+for (unlimited in unlimiteds) {
+data = data6[unlimited == data5$unlimited, ]
+
+	ggplot(data=data, aes(x=nn, y=write, colour=as.factor(blocksize/1024), group=blocksize), ymin=0) +
+		#ggtitle("Write") +
+		facet_grid(ppn ~ ., 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_y_log10() +
+		scale_x_continuous(breaks = c(unique(data$nn))) +
+		scale_color_manual(name="Blocksize in KiB: ", values=c('#999999','#E69F00', '#56B4E9', '#000000'), breaks=sort(unique(data$blocksize)/1024)) +
+		#stat_summary(fun.y="median", geom="line", aes(group=factor(blocksize))) +
+		stat_summary(fun.y="max", geom="line", aes(group=factor(blocksize))) +
+		#geom_boxplot()
+		geom_point()
+	filename_eps = sprintf("%s/performance_%s_%s_%s_%s_CHUNK:%s_FILL:%s_LIM:%s_%s.eps", folder_out, app, fs, iface, type, chunked, filled, unlimited, "write")
+	filename_png = sprintf("%s/performance_%s_%s_%s_%s_CHUNK:%s_FILL:%s_LIM:%s_%s.png", folder_out, app, fs, iface, type, chunked, filled, unlimited, "write")
+	ggsave(filename_png, width = 6, height = 4)
+	ggsave(filename_eps, width = 6, height = 4)
+	system(sprintf("epstopdf %s", filename_eps))
+	system(sprintf("rm %s", filename_eps))
+
+	ggplot(data=data, aes(x=nn, y=read, colour=as.factor(blocksize/1024), group=blocksize), ymin=0) +
+		#ggtitle("Read") +
+		facet_grid(ppn ~ ., 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_y_log10() +
+		scale_x_continuous(breaks = c(unique(data$nn))) +
+		scale_color_manual(name="Blocksize in KiB: ", values=c('#999999','#E69F00', '#56B4E9', '#000000'), breaks=sort(unique(data$blocksize)/1024)) +
+		#stat_summary(fun.y="median", geom="line", aes(group=factor(blocksize))) +
+		stat_summary(fun.y="max", geom="line", aes(group=factor(blocksize))) +
+		#geom_boxplot()
+		geom_point()
+	filename_eps = sprintf("%s/performance_%s_%s_%s_%s_CHUNK:%s_FILL:%s_LIM:%s_%s.eps", folder_out, app, fs, iface, type, chunked, filled, unlimited, "read")
+	filename_png = sprintf("%s/performance_%s_%s_%s_%s_CHUNK:%s_FILL:%s_LIM:%s_%s.png", folder_out, app, fs, iface, type, chunked, filled, unlimited, "read")
+	ggsave(filename_png, width = 6, height = 4)
+	ggsave(filename_eps, width = 6, height = 4)
+	system(sprintf("epstopdf %s", filename_eps))
+	system(sprintf("rm %s", filename_eps))
+
+
+	#ggplot(data=data, aes(x=blocksize, y=read, colour=app, group=blocksize)) +
+	#  ggtitle("Read") +
+	#  facet_grid(ppn ~ nn, labeller = labeller(nn = as_labeller(nn_lab), ppn = as_labeller(ppn_lab))) +
+	#  xlab("Blocksize in KiB") +
+	#  ylab("Performance in MiB/s") +
+	#  theme(axis.text.x=element_text(angle=90, hjust=0.95, vjust=0.5)) +
+	#  scale_y_log10() +
+	#  scale_x_log10(breaks = breaks, labels=breaks/1024) +
+	#  geom_boxplot()
+	##geom_line() +
+	##geom_point()
+	#filename_eps = sprintf("%s/performance_%s_%s_%s_%s_%s.eps", folder_out, app, fs, iface, type, "read")
+	#ggsave(filename_eps, width = 8, height = 6)
+	##system(sprintf("epstopdf %s", filename_eps))
+
+}}}}}}}
