forked from rarias/bscpkgs
saiph: clean exps and figs
This commit is contained in:
Sandra
@@ -42,14 +42,8 @@ in
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};
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saiph = with exp.saiph; {
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granularity = stdPlot ./saiph/granularity.R [ granularity ];
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scaling = stdPlot ./saiph/scaling.R [ scaling ];
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scaling2 = stdPlot ./saiph/scaling2.R [ scaling2 ];
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scalingnblyz = stdPlot ./saiph/scalingnblyz.R [ scaling scaling2 ];
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blockingY = stdPlot ./saiph/granularityY.R [ blockingY ];
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blockingZ = stdPlot ./saiph/granularityZ.R [ blockingZ ];
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blockingYZ = stdPlot ./saiph/granularityYZ.R [ blockingYZ ];
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blockingZY = stdPlot ./saiph/granularityZY.R [ blockingZY ];
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granularity-saiph = stdPlot ./saiph/granularity-saiph.R [ granularity-saiph ];
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scalability-saiph = stdPlot ./saiph/scalability-saiph.R [ scalability-saiph ];
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};
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heat = with exp.heat; {
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@@ -1,100 +0,0 @@
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library(ggplot2)
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library(dplyr)
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library(scales)
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library(jsonlite)
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args=commandArgs(trailingOnly=TRUE)
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# Read the timetable from args[1]
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input_file = "input.json"
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if (length(args)>0) { input_file = args[1] }
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# Load the dataset in NDJSON format
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dataset = jsonlite::stream_in(file(input_file)) %>%
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jsonlite::flatten()
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# We only need the nblocks and time
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df = select(dataset, config.nby, time) %>%
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rename(nby=config.nby)
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df$nby = as.factor(df$nby)
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# Normalize the time by the median
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D=group_by(df, nby) %>%
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mutate(tnorm = time / median(time) - 1) %>%
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mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
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D$bad = as.factor(D$bad)
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print(D)
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ppi=300
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h=5
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w=5
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png("box.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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#
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#
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# Create the plot with the normalized time vs nblocks
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p = ggplot(data=D, aes(