Add R script to print bsize plot.

Add information in the wrap for each different plot (add bsize)

Utils/benchmark/common.R

@@ -31,22 +31,22 @@ get_data_subset <- function(f, n, h, p)
     return (d)
 }
 
-# OMP Compiler/runtime breaks, colors...
 get_breaks_runtime <- function()
 {
-    return (c('implicit', 'explicit', 'implicit limited'))
+    return (c('implicit', 'explicit', 'implicit limited', 'simple-mpi'))
 }
 
 get_labels_runtime <- function()
 {
-    return (c('Implicit', 'Explicit', 'Implicit Limited'))
+    return (c('Implicit', 'Explicit', 'Implicit Limited', 'Simple MPI'))
 }
 
 get_colors_runtime <- function()
 {
     return (c('implicit'  = "#266d83",
               'explicit'   = "#e20025",
-			  'implicit limited' = "#bd02b6"))
+			  'implicit limited' = "#bd02b6",
+			  'simple-mpi' = "#9aff4f"))
 }
 
 # Scheme breaks, colors ...
@@ -109,3 +109,11 @@ height_labeller <- function(value)
 {
     return (paste("h =", value))
 }
+group_size_labeller <- function(value)
+{
+    return (paste("bs =", value))
+}
+nnode_labeller <- function(value)
+{
+    return (paste("node =", value))
+}

Utils/benchmark/gen_comm.R

@@ -7,9 +7,10 @@ gen_comm_plot <- function(db, d_breaks, model_wanted)
 
     g <- ggplot(data=db,aes_string(x="nnode", y="communication_vol", color="algo"))
     g <- g + geom_line()
-    g <- g + facet_wrap(npart ~ height, scales="free",
+    g <- g + facet_wrap(npart ~ height ~ bsize, scales="free",
                         labeller = labeller(npart = as_labeller(npart_labeller),
                                             height = as_labeller(height_labeller),
+											bsize = as_labeller(group_size_labeller),
                                             .default=label_both,
                                             .multi_line=FALSE))
 

Utils/benchmark/gen_efficiencies_taskdep.R

@@ -8,9 +8,10 @@ gen_efficiencies_plot <- function(output, data)
                 color="event", group="event"))
     g <- g + geom_line()
     g <- g + geom_point(aes_string(color="event"), size=2)
-    g <- g + facet_wrap(npart ~ height, scales="free",
+    g <- g + facet_wrap(npart ~ height ~ bsize, scales="free",
                         labeller = labeller(npart = as_labeller(npart_labeller),
                                             height = as_labeller(height_labeller),
+											bsize = as_labeller(group_size_labeller),
                                             .default=label_both,
                                             .multi_line=FALSE))
     # Set colors.
@@ -23,7 +24,7 @@ gen_efficiencies_plot <- function(output, data)
     g <- g + xlab("Number of node")
     g <- g + ylab("Efficiency")
 
-    g <- g + scale_x_continuous(breaks=c(1, 2, 3, 4, 6, 9, 12, 16, 20, 24))
+    #g <- g + scale_x_continuous(breaks=c(1, 2, 3, 4, 6, 9, 12, 16, 20, 24))
 
     # Set y-axis range
     g <- g + ylim(c(0.0, 1.10))
@@ -40,9 +41,12 @@ compute_efficiency <- function(data, n)
 
 
     # Compute task efficiency
-    #tt_1 <- subset(sdata, event == "task_time" & nthreads == 1)
 	tt_1 <- subset(sdata, event == "task_time" & nnode == 1)
     et <- subset(sdata, event == "task_time")
+	if(nrow(tt_1))
+	{
+		print("There is no one node reference for this algorithm")
+	}
     et$efficiency <- tt_1$duration / et$duration
 