diff --git a/eval_netcdfbench.R b/eval_netcdfbench.R
new file mode 100755
index 000000000..582c29d80
--- /dev/null
+++ b/eval_netcdfbench.R
@@ -0,0 +1,134 @@
+#!/usr/bin/env Rscript
+
+library(sqldf)
+library(plyr)
+library(plot3D)
+library(ggplot2)
+library(gtools)
+
+
+args = commandArgs(trailingOnly=TRUE)
+#print(args)
+#if (2 != length(args)) {
+	#print("Requires 2 parameters)")
+	#q()
+#}
+
+#file_db = args[1]
+#folder_out = args[2]
+
+file_db = 'results_benchtool.db'
+folder_out = 'plot_coll_chunked'
+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 nn==10 and ppn=8')
+dbdata[,"blocksize"] = dbdata$x * dbdata$y * dbdata$z * 4
+
+
+summary(dbdata)
+
+nn_lab <- sprintf(fmt="NN %d", unique(dbdata$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))
+
+
+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('log', 'linear')) {
+
+fss = unique(dbdata$fs)
+for (fs in fss) {
+data1 = dbdata[fs == dbdata$fs, ] # ime, lustre, fuse
+ifaces = unique(data1$iface)
+
+for (iface in ifaces) {
+data2 = data1[iface == data1$iface, ] # posix, mpio, ime
+apps = unique(data2$app)
+
+for (app in apps) {
+data3 = data2[app == data2$app, ] # ior, benchtool
+filleds = unique(data3$filled)
+
+for (filled in filleds) {
+data4 = data3[filled == data2$filled, ]
+unlimiteds = unique(data4$unlimited)
+
+for (unlimited in unlimiteds) {
+data = data4[unlimited == data3$unlimited, ]
+
+	dat_write <- data.frame(chunked=data$chunked, type=data$type, perf=data$write, blocksize=data$blocksize, access="write")
+	dat_read <-  data.frame(chunked=data$chunked, type=data$type, perf=data$read, blocksize=data$blocksize, access="read")
+
+	dat <- rbind(dat_write, dat_read)
+	#dat$lab <- paste0(dat$blocksize/1024, "/", dat$access)
+	dat$lab <- paste0(dat$blocksize/1024)
+	dat$lab_type <- data$type;
+	dat$lab_type[dat$lab_type == "coll"] = "Collective"
+	dat$lab_type[dat$lab_type == "ind"] = "Independent"
+	dat$lab_chunked <- data$chunked;
+	dat$lab_chunked[dat$lab_chunked == "auto"] = "Chunking"
+	dat$lab_chunked[dat$lab_chunked == "notset"] = "No chunking"
+	print(summary(dat))
+
+
+	print(mixedsort(unique(dat$lab)))
+
+
+	p <- ggplot(data=dat, aes(x=lab, y=perf, colour=as.factor(blocksize/1024), group=interaction(access, blocksize)), ymin=0) +
+		#ggtitle("Write") +
+		#facet_grid(ppn ~ ., labeller = labeller(nn = as_labeller(nn_lab), ppn = as_labeller(ppn_lab))) +
+		facet_grid(. ~ lab_type + lab_chunked + access) +
+		xlab("Blocksizes in KiB / Access") +
+		ylab("Performance in MiB/s") +
+		theme(axis.text.x=element_text(angle=90, hjust=0.95, vjust=0.5)) +
+		theme(legend.position="bottom") +
+		#ylim(0, 100000) +
+		#scale_x_continuous(breaks = c(unique(data$nn))) +
+		#scale_color_manual(name="Blocksize in KiB: ", values=c('#999999','#E69F00', '#56B4E9', '#000000'), breaks=sort(unique(data$blocksize)/1024)) +
+		scale_color_manual(name="Blocksize in KiB: ", values=c('#999999','#E69F00', '#56B4E9', '#000000', '#999999','#E69F00', '#56B4E9', '#000000'), breaks=mixedsort(unique(dat$lab))) +
