saiph: add figures for blocking experiment

garlic/fig/saiph/blocking.R

@@ -0,0 +1,100 @@
+library(ggplot2)
+library(dplyr)
+library(scales)
+library(jsonlite)
+
+args=commandArgs(trailingOnly=TRUE)
+
+# Read the timetable from args[1]
+input_file = "input.json"
+if (length(args)>0) { input_file = args[1] }
+
+# Load the dataset in NDJSON format
+dataset = jsonlite::stream_in(file(input_file)) %>%
+	jsonlite::flatten()
+
+
+# We only need the nblocks and time
+df = select(dataset, config.nby, time) %>%
+	rename(nby=config.nby)
+
+df$nby = as.factor(df$nby)
+
+# Normalize the time by the median
+D=group_by(df, nby) %>%
+	mutate(tnorm = time / median(time) - 1) %>%
+	mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
+
+D$bad = as.factor(D$bad)
+
+
+print(D)
+
+ppi=300
+h=5
+w=5
+
+png("box.png", width=w*ppi, height=h*ppi, res=ppi)
+#
+#
+#
+# Create the plot with the normalized time vs nblocks
+p = ggplot(data=D, aes(x=nby, y=tnorm, color=bad)) +
+
+	# Labels
+	labs(x="nb{y-z}", y="Normalized time",
+              title=sprintf("Saiph-Heat3D normalized time"), 
+              subtitle=input_file) +
+
+	# Center the title
+	#theme(plot.title = element_text(hjust = 0.5)) +
+
+	# Black and white mode (useful for printing)
+	#theme_bw() +
+
+	# Add the maximum allowed error lines
+	geom_hline(yintercept=c(-0.01, 0.01),
+		linetype="dashed", color="gray") +
+
+	# Draw boxplots
+	geom_boxplot() +
+	scale_color_manual(values=c("black", "brown")) +
+
+	#scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
+
+	theme_bw() +
+
+	theme(plot.subtitle=element_text(size=8)) +
+        theme(legend.position = "none")
+	#theme(legend.position = c(0.85, 0.85))
+
+
+
+
+# Render the plot
+print(p)
+
+## Save the png image
+dev.off()
+#
+png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
+#
+## Create the plot with the normalized time vs nblocks
+p = ggplot(D, aes(x=nby, y=time)) +
+
+	labs(x="nb{y-z}", y="Time (s)",
+              title=sprintf("Saiph-Heat3D blocking-granularity"), 
+              subtitle=input_file) +
+	theme_bw() +
+	theme(plot.subtitle=element_text(size=8)) +
+	theme(legend.position = c(0.5, 0.88)) +
+
+	geom_point(shape=21, size=3) +
+	#scale_x_continuous(trans=log2_trans()) +
+	scale_y_continuous(trans=log2_trans())
+
+# Render the plot
+print(p)
+
+# Save the png image
+dev.off()

garlic/fig/saiph/blockingY_blocking_Z.R

@@ -0,0 +1,77 @@
+library(ggplot2)
+library(dplyr)
+library(scales)
+library(jsonlite)
+
+args=commandArgs(trailingOnly=TRUE)
+
+# Read the timetable from args[1]
+input_file = "input1.json"
+if (length(args)>0) { input_file = args[1] }
+
+input_file2 = "input2.json"
+if (length(args)>0) { input_file2 = args[1] }
+
+# Load the dataset in NDJSON format
+dataset = jsonlite::stream_in(file(input_file)) %>%
+	jsonlite::flatten()
+
+dataset2 = jsonlite::stream_in(file(input_file2)) %>%
+	jsonlite::flatten()
+
+
+# We only need the nblocks and time
+df = select(dataset, config.nby, time) %>%
+     rename(nby=config.nby)
+
+df$nby = as.factor(df$nby)
+
+df2 = select(dataset2, config.nbz, time) %>%
+      rename(nbz=config.nbz)
+
+df2$nbz = as.factor(df2$nbz)
+
+
+# Normalize the time by the median
+D=group_by(df, nby) %>%
+	mutate(tnorm = time / median(time) - 1) %>%
+	mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
+
+D$bad = as.factor(D$bad)
+
+
+print(D)
+
+D2=group_by(df2, nbz) %>%
+	mutate(tnorm = time / median(time) - 1) %>%
+	mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
+
+D2$bad = as.factor(D2$bad)
+
+print(D)
+print(D2)
+
+png("scatter-blockY8Z_yZ8.png", width=w*ppi, height=h*ppi, res=ppi)
+#
+## Create the plot with the normalized time vs nblocks
+p = ggplot() +
+	geom_point(data=D, aes(x=nby, y=time, colour="nby blocks - nbz = 8"), shape=1, size=3) +
+	geom_point(data=D2, aes(x=nbz, y=time, colour="nby = 8 - nbz blocks"), shape=1, size=3) +
+
+	labs(x="nb", y="Time (s)",
+              title=sprintf("Saiph-Heat3D blockingY/Z"), 
+              subtitle=input_file) +
+	theme_bw() +
+	theme(plot.subtitle=element_text(size=8)) +
+	theme(legend.position = "right") +
+
+	geom_point(shape=21, size=3) +
+	scale_colour_discrete("Blocked directions")
+	#+ scale_x_continuous(trans=log2_trans())
+	#+ scale_y_continuous(trans=log2_trans())
+
+# Render the plot
+print(p)
+
+# Save the png image
+dev.off()