     # Compute scheduling efficiency

Utils/benchmark/gen_gantt.R

@@ -107,10 +107,10 @@ read_trace <- function(file, nnode, nthreads, start_profiling, stop_profiling)
 	#Return
 	df
 } 
-gen_simple_gantt_plot <- function(data, model, algo, nnode, npart)
+gen_simple_gantt_plot <- function(data, model, algo, nnode, npart, bsize, height)
 {
-	output <- paste(get_output_directory(), "/", model, "-", algo, "-", nnode, "N-", npart/1000000, "M-simple-gantt.pdf", sep="")
-	title <- paste(model, " ", algo, " ", nnode, "N ", npart/1000000, "M", sep="")
+	output <- paste(get_output_directory(), "/", model, "-", algo, "-", nnode, "N-", npart/1000000, "M-h", height, "-bs", bsize, "-simple-gantt.pdf", sep="")
+	title <- paste(model, " ", algo, " ", nnode, "N ", npart/1000000, "M", " h = ", height, " bs = ", bsize, sep="")
 	breaks <- c('Sleeping', 'Far Field', 'Near Field')
 	labels <- c('Sleeping', 'Far Field', 'Near Field')
 	colors <- c(
@@ -128,10 +128,10 @@ gen_simple_gantt_plot <- function(data, model, algo, nnode, npart)
 	g <- g + scale_y_discrete(breaks=NULL)
     ggsave(output, g, width=29.7, height=21, units=c("cm"), device=cairo_pdf)
 }
-gen_gantt_plot <- function(data, model, algo, nnode, npart)
+gen_gantt_plot <- function(data, model, algo, nnode, npart, bsize, height)
 {
-	output <- paste(get_output_directory(), "/", model, "-", algo, "-", nnode, "N-", npart/1000000, "M-gantt.pdf", sep="")
-	title <- paste(model, " ", algo, " ", nnode, "N ", npart/1000000, "M", sep="")
+	output <- paste(get_output_directory(), "/", model, "-", algo, "-", nnode, "N-", npart/1000000, "M-h", height, "-bs", bsize, "-gantt.pdf", sep="")
+	title <- paste(model, " ", algo, " ", nnode, "N ", npart/1000000, "M", " h = ", height, " bs = ", bsize, sep="")
 	g <- ggplot(data,aes(x=Start,xend=End, y=factor(ResourceId), yend=factor(ResourceId),color=Value)) 
 	g <- g + theme_bw()
 	g <- g + geom_segment(size=8)
@@ -167,30 +167,38 @@ gen_gantt <- function(dbfile)
 	all_model <- unique(df$model)
 	all_nnode <- unique(df$nnode)
 	all_npart <- unique(df$npart)
-	for (mod in 1:length(all_model))
+	all_bsize <- unique(df$bsize)
+	all_height <- unique(df$height)
+	for (bsi in 1:length(all_bsize))
 	{
-		for (alg in 1:length(all_algorithm))
+		for (hei in 1:length(all_height))
 		{
-			for (nno in 1:length(all_nnode))
+			for (mod in 1:length(all_model))
 			{
-				for (npa in 1:length(all_npart))
+				for (alg in 1:length(all_algorithm))
 				{
-					#Get the corresponding line
-					tmp_df <- subset(df, model == all_model[mod] & algo == all_algorithm[alg] & nnode == all_nnode[nno] & npart == all_npart[npa])
-					if(nrow(tmp_df) > 0)
+					for (nno in 1:length(all_nnode))
 					{
-						look <- paste("Grab data ", all_model[mod],all_algorithm[alg], all_nnode[nno], "N", all_npart[npa], sep="-")
-						#Print something so I know were is the script
-						print(look) 
-						data <- gen_gantt_grab_data(tmp_df)
-						gen_gantt_plot(data, all_model[mod], all_algorithm[alg], all_nnode[nno], all_npart[npa])
-						gen_simple_gantt_plot(data, all_model[mod], all_algorithm[alg], all_nnode[nno], all_npart[npa])
-						#rm(data)
-						#gc()
+						for (npa in 1:length(all_npart))
+						{
+							#Get the corresponding line
+							tmp_df <- subset(df, model == all_model[mod] & algo == all_algorithm[alg] & nnode == all_nnode[nno] & npart == all_npart[npa] & bsize == all_bsize[bsi] & height == all_height[hei])
+							if(nrow(tmp_df) > 0)
+							{
+								look <- paste("Grab data ", all_model[mod],all_algorithm[alg], all_nnode[nno], "N", all_npart[npa], all_height[hei], all_bsize[bsi], sep="-")
+								#Print something so I know were is the script
+								print(look) 
+								data <- gen_gantt_grab_data(tmp_df)
+								gen_gantt_plot(data, all_model[mod], all_algorithm[alg], all_nnode[nno], all_npart[npa], all_bsize[bsi], all_height[hei])
+								gen_simple_gantt_plot(data, all_model[mod], all_algorithm[alg], all_nnode[nno], all_npart[npa], all_bsize[bsi], all_height[hei])
+								rm(data)
+								gc()
+							}
+						}
 					}
+
 				}
 			}
-
 		}
 	}
 }

Utils/benchmark/gen_group_size_plots.R

+library(plyr)
+library(ggplot2)
+library(scales)
+
+gen_group_size_plot <- function(d, model_wanted)
+{
+	d <- subset(d, model == model_wanted)
+	d$global_time <- d$global_time/1000
+	g <- ggplot(data=d,aes_string(x="bsize", y="global_time", color="algo"))
+    g <- g + geom_line()
+	g <- g + facet_wrap(npart ~ height ~ nnode, scales="free",
+						labeller = labeller(npart = as_labeller(npart_labeller),
+											height = as_labeller(height_labeller),
+											nnode = as_labeller(nnode_labeller),
+											.default=label_both,
+											.multi_line=FALSE))
+
+    # Set our own colors, linetypes and point shapes.
+	g <- g + scale_color_manual(name="Algorithm",
+								breaks=get_breaks_runtime(),
+								labels=get_labels_runtime(),
+								values=get_colors_runtime())
+
+    # Set X/Y labels.
+	g <- g + xlab("bsize")
+	g <- g + ylab("Time (s)")
+
+    # Save generated plot.
+	output <- paste(get_output_directory(), "/", model_wanted, "-bsize.pdf", sep="")
+	ggsave(output, g, width=29.7, height=21, units=c("cm"), device=cairo_pdf)
+}
+
+gen_group_size <- function(dbfile)
+{
+    data <- get_data_subset(dbfile, 0L, 0L, "False")
+	data <- subset(data, algo != get_one_node_reference_algorithm())
+
+	all_model <- unique(data$model)
+	for (i in 1:length(all_model))
+	{
+		gen_group_size_plot(data, all_model[i])
+	}
+}

Utils/benchmark/gen_normalized_time_plots.R

@@ -32,9 +32,10 @@ gen_normalized_time_plot <- function(db, d_breaks, model_wanted)
 
     g <- ggplot(data=db,aes_string(x="nnode", y="efficiency", color="algo"))
     g <- g + geom_line()
-    g <- g + facet_wrap(npart ~ height, scales="free",
+    g <- g + facet_wrap(npart ~ height ~ bsize, scales="free",
                         labeller = labeller(npart = as_labeller(npart_labeller),
                                             height = as_labeller(height_labeller),
+											bsize = as_labeller(group_size_labeller),
                                             .default=label_both,
                                             .multi_line=FALSE))
 

Utils/benchmark/gen_parallel_efficiency_plots.R

@@ -31,9 +31,10 @@ gen_pareff_plot <- function(db, d_breaks, model_wanted)
 
     g <- ggplot(data=db,aes_string(x="nnode", y="efficiency", color="algo"))
     g <- g + geom_line()
-    g <- g + facet_wrap(npart ~ height, scales="free",
+    g <- g + facet_wrap(npart ~ height ~ bsize, scales="free",
                         labeller = labeller(npart = as_labeller(npart_labeller),
                                             height = as_labeller(height_labeller),
+											bsize = as_labeller(group_size_labeller),
                                             .default=label_both,
                                             .multi_line=FALSE))
 