+		scale_fill_manual(name="Blocksize in KiB: ", values=c('#999999','#E69F00', '#56B4E9', '#000000', '#999999','#E69F00', '#56B4E9', '#000000'), breaks=mixedsort(unique(dat$lab))) +
+		#stat_summary(fun.y="median", geom="line", aes(group=factor(blocksize))) +
+		#stat_summary(fun.y="max", geom="line", aes(group=factor(blocksize))) +
+		geom_boxplot()
+		#geom_bar()
+		#geom_point()
+
+		if ('log' == scale) {
+			p = p + scale_y_log10(breaks=c(100, 1000, 10000, 40000))
+		}
+		{
+			p = p + scale_x_discrete(breaks=mixedsort(unique(dat$lab)), limits=mixedsort(unique(dat$lab)))
+		}
+
+
+	filename_eps = sprintf("%s/performance_%s_FS:%s_IFACE:%s_FILLED_%s_LIM:%s_SCALE:%s.eps", folder_out, app, fs, iface, filled, unlimited, scale)
+	filename_png = sprintf("%s/performance_%s_FS:%s_IFACE:%s_FILLED_%s_LIM:%s_SCALE:%s.png", folder_out, app, fs, iface, filled, unlimited, scale)
+	ggsave(filename_eps, width = 8, height = 8)
+	ggsave(filename_png, width = 8, height = 8)
+	system(sprintf("epstopdf %s", filename_eps))
+	system(sprintf("rm %s", filename_eps))
+
+}}}}}}
+#}}
diff --git a/eval_openclose.R b/eval_openclose.R
new file mode 100755
index 000000000..914c182a0
--- /dev/null
+++ b/eval_openclose.R
@@ -0,0 +1,111 @@
+#!/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 *, (nn*ppn) as procs from p ')
+dbdata[,"blocksize"] = dbdata$x * dbdata$y * dbdata$z * 4
+
+
+summary(dbdata)
+
+nn_lab <- sprintf(fmt="NN %d", unique(dbdata$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))
+
+
+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
+
+
+
+
+fss = unique(dbdata$fs) # lustre, ime, fuse
+for (fs in fss) {
+data1 = dbdata[fs == dbdata$fs, ]
+ifaces = unique(data1$iface)
+
+for (iface in ifaces) {  # mpio, ime, posix
+data2 = data1[iface == data1$iface, ]
+apps = unique(data2$app)
+
+for (app in apps) { # benchtool, ior
+data = data2[app == data2$app, ]
+
+
+	#ggplot(data=data, aes(x=nn, y=ropen, colour=as.factor(blocksize/1024), group=blocksize), ymin=0) +
+	ggplot(data=data, aes(x=nn, y=ropen, color=ppn)) +
+		geom_point() +
+		xlab("Nodes") +
+		ylab("Duration in sec") +
+		theme(axis.text.x=element_text(angle=90, hjust=0.95, vjust=0.5)) +
+		theme(legend.position="right") +
+		#scale_y_log10() +
+		scale_x_continuous(breaks = c(unique(data$nn))) +
+    geom_smooth(data=data[data$ppn==8,]) + 
+    geom_smooth(data=data[data$ppn==6,]) + 
+    geom_smooth(data=data[data$ppn==4,]) + 
+    geom_smooth(data=data[data$ppn==2,]) + 
+    geom_smooth(data=data[data$ppn==1,]) + 
+    scale_colour_gradientn(colours = rainbow(7))   # + geom_abline(slope=0.1089, intercept=0.1315)
+	filename_eps = sprintf("%s/performance_%s_%s_%s_%s.eps", folder_out, app, fs, iface, "readopen")
+	ggsave(filename_eps, width = 6, height = 4)
+	system(sprintf("epstopdf %s", filename_eps))
+	system(sprintf("rm %s", filename_eps))
+
+	ggplot(data=data, aes(x=nn, y=wopen, color=ppn)) +
+		geom_point() +
