saiph: simplify and update figure scripts

garlic/fig/index.nix

@@ -42,8 +42,8 @@ in
   };
 
   saiph = with exp.saiph; {
-    granularity-saiph = stdPlot ./saiph/granularity-saiph.R [ granularity-saiph ];
+    granularity = stdPlot ./saiph/granularity.R [ granularity ];
-    scalability-saiph = stdPlot ./saiph/scalability-saiph.R [ scalability-saiph ];
+    ss = stdPlot ./saiph/ss.R [ ss ];
   };
 
   heat = with exp.heat; {

garlic/fig/saiph/granularity-saiph.R

@@ -1,155 +0,0 @@
-library(ggplot2)
-library(dplyr)
-library(scales)
-library(jsonlite)
-library(viridis)
-
-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()
-
-
-# Create a data frame selecting the desired variables from the data set
-df = select(dataset, config.nbly, config.nblz, config.nodes, time, total_time) %>%
-        rename(nbly=config.nbly, nblz=config.nblz, nnodes=config.nodes)
-
-# Declare variables as factors 
-# --> R does not allow to operate with factors: operate before casting to factors
-df$nblPerProc = as.factor(round((df$nbly * df$nblz) / 24, digits = 2))
-df$biggernbly = as.factor(df$nbly > df$nblz)
-df$nbly = as.factor(df$nbly)
-df$nblz = as.factor(df$nblz)
-df$nodes = as.factor(df$nnodes)
-
-# Create a new data frame including statistics
-D=group_by(df, nbly, nblz, nblPerProc, nodes) %>%
-        mutate(tmedian = median(time)) %>%
-        mutate(ttmedian = median(total_time)) %>%
-        mutate(tnorm = time / tmedian - 1) %>%
-        mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0))) %>%
-        mutate(tn = tmedian * nnodes) %>%
-  ungroup()
-
-D$bad = as.factor(D$bad)
-
-### Std output data frame D
-print(D)
-
-### Output figure size
-ppi=300
-h=5
-w=8
-
-####################################################################
-### Boxplot
-####################################################################
-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=nblPerProc, y=tnorm, color=bad)) +
-
-        # Labels
-        labs(x="nblPerProc", y="Normalized time",
-              title=sprintf("Saiph-Heat3D normalized time"),
-              subtitle=input_file) +
-        # 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")) +
-        theme_bw() +
-        theme(plot.subtitle=element_text(size=8)) +
-        theme(legend.position = "none")
-
-
-# Render the plot
-print(p)
-
-## Save the png image
-dev.off()
-
-####################################################################
-### XY Scatter plot - measured_time & total_time vs tasks per cpu
-####################################################################
-
-
-####################################################################
-### XY Scatter plot - time vs tasks per cpu
-####################################################################
-png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
-## Create the plot with the normalized time vs nblocks per proc
-p = ggplot(D, aes(x=nblPerProc, y=time)) +
-        labs(x="nblPerProc", y="Time (s)",
-              title=sprintf("Saiph-Heat3D 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_y_continuous(trans=log2_trans())
-
-
-# Render the plot
-print(p)
-
-## Save the png image
-dev.off()
-
-####################################################################
-### XY Scatter plot - median time vs tasks per cpu 
-####################################################################
-png("scatter2.png", width=w*ppi, height=h*ppi, res=ppi)
-## Create the plot with the normalized time vs nblocks per proc
-p = ggplot(D, aes(x=nblPerProc, y=tmedian)) +
-        labs(x="nblPerProc", y="Median Time (s)",
-              title=sprintf("Saiph-Heat3D granularity"),
-              subtitle=input_file) +
-        theme_bw() +
-        theme(plot.subtitle=element_text(size=8)) +
-        theme(legend.position = c(0.5, 0.88)) +
-        geom_point(aes(color=biggernbly), shape=21, size=3) +
-	labs(color = "nbly > nblz")
-        scale_y_continuous(trans=log2_trans())
-
-# Render the plot
-print(p)
-
-# Save the png image
-dev.off()
-
-####################################################################
-### Heatmap plot - median time vs tasks per cpu per dimension
-####################################################################
-heatmap_plot = function(df, colname, title) {
-  p = ggplot(df, aes(x=nbly, y=nblz, fill=!!ensym(colname))) +
-    geom_raster() +
-    #scale_fill_gradient(high="black", low="white") +
-    scale_fill_viridis(option="plasma") +
-    coord_fixed() +
-    theme_bw() +
-    theme(axis.text.x=element_text(angle = -45, hjust = 0)) +
-    theme(plot.subtitle=element_text(size=8)) +
-    guides(fill = guide_colorbar(barwidth=12, title.vjust=0.8)) +
-    labs(x="nbly", y="nblz",
-      title=sprintf("Heat granularity: %s", title),
-      subtitle=input_file) +
-    theme(legend.position="bottom")+
-    facet_wrap( ~ nodes)
-
-  k=1
-  ggsave(sprintf("%s.png", colname), plot=p, width=4.8*k, height=5*k, dpi=300)
-  ggsave(sprintf("%s.pdf", colname), plot=p, width=4.8*k, height=5*k, dpi=300)
-}
-
-# call heatmap function with colname and legend title
-heatmap_plot(D, "tmedian", "time")
-