garlic/fig/saiph/blocking_Y.R

@@ -0,0 +1,100 @@
+library(ggplot2)
+library(dplyr)
+library(scales)
+library(jsonlite)
+
+args=commandArgs(trailingOnly=TRUE)
+
+# Read the timetable from args[1]
+input_file = "input.json"
+if (length(args)>0) { input_file = args[1] }
+
+# Load the dataset in NDJSON format
+dataset = jsonlite::stream_in(file(input_file)) %>%
+	jsonlite::flatten()
+
+
+# We only need the nblocks and time
+df = select(dataset, config.nby, time) %>%
+	rename(nby=config.nby)
+
+df$nby = as.factor(df$nby)
+
+# Normalize the time by the median
+D=group_by(df, nby) %>%
+	mutate(tnorm = time / median(time) - 1) %>%
+	mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
+
+D$bad = as.factor(D$bad)
+
+
+print(D)
+
+ppi=300
+h=5
+w=5
+
+png("box.png", width=w*ppi, height=h*ppi, res=ppi)
+#
+#
+#
+# Create the plot with the normalized time vs nblocks
+p = ggplot(data=D, aes(x=nby, y=tnorm, color=bad)) +
+
+	# Labels
+	labs(x="nby", y="Normalized time",
+              title=sprintf("Saiph-Heat3D normalized time"), 
+              subtitle=input_file) +
+
+	# Center the title
+	#theme(plot.title = element_text(hjust = 0.5)) +
+
+	# Black and white mode (useful for printing)
+	#theme_bw() +
+
+	# Add the maximum allowed error lines
+	geom_hline(yintercept=c(-0.01, 0.01),
+		linetype="dashed", color="gray") +
+
+	# Draw boxplots
+	geom_boxplot() +
+	scale_color_manual(values=c("black", "brown")) +
+
+	#scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
+
+	theme_bw() +
+
+	theme(plot.subtitle=element_text(size=8)) +
+        theme(legend.position = "none")
+	#theme(legend.position = c(0.85, 0.85))
+
+
+
+
+# Render the plot
+print(p)
+
+## Save the png image
+dev.off()
+#
+png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
+#
+## Create the plot with the normalized time vs nblocks
+p = ggplot(D, aes(x=nby, y=time)) +
+
+	labs(x="nby", y="Time (s)",
+              title=sprintf("Saiph-Heat3D blockingY"), 
+              subtitle=input_file) +
+	theme_bw() +
+	theme(plot.subtitle=element_text(size=8)) +
+	theme(legend.position = c(0.5, 0.88)) +
+
+	geom_point(shape=21, size=3)
+	#+ scale_x_continuous(trans=log2_trans())
+	#+ scale_y_continuous(trans=log2_trans())
+
+# Render the plot
+print(p)
+
+# Save the png image
+dev.off()