+		xlab("Nodes") +
+		ylab("Duration in sec") +
+		theme(axis.text.x=element_text(angle=90, hjust=0.95, vjust=0.5)) +
+		theme(legend.position="right") +
+		#scale_y_log10() +
+		scale_x_continuous(breaks = c(unique(data$nn))) +
+    geom_smooth(data=data[data$ppn==8,]) + 
+    geom_smooth(data=data[data$ppn==6,]) + 
+    geom_smooth(data=data[data$ppn==4,]) + 
+    geom_smooth(data=data[data$ppn==2,]) + 
+    geom_smooth(data=data[data$ppn==1,]) + 
+    scale_colour_gradientn(colours = rainbow(7))   # + geom_abline(slope=0.1089, intercept=0.1315)
+	filename_eps = sprintf("%s/performance_%s_%s_%s_%s.eps", folder_out, app, fs, iface, "writeopen")
+	ggsave(filename_eps, width = 6, height = 4)
+	system(sprintf("epstopdf %s", filename_eps))
+	system(sprintf("rm %s", filename_eps))
+
+
+
+}}}
diff --git a/eval_performance.R b/eval_performance.R
new file mode 100755
index 000000000..e7037cb52
--- /dev/null
+++ b/eval_performance.R
@@ -0,0 +1,207 @@
+#!/usr/bin/env Rscript
+
+library(sqldf)
+library(plyr)
+library(plot3D)
+library(ggplot2)
+require(gridExtra)
+
+
+theme_set(theme_gray(base_size = 25))
+
+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 iface="mpio" and (ppn==1 or ppn=4 or ppn=8)')
+dbdata = dbGetQuery(connection,'select * from p where (ppn==1 or ppn=4 or ppn=8)')
+dbdata[,"blocksize"] = dbdata$x * dbdata$y * dbdata$z * 4
+
+
+#summary(dbdata)
+
+nn_lab <- sprintf(fmt="NN=%d", unique(dbdata$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))
+
+
+#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('log', 'linear')) {
+fss = unique(dbdata$fs)
+for (fs in fss) {
+data1 = dbdata[fs == dbdata$fs, ]
+#apps = unique(data1$app)
+ifaces = unique(data1$iface)
+
+for (iface in ifaces) {
+data2 = data1[iface == data1$iface, ]
+apps = unique(data2$app)
+
+for (app in apps) {
+data3 = data2[app == data2$app, ]
+types = unique(data3$type)
+
+for (type in types) {
+data4 = data3[type == data3$type, ]
+chunkeds = unique(data4$chunked)
+
+for (chunked in chunkeds) {
+data5 = data4[chunked == data4$chunked, ]
+filleds = unique(data4$filled)
+
+for (filled in filleds) {
+data6 = data5[filled == data5$filled, ]
+unlimiteds = unique(data5$unlimited)
+
+for (unlimited in unlimiteds) {
+data = data6[unlimited == data5$unlimited, ]
+
+	dat_write <- data.frame(real_perf=data$fsize/data$wio/1024^2, nn=data$nn, ppn=data$ppn, iface=data$iface, chunked=data$chunked, type=data$type, perf=data$write, blocksize=data$blocksize, access="write", tio="wio", stringsAsFactors = FALSE)
+	dat_read <- data.frame(real_perf=data$fsize/data$rio/1024^2, nn=data$nn, ppn=data$ppn, iface=data$iface, chunked=data$chunked, type=data$type, perf=data$read, blocksize=data$blocksize, access="read", tio="rio", stringsAsFactors = FALSE)
+	dat <- rbind(dat_write, dat_read)
+
+
+	dat$lab_ppn <- paste0("PPN=", dat$ppn)
+
+	dat$lab_iface <- dat$iface
+	dat$lab_iface[dat$lab_iface == "posix"] = "POSIX"
+	dat$lab_iface[dat$lab_iface == "mpio"] = "MPIIO"