garlic/fig/saiph/granularity.R

@@ -0,0 +1,94 @@
+library(ggplot2)
+library(dplyr, warn.conflicts = FALSE)
+library(scales)
+library(jsonlite)
+library(viridis, warn.conflicts = FALSE)
+library(stringr)
+
+args = commandArgs(trailingOnly=TRUE)
+
+# Set the input dataset if given in argv[1], or use "input" as default
+if (length(args)>0) { input_file = args[1] } else { input_file = "input" }
+
+df = jsonlite::stream_in(file(input_file), verbose=FALSE) %>%
+
+  jsonlite::flatten() %>%
+
+  select(unit,
+    config.nodes,
+    config.nblx,
+    config.nbly,
+    config.nblz,
+    config.gitBranch,
+    config.blocksPerCpu,
+    config.sizex,
+    time,
+    total_time) %>%
+
+  rename(nodes=config.nodes,
+    nblx=config.nblx,
+    nbly=config.nbly,
+    nblz=config.nblz,
+    gitBranch=config.gitBranch,
+    blocksPerCpu=config.blocksPerCpu,
+    sizex=config.sizex) %>%
+
+  # Remove the "garlic/" prefix from the gitBranch
+  mutate(branch = str_replace(gitBranch, "garlic/", "")) %>%
+
+  # Computations before converting to factor
+  mutate(time.nodes = time * nodes) %>%
+
+  # Convert to factors
+  mutate(unit = as.factor(unit)) %>%
+  mutate(nodes = as.factor(nodes)) %>%
+  mutate(gitBranch = as.factor(gitBranch)) %>%
+  mutate(nblx = as.factor(nblx)) %>%
+  mutate(nbly = as.factor(nbly)) %>%
+  mutate(nblz = as.factor(nblz)) %>%
+  mutate(sizex = as.factor(sizex)) %>%
+  mutate(unit = as.factor(unit)) %>%
+
+  # Compute median times
+  group_by(unit) %>%
+  mutate(median.time = median(time)) %>%
+  mutate(median.time.nodes = median(time.nodes)) %>%
+  mutate(normalized.time = time / median.time - 1) %>%
+  mutate(log.median.time = log(median.time)) %>%
+  ungroup()
+
+dpi = 300
+h = 5
+w = 8
+
+maintitle = "Saiph-Heat3D granularity"
+
+# ---------------------------------------------------------------------
+
+p = ggplot(df, aes(x=nbly, y=normalized.time, fill=sizex)) +
+  geom_boxplot() +
+  geom_hline(yintercept=c(-0.01, 0.01), linetype="dashed", color="red") +
+  theme_bw() +
+  facet_wrap(branch ~ .) +
+  labs(y="Normalized time",
+    title=sprintf("%s: normalized time", maintitle), 
+    subtitle=input_file) + 
+  theme(plot.subtitle=element_text(size=8))
+
+ggsave("normalized.time.png", plot=p, width=w, height=h, dpi=dpi)
+ggsave("normalized.time.pdf", plot=p, width=w, height=h, dpi=dpi)
+
+# ---------------------------------------------------------------------
+
+p = ggplot(df, aes(x=blocksPerCpu, y=time, color=sizex)) +
+  geom_point(shape=21, size=3) +
+  geom_line(aes(y=median.time, group=sizex)) +
+  theme_bw() +
+  scale_x_continuous(trans=log2_trans()) +
+  labs(y="Time (s)",
+    title=sprintf("%s: time", maintitle), 
+    subtitle=input_file) + 
+  theme(plot.subtitle=element_text(size=8))
+
+ggsave("time.png", plot=p, width=w, height=h, dpi=dpi)
+ggsave("time.pdf", plot=p, width=w, height=h, dpi=dpi)