garlic/fig/saiph/blocking_YZ.R

@@ -0,0 +1,100 @@
+library(ggplot2)
+library(dplyr)
+library(scales)
+library(jsonlite)
+
+args=commandArgs(trailingOnly=TRUE)
+
+# Read the timetable from args[1]
+input_file = "input.json"
+if (length(args)>0) { input_file = args[1] }
+
+# Load the dataset in NDJSON format
+dataset = jsonlite::stream_in(file(input_file)) %>%
+	jsonlite::flatten()
+
+
+# We only need the nblocks and time
+df = select(dataset, config.nbz, time) %>%
+	rename(nbz=config.nbz)
+
+df$nbz = as.factor(df$nbz)
+
+# Normalize the time by the median
+D=group_by(df, nbz) %>%
+	mutate(tnorm = time / median(time) - 1) %>%
+	mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
+
+D$bad = as.factor(D$bad)
+
+
+print(D)
+
+ppi=300
+h=5
+w=5
+
+png("box.png", width=w*ppi, height=h*ppi, res=ppi)
+#
+#
+#
+# Create the plot with the normalized time vs nblocks
+p = ggplot(data=D, aes(x=nbz, y=tnorm, color=bad)) +
+
+	# Labels
+	labs(x="nbz", y="Normalized time",
+              title=sprintf("Saiph-Heat3D normalized time - nby = 8"), 
+              subtitle=input_file) +
+
+	# Center the title
+	#theme(plot.title = element_text(hjust = 0.5)) +
+
+	# Black and white mode (useful for printing)
+	#theme_bw() +
+
+	# Add the maximum allowed error lines
+	geom_hline(yintercept=c(-0.01, 0.01),
+		linetype="dashed", color="gray") +
+
+	# Draw boxplots
+	geom_boxplot() +
+	scale_color_manual(values=c("black", "brown")) +
+
+	#scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
+
+	theme_bw() +
+
+	theme(plot.subtitle=element_text(size=8)) +
+        theme(legend.position = "none")
+	#theme(legend.position = c(0.85, 0.85))
+
+
+
+
+# Render the plot
+print(p)
+
+## Save the png image
+dev.off()
+#
+png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
+#
+## Create the plot with the normalized time vs nblocks
+p = ggplot(D, aes(x=nbz, y=time)) +
+
+	labs(x="nbz", y="Time (s)",
+               title=sprintf("Saiph-Heat3D blockingZ - nby = 8"), 
+               subtitle=input_file) +
+	theme_bw() +
+	theme(plot.subtitle=element_text(size=8)) +
+	theme(legend.position = c(0.5, 0.88)) +
+
+	geom_point(shape=21, size=3)
+	#+ scale_x_continuous(trans=log2_trans())
+	#+ scale_y_continuous(trans=log2_trans())
+
+# Render the plot
+print(p)
+
+# Save the png image
+dev.off()

garlic/fig/saiph/blocking_Z.R

@@ -0,0 +1,100 @@
+library(ggplot2)
+library(dplyr)
+library(scales)
+library(jsonlite)
+
+args=commandArgs(trailingOnly=TRUE)
+
+# Read the timetable from args[1]
+input_file = "input.json"
+if (length(args)>0) { input_file = args[1] }
+
+# Load the dataset in NDJSON format
+dataset = jsonlite::stream_in(file(input_file)) %>%
+	jsonlite::flatten()
+
+
+# We only need the nblocks and time
+df = select(dataset, config.nbz, time) %>%
+	rename(nbz=config.nbz)
+
+df$nbz = as.factor(df$nbz)
+
+# Normalize the time by the median
+D=group_by(df, nbz) %>%
+	mutate(tnorm = time / median(time) - 1) %>%
+	mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
+
+D$bad = as.factor(D$bad)
+
+
+print(D)
+
+ppi=300
+h=5
+w=5
+
+png("box.png", width=w*ppi, height=h*ppi, res=ppi)
+#
+#
+#
+# Create the plot with the normalized time vs nblocks
+p = ggplot(data=D, aes(x=nbz, y=tnorm, color=bad)) +
+
+	# Labels
+	labs(x="nbz", y="Normalized time",
+              title=sprintf("Saiph-Heat3D normalized time"), 
+              subtitle=input_file) +
+
+	# Center the title
+	#theme(plot.title = element_text(hjust = 0.5)) +
+
+	# Black and white mode (useful for printing)
+	#theme_bw() +
+
+	# Add the maximum allowed error lines
+	geom_hline(yintercept=c(-0.01, 0.01),
+		linetype="dashed", color="gray") +
+
+	# Draw boxplots
+	geom_boxplot() +
+	scale_color_manual(values=c("black", "brown")) +
+
+	#scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
+
+	theme_bw() +
+
+	theme(plot.subtitle=element_text(size=8)) +
+        theme(legend.position = "none")
+	#theme(legend.position = c(0.85, 0.85))
+
+
+
+
+# Render the plot
+print(p)
+
+## Save the png image
+dev.off()
+#
+png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
+#
+## Create the plot with the normalized time vs nblocks
+p = ggplot(D, aes(x=nbz, y=time)) +
+
+	labs(x="nbz", y="Time (s)",
+               title=sprintf("Saiph-Heat3D blockingZ"), 
+               subtitle=input_file) +
+	theme_bw() +
+	theme(plot.subtitle=element_text(size=8)) +
+	theme(legend.position = c(0.5, 0.88)) +
+
+	geom_point(shape=21, size=3)
+	#+ scale_x_continuous(trans=log2_trans())
+	#+ scale_y_continuous(trans=log2_trans())
+
+# Render the plot
+print(p)
+
+# Save the png image
+dev.off()