+	dat$lab_iface[dat$lab_iface == "ime"] = "IME"
+	
+	dat$lab_access <- dat$access
+	dat$lab_access[dat$lab_access == "write"] = "Write"
+	dat$lab_access[dat$lab_access == "read"] = "Read"
+
+
+	print(summary(dat))
+	#print(dat[0:10])
+
+
+#library(ggplot2)
+#p = qplot(1, 1)
+#grid.arrange(p, p, respect=TRUE) # both viewports are square
+#grid.arrange(p, p, respect=TRUE, heights=c(1,2)) # relative heights
+
+#p1 = p + theme(aspect.ratio=3)
+#grid.arrange(p,p1, respect=TRUE) # one is square, the other thinner
+
+
+for (print_legend in c("yes", "no")) {
+
+	#p <- ggplot(data=dat, aes(x=nn, y=perf, colour=as.factor(blocksize/1024), group=blocksize), ymin=0) +
+	p <- ggplot(data=dat, aes(x=nn, y=real_perf, colour=as.factor(blocksize/1024), group=blocksize), ymin=0) +
+		#ggtitle("Write") +
+		#facet_grid(ppn ~ ., labeller = labeller(nn = as_labeller(lab), ppn = as_labeller(lab))) +
+		#facet_grid(lab_ppn ~ lab_iface + lab_access) +
+		facet_grid(lab_ppn ~ lab_access) +
+		xlab("Nodes") +
+		ylab("Performance in MiB/s") +
+		theme(axis.text.x=element_text(angle=90, hjust=0.95, vjust=0.5)) +
+		#scale_y_log10() +
+		scale_x_continuous(breaks = c(unique(data$nn))) +
+		scale_color_manual(name="Blocksize in KiB: ", values=c('#999999','#E69F00', '#56B4E9', '#000000'), breaks=sort(unique(data$blocksize)/1024)) +
+		#stat_summary(fun.y="median", geom="line", aes(group=factor(blocksize))) +
+		stat_summary(fun.y="max", geom="line", aes(group=factor(blocksize))) +
+		#coord_fixed(ratio=1) +
+		theme(aspect.ratio=1) +
+		theme(plot.margin=grid::unit(c(0,0,0,0), "mm")) +
+		#geom_boxplot()
+		geom_point()
+
+
+#grid.arrange(p, p, respect=TRUE) # both viewports are square
+
+
+#p1 = p + theme(aspect.ratio=3)
+#grid.arrange(p,p1, respect=TRUE) # one is square, the other thinner
+
+#p = qplot(1, 1)
+#grid.arrange(p, p, respect=TRUE) # both viewports are square
+#grid.arrange(p, p, respect=TRUE, heights=c(1,2)) # relative heights
+
+	if(fs=="lustre") {
+		print("Lustre limit")
+		p = p + ylim(0, 20000)
+	}
+	#if(fs=="fuse") {
+	#p = p + theme(legend.position="bottom")
+	#}
+	if(print_legend=="yes") {
+		p = p + theme(legend.position="bottom")
+	}
+	else {
+		p = p + theme(legend.position="none")
+	}
+
+	if ('log' == scale) {
+		p = p + scale_y_log10()
+		p = p + scale_x_log10(breaks = c(unique(data$nn)))
+		#p = p + scale_y_log10(breaks=c(100, 1000, 10000, 40000))
+	}
+	{
+		#p = p + scale_x_discrete(breaks=mixedsort(unique(dat$lab)), limits=mixedsort(unique(dat$lab)))
+	}
+
+	filename_eps_base = sprintf("%s/performance_%s_%s_%s_%s_CHUNK:%s_FILL:%s_LIM:%s_legend:%s_SCALE:%s", folder_out, app, fs, iface, type, chunked, filled, unlimited, print_legend, scale)
+	filenmae_eps=""
+
+	if(fs=="ime") {
+		filename = sprintf("%s_size:%dx%d", filename_eps_base, 6, 8)
+		filename_eps = sprintf("%s.eps", filename)
+		filename_png = sprintf("%s.png", filename)
+		filename_pdf = sprintf("%s.pdf", filename)
+		ggsave(filename_eps, width = 6, height = 6)