garlic/fig/saiph/scalability-saiph.R

@@ -1,156 +0,0 @@
-library(ggplot2)
-library(dplyr)
-library(scales)
-library(jsonlite)
-library(viridis)
-
-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()
-
-
-# Create a data frame selecting the desired variables from the data set
-df = select(dataset, config.nbly, config.nblz, config.nodes, time, total_time) %>%
-        rename(nbly=config.nbly, nblz=config.nblz, nnodes=config.nodes)
-
-# Declare variables as factors 
-# --> R does not allow to operate with factors: operate before casting to factors
-df$nblPerProc = as.factor(round((df$nbly * df$nblz) / 24, digits = 2))
-df$biggernbly = as.factor(df$nbly > df$nblz)
-df$nbly = as.factor(df$nbly)
-df$nblz = as.factor(df$nblz)
-df$nodes = as.factor(df$nnodes)
-
-# Create a new data frame including statistics
-D=group_by(df, nbly, nblz, nblPerProc, nodes) %>%
-        mutate(tmedian = median(time)) %>%
-        mutate(ttmedian = median(total_time)) %>%
-        mutate(tnorm = time / tmedian - 1) %>%
-        mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0))) %>%
-        mutate(tn = tmedian * nnodes) %>%
-  ungroup()
-
-D$bad = as.factor(D$bad)
-
-### Std output data frame D
-print(D)
-
-### Output figure size
-ppi=300
-h=5
-w=8
-
-####################################################################
-### Boxplot
-####################################################################
-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=nblPerProc, y=tnorm, color=bad)) +
-
-        # Labels
-        labs(x="nblPerProc", y="Normalized time",
-              title=sprintf("Saiph-Heat3D normalized time"),
-              subtitle=input_file) +
-        # 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")) +
-        theme_bw() +
-        theme(plot.subtitle=element_text(size=8)) +
-        theme(legend.position = "none")
-
-
-# Render the plot
-print(p)
-
-## Save the png image
-dev.off()
-
-####################################################################
-### XY Scatter plot - measured_time & total_time vs tasks per cpu
-####################################################################
-
-
-####################################################################
-### XY Scatter plot - time vs tasks per cpu
-####################################################################
-png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
-## Create the plot with the normalized time vs nblocks per proc
-p = ggplot(D, aes(x=nblPerProc, y=time)) +
-        labs(x="nblPerProc", y="Time (s)",
-              title=sprintf("Saiph-Heat3D granularity"),
-              subtitle=input_file) +
-        theme_bw() +
-        theme(plot.subtitle=element_text(size=8)) +
-        theme(legend.position = c(0.5, 0.88)) +
-        geom_point(aes(color=nodes), shape=21, size=3) + 
-        scale_y_continuous(trans=log2_trans())
-
-
-# Render the plot
-print(p)
-
-## Save the png image
-dev.off()
-
-
-####################################################################
-### XY Scatter plot - median time vs tasks per cpu 
-####################################################################
-png("scatter2.png", width=w*ppi, height=h*ppi, res=ppi)
-## Create the plot with the normalized time vs nblocks per proc
-p = ggplot(D, aes(x=nblPerProc, y=tn)) +
-        labs(x="nblPerProc", y="Median Time (s) * nodes",
-              title=sprintf("Saiph-Heat3D granularity"),
-              subtitle=input_file) +
-        theme_bw() +
-        theme(plot.subtitle=element_text(size=8)) +
-        theme(legend.position = c(0.5, 0.88)) +
-        geom_point(aes(color=nodes), shape=21, size=3) +
-	labs(color = "nbly > nblz")
-        scale_y_continuous(trans=log2_trans())
-
-# Render the plot
-print(p)
-
-# Save the png image
-dev.off()
-
-####################################################################
-### Heatmap plot - median time vs tasks per cpu per dimension
-####################################################################
-heatmap_plot = function(df, colname, title) {
-  p = ggplot(df, aes(x=nbly, y=nblz, fill=!!ensym(colname))) +
-    geom_raster() +
-    #scale_fill_gradient(high="black", low="white") +
-    scale_fill_viridis(option="plasma") +
-    coord_fixed() +
-    theme_bw() +
-    theme(axis.text.x=element_text(angle = -45, hjust = 0)) +
-    theme(plot.subtitle=element_text(size=8)) +
-    guides(fill = guide_colorbar(barwidth=12, title.vjust=0.8)) +
-    labs(x="nbly", y="nblz",
-      title=sprintf("Heat granularity: %s", title),
-      subtitle=input_file) +
-    theme(legend.position="bottom")+
-    facet_wrap( ~ nodes)
-
-  k=1
-  ggsave(sprintf("%s.png", colname), plot=p, width=4.8*k, height=5*k, dpi=300)
-  ggsave(sprintf("%s.pdf", colname), plot=p, width=4.8*k, height=5*k, dpi=300)
-}
-
-# call heatmap function with colname and legend title
-heatmap_plot(D, "tmedian", "time")
-