Utils/benchmark/gen_speed_plots.R

@@ -5,11 +5,13 @@ library(scales)
 gen_speed_plot <- function(d, model_wanted)
 {
 	d <- subset(d, model == model_wanted)
+	d$global_time <- d$global_time/1000
 	g <- ggplot(data=d,aes_string(x="nnode", y="global_time", color="algo"))
     g <- g + geom_line()
-	g <- g + facet_wrap(npart ~ height, scales="free",
+	g <- g + facet_wrap(npart ~ height ~ bsize, scales="free",
 						labeller = labeller(npart = as_labeller(npart_labeller),
 											height = as_labeller(height_labeller),
+											bsize = as_labeller(group_size_labeller),
 											.default=label_both,
 											.multi_line=FALSE))
 
@@ -21,7 +23,7 @@ gen_speed_plot <- function(d, model_wanted)
 
     # Set X/Y labels.
 	g <- g + xlab("Number of nodes")
-	g <- g + ylab("Speed")
+	g <- g + ylab("Time (s)")
 
     # Save generated plot.
 	output <- paste(get_output_directory(), "/", model_wanted, "-speed.pdf", sep="")

Utils/benchmark/gen_speedup_plots.R

@@ -16,12 +16,13 @@ calc_speedup <- function(data, ref_algo)
 gen_speedup_taskdep_plot <- function(d, model_wanted)
 {
 	d <- subset(d, model == model_wanted)
-	d <- calc_speedup(d, "explicit")
+	d <- calc_speedup(d, "simple-mpi")
 	g <- ggplot(data=d,aes_string(x="nnode", y="speedup", color="algo"))
     g <- g + geom_line()
-	g <- g + facet_wrap(npart ~ height, scales="free",
+	g <- g + facet_wrap(npart ~ height ~ bsize, scales="free",
 						labeller = labeller(npart = as_labeller(npart_labeller),
 											height = as_labeller(height_labeller),
+											bsize = as_labeller(group_size_labeller),
 											.default=label_both,
 											.multi_line=FALSE))
 

Utils/benchmark/gen_times_taskdep.R

@@ -17,9 +17,10 @@ gen_times_taskdep_plot <- function(data, algo_wanted, model_wanted)
 	g <- g + geom_bar(stat="identity", position="fill")
 
 	#Pour le titre
-	g <- g + facet_wrap(npart ~ height, scales="free",
+	g <- g + facet_wrap(npart ~ height ~ bsize, scales="free",
 						labeller = labeller(npart = as_labeller(npart_labeller),
 											height = as_labeller(height_labeller),
+											bsize = as_labeller(group_size_labeller),
 											.default=label_both,
 											.multi_line=FALSE))
     # Set colors.

Utils/benchmark/generate_graph.R

@@ -7,22 +7,27 @@ source("gen_normalized_time_plots.R")
 source("gen_comm.R")
 source("gen_gantt.R")
 source("gen_speed_plots.R")
+source("gen_group_size_plots.R")
 
 ###
 # Generate display of bars with the time spent in Task, Runtime and Idle.
 ###
 print("Generate Communication volume plot")
 gen_comm("loutre.db")
+print("Generate Group size plot")
+gen_group_size("loutre.db")
 print("Generate Speed plot")
 gen_speed("loutre.db")
 print("Generate times task plot")
 gen_times_taskdep("loutre.db")
 print("Generate Gantt")
 gen_gantt("canard.db")
-print("Generate Efficiency plot")
-gen_efficiencies("loutre.db")
 print("Generate Normalized time plot")
 gen_normalized_time("loutre.db")
+print("Generate Gantt")
+gen_gantt("canard.db")
+print("Generate Efficiency plot")
+gen_efficiencies("loutre.db")
 print("Generate Parrallel efficiency plot")
 gen_pareff("loutre.db")
 print("Generate Speedup plot")