+		ggsave(filename_png, dpi=300)
+		system(sprintf("epstopdf %s", filename_eps))
+		system(sprintf("rm %s", filename_eps))
+		system(sprintf("pdfcrop %s %s", filename_pdf, filename_pdf))
+	}
+
+	#filename = sprintf("%s_size:%dx%d", filename_eps_base, 12, 8)
+	filename = sprintf("%s_size:%dx%d", filename_eps_base, 9, 6)
+	filename_eps = sprintf("%s.eps", filename)
+	filename_png = sprintf("%s.png", filename)
+	filename_pdf = sprintf("%s.pdf", filename)
+	#ggsave(filename_eps, width = 12, height = 6)
+	ggsave(filename_eps, dpi=300)
+	ggsave(filename_png, dpi=300)
+	system(sprintf("epstopdf %s", filename_eps))
+	system(sprintf("rm %s", filename_eps))
+	system(sprintf("pdfcrop %s %s", filename_pdf, filename_pdf))
+} # for legend
+
+}}}}}}}
+} # scale
diff --git a/eval_readwrite.R b/eval_readwrite.R
new file mode 100755
index 000000000..50a4a45ac
--- /dev/null
+++ b/eval_readwrite.R
@@ -0,0 +1,75 @@
+#!/usr/bin/env Rscript
+
+library(sqldf)
+library(plyr)
+library(plot3D)
+library(ggplot2)
+library(gtools)
+
+
+args = commandArgs(trailingOnly=TRUE)
+#print(args)
+#if (2 != length(args)) {
+	#print("Requires 2 parameters)")
+	#q()
+#}
+
+#file_db = args[1]
+#folder_out = args[2]
+
+file_db_random = 'results_random.db'
+file_db_sequential = 'results_sequential.db'
+folder_out = 'plot_read_write'
+
+#file_db = file_db_sequential
+file_db = file_db_random
+
+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')
+dbdata[,"blocksize"] = dbdata$x * dbdata$y * dbdata$z * 4
+
+dbdata$nn_lab <- sprintf(fmt="NN=%d; PPN=1-10", dbdata$nn)
+dbdata$nn_lab_f <- factor(dbdata$nn_lab, sprintf(fmt="NN=%d; PPN=1-10", sort(unique(dbdata$nn))))
+print(dbdata$nn_lab)
+#names(nn_lab) <- unique(dbdata$nn)
+
+summary(dbdata)
+
+fss = unique(dbdata$fs)
+for (fs in fss) {
+data = dbdata[fs == dbdata$fs, ]
+
+ggplot(data=data, aes(x=read, y=write, colour=as.factor(blocksize/1024), group=blocksize), ymin=0, xmin=0) +
+	#ggtitle("Write") +
+	facet_grid(. ~ nn_lab_f) +
+	facet_wrap(~ nn_lab_f) +
+	xlab("Read Performance in MiB/s") +
+	ylab("Write Performance in MiB/s") +
+	#theme(axis.text.x=element_text(angle=90, hjust=0.95, vjust=0.5)) +
+	theme(legend.position="bottom") +
+	theme(aspect.ratio=1) +
+	scale_y_log10() +
+	scale_x_log10() +
+	scale_color_manual(name="Blocksize in KiB: ", values=c('#999999','#E69F00', '#56B4E9', '#000000','#999999','#E69F00', '#56B4E9', '#000000','#999999','#E69F00', '#56B4E9', '#000000' )) +
+	geom_point()
+
+	filename = sprintf("%s/performance_overview_rnd_%s", folder_out, fs)
+	filename_eps = sprintf("%s.eps", filename)
+	filename_png = sprintf("%s.png", filename)
+	filename_pdf = sprintf("%s.pdf", filename)
+	#ggsave(filename_eps, width = 12, height = 6)
+	#ggsave(filename_eps, dpi=1000)
+	ggsave(filename_eps, width = 16)
+	ggsave(filename_png, dpi=300)
+	system(sprintf("epstopdf %s", filename_eps))
+	system(sprintf("rm %s", filename_eps))
+	system(sprintf("pdfcrop %s %s", filename_pdf, filename_pdf))
+}