garlic/fig/saiph/ss.R

@@ -0,0 +1,109 @@
+library(ggplot2)
+library(dplyr, warn.conflicts = FALSE)
+library(scales)
+library(jsonlite)
+library(viridis, warn.conflicts = FALSE)
+library(stringr)
+
+args = commandArgs(trailingOnly=TRUE)
+
+# Set the input dataset if given in argv[1], or use "input" as default
+if (length(args)>0) { input_file = args[1] } else { input_file = "input" }
+
+df = jsonlite::stream_in(file(input_file), verbose=FALSE) %>%
+
+  jsonlite::flatten() %>%
+
+  select(unit,
+    config.nodes,
+    config.nblx,
+    config.nbly,
+    config.nblz,
+    config.gitBranch,
+    config.blocksPerCpu,
+    config.sizex,
+    time,
+    total_time) %>%
+
+  rename(nodes=config.nodes,
+    nblx=config.nblx,
+    nbly=config.nbly,
+    nblz=config.nblz,
+    gitBranch=config.gitBranch,
+    blocksPerCpu=config.blocksPerCpu,
+    sizex=config.sizex) %>%
+
+  # Remove the "garlic/" prefix from the gitBranch
+  mutate(branch = str_replace(gitBranch, "garlic/", "")) %>%
+
+  # Computations before converting to factor
+  mutate(time.nodes = time * nodes) %>%
+
+  # Convert to factors
+  mutate(unit = as.factor(unit)) %>%
+  mutate(nodes = as.factor(nodes)) %>%
+  mutate(gitBranch = as.factor(gitBranch)) %>%
+  mutate(nblx = as.factor(nblx)) %>%
+  mutate(nbly = as.factor(nbly)) %>%
+  mutate(nblz = as.factor(nblz)) %>%
+  mutate(sizex = as.factor(sizex)) %>%
+  mutate(unit = as.factor(unit)) %>%
+
+  # Compute median times
+  group_by(unit) %>%
+  mutate(median.time = median(time)) %>%
+  mutate(median.time.nodes = median(time.nodes)) %>%
+  mutate(normalized.time = time / median.time - 1) %>%
+  mutate(log.median.time = log(median.time)) %>%
+  ungroup()
+
+dpi = 300
+h = 5
+w = 8
+
+maintitle = "Saiph-Heat3D strong scaling"
+
+# ---------------------------------------------------------------------
+
+p = ggplot(df, aes(x=nodes, y=normalized.time, fill=sizex)) +
+  geom_boxplot() +
+  geom_hline(yintercept=c(-0.01, 0.01), linetype="dashed", color="red") +
+  theme_bw() +
+  facet_wrap(branch ~ .) +
+  labs(x="nodes", y="Normalized time",
+    title=sprintf("%s: normalized time", maintitle), 
+    subtitle=input_file) + 
+  theme(plot.subtitle=element_text(size=8))
+
+ggsave("normalized.time.png", plot=p, width=w, height=h, dpi=dpi)
+ggsave("normalized.time.pdf", plot=p, width=w, height=h, dpi=dpi)
+
+# ---------------------------------------------------------------------
+
+p = ggplot(df, aes(x=nodes, y=time, color=sizex)) +
+  geom_point(shape=21, size=3) +
+  geom_line(aes(y=median.time, group=sizex)) +
+  theme_bw() +
+#  facet_wrap(branch ~ .) +
+  labs(x="nodes", y="Time (s)",
+    title=sprintf("%s: time", maintitle), 
+    subtitle=input_file) + 
+  theme(plot.subtitle=element_text(size=8))
+
+ggsave("time.png", plot=p, width=w, height=h, dpi=dpi)
+ggsave("time.pdf", plot=p, width=w, height=h, dpi=dpi)
+
+# ---------------------------------------------------------------------
+
+p = ggplot(df, aes(x=nodes, y=time.nodes, color=sizex)) +
+  geom_point(shape=21, size=3) +
+  geom_line(aes(y=median.time.nodes, group=sizex)) +
+  theme_bw() +
+  #facet_wrap(branch ~ .) +
+  labs(x="nodes", y="Time * nodes (s)",
+    title=sprintf("%s: time * nodes", maintitle), 
+    subtitle=input_file) + 
+  theme(plot.subtitle=element_text(size=8))
+
+ggsave("time.nodes.png", plot=p, width=w, height=h, dpi=dpi)
+ggsave("time.nodes.pdf", plot=p, width=w, height=h, dpi=dpi)