garlic/fig/saiph/blocking_ZY.R

@@ -0,0 +1,100 @@
+library(ggplot2)
+library(dplyr)
+library(scales)
+library(jsonlite)
+
+args=commandArgs(trailingOnly=TRUE)
+
+# Read the timetable from args[1]
+input_file = "input.json"
+if (length(args)>0) { input_file = args[1] }
+
+# Load the dataset in NDJSON format
+dataset = jsonlite::stream_in(file(input_file)) %>%
+	jsonlite::flatten()
+
+
+# We only need the nblocks and time
+df = select(dataset, config.nby, time) %>%
+	rename(nby=config.nby)
+
+df$nby = as.factor(df$nby)
+
+# Normalize the time by the median
+D=group_by(df, nby) %>%
+	mutate(tnorm = time / median(time) - 1) %>%
+	mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
+
+D$bad = as.factor(D$bad)
+
+
+print(D)
+
+ppi=300
+h=5
+w=5
+
+png("box.png", width=w*ppi, height=h*ppi, res=ppi)
+#
+#
+#
+# Create the plot with the normalized time vs nblocks
+p = ggplot(data=D, aes(x=nby, y=tnorm, color=bad)) +
+
+	# Labels
+	labs(x="nby", y="Normalized time",
+              title=sprintf("Saiph-Heat3D normalized time - nbz = 8"), 
+              subtitle=input_file) +
+
+	# Center the title
+	#theme(plot.title = element_text(hjust = 0.5)) +
+
+	# Black and white mode (useful for printing)
+	#theme_bw() +
+
+	# Add the maximum allowed error lines
+	geom_hline(yintercept=c(-0.01, 0.01),
+		linetype="dashed", color="gray") +
+
+	# Draw boxplots
+	geom_boxplot() +
+	scale_color_manual(values=c("black", "brown")) +
+
+	#scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
+
+	theme_bw() +
+
+	theme(plot.subtitle=element_text(size=8)) +
+        theme(legend.position = "none")
+	#theme(legend.position = c(0.85, 0.85))
+
+
+
+
+# Render the plot
+print(p)
+
+## Save the png image
+dev.off()
+#
+png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
+#
+## Create the plot with the normalized time vs nblocks
+p = ggplot(D, aes(x=nby, y=time)) +
+
+	labs(x="nby", y="Time (s)",
+               title=sprintf("Saiph-Heat3D blockingY - nbz = 8"), 
+               subtitle=input_file) +
+	theme_bw() +
+	theme(plot.subtitle=element_text(size=8)) +
+	theme(legend.position = c(0.5, 0.88)) +
+
+	geom_point(shape=21, size=3)
+	#+ scale_x_continuous(trans=log2_trans())
+	#+ scale_y_continuous(trans=log2_trans())
+
+# Render the plot
+print(p)
+
+# Save the png image
+dev.off()

garlic/fig/saiph/granBlock.R

@@ -0,0 +1,67 @@
+library(ggplot2)
+library(dplyr)
+library(scales)
+library(jsonlite)
+
+args=commandArgs(trailingOnly=TRUE)
+
+# Read the timetable from args[1]
+input_file1 = "input1.json"
+if (length(args)>0) { input_file1 = args[1] }
+
+input_file2 = "input2.json"
+if (length(args)>1) { input_file2 = args[2] }
+
+# Load the dataset in NDJSON format
+dataset1 = jsonlite::stream_in(file(input_file1)) %>%
+	 jsonlite::flatten()
+dataset2 = jsonlite::stream_in(file(input_file2)) %>%
+	 jsonlite::flatten()
+
+# We only need the nblocks and time
+df1 = select(dataset1, config.nbx, time) %>%
+	rename(nb1=config.nbx)
+
+df2 = select(dataset2, config.nby, time) %>%
+	rename(nb2=config.nby)
+
+df1$nb1 = as.factor(df1$nb1)
+df2$nb2 = as.factor(df2$nb2)
+
+# Normalize the time by the median
+D1=group_by(df1, nb1)
+D2=group_by(df2, nb2) 
+
+print(D1)
+print(D2)
+
+ppi=300
+h=5
+w=7
+
+png("scatter_granularity_and_blocking.png", width=w*ppi, height=h*ppi, res=ppi)
+#
+## Create the plot with the normalized time vs nblocks
+p = ggplot() +
+    geom_point(data=D1, aes(x=nb1, y=time, colour = 'nbx-nby-nbz'), shape=1, size=4) +
+    geom_point(data=D2, aes(x=nb2, y=time, colour = 'nby-nbz'), shape=1, size=4) +
+
+	labs(x="nb", y="Time (s)",
+              title=sprintf("Saiph-Heat3D granularity & blocking"), 
+              subtitle=input_file1) +
+	theme_bw() +
+	theme(plot.subtitle=element_text(size=8)) +
+	#theme(legend.position = c(0.5, 0.88)) +
+	theme(legend.position = "right") +
+
+	geom_point(shape=21, size=3) +
+	#scale_x_continuous(trans=log2_trans()) +
+	scale_y_continuous(trans=log2_trans()) +
+	scale_colour_discrete("Blocked directions")
+
+
+# Render the plot
+print(p)
+
+# Save the png image
+dev.off()

garlic/fig/saiph/strongScaling.R

@@ -0,0 +1,100 @@
+library(ggplot2)
+library(dplyr)
+library(scales)
+library(jsonlite)
+
+args=commandArgs(trailingOnly=TRUE)
+
+# Read the timetable from args[1]
+input_file = "input.json"
+if (length(args)>0) { input_file = args[1] }
+
+# Load the dataset in NDJSON format
+dataset = jsonlite::stream_in(file(input_file)) %>%
+	jsonlite::flatten()
+
+
+# We only need the nblocks and time
+df = select(dataset, config.nodes, time) %>%
+	rename(nodes=config.nodes)
+
+df$nodes = as.factor(df$nodes)
+
+# Normalize the time by the median
+D=group_by(df, nodes) %>%
+	mutate(tnorm = time / median(time) - 1) %>%
+	mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
+
+D$bad = as.factor(D$bad)
+
+
+print(D)
+
+ppi=300
+h=5
+w=5
+
+png("box.png", width=w*ppi, height=h*ppi, res=ppi)
+#
+#
+#
+# Create the plot with the normalized time vs nblocks
+p = ggplot(data=D, aes(x=nodes, y=tnorm, color=bad)) +
+
+	# Labels
+	labs(x="#nodes", y="Normalized time",
+             title=sprintf("Saiph-Heat3D Strong-Scaling\nLocal blocking nb{y-z} = 4"), 
+              subtitle=input_file) +
+
+	# Center the title
+	#theme(plot.title = element_text(hjust = 0.5)) +
+
+	# Black and white mode (useful for printing)
+	#theme_bw() +
+
+	# Add the maximum allowed error lines
+	geom_hline(yintercept=c(-0.01, 0.01),
+		linetype="dashed", color="gray") +
+
+	# Draw boxplots
+	geom_boxplot() +
+	scale_color_manual(values=c("black", "brown")) +
+
+	#scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
+
+	theme_bw() +
+
+	theme(plot.subtitle=element_text(size=8)) +
+        theme(legend.position = "none")
+	#theme(legend.position = c(0.85, 0.85))
+
+
+
+
+# Render the plot
+print(p)
+
+## Save the png image
+dev.off()
+#
+png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
+#
+## Create the plot with the normalized time vs nblocks
+p = ggplot(D, aes(x=nodes, y=time)) +
+
+	labs(x="#nodes", y="Time (s)",
+              title=sprintf("Saiph-Heat3D Strong-Scaling\nLocal blocking nb{y-z} = 4"), 
+              subtitle=input_file) +
+	theme_bw() +
+	theme(plot.subtitle=element_text(size=8)) +
+	theme(legend.position = c(0.5, 0.88)) +
+
+	geom_point(shape=21, size=3) +
+	#scale_x_continuous(trans=log2_trans()) +
+	scale_y_continuous(trans=log2_trans())
+
+# Render the plot
+print(p)
+
+# Save the png image
+dev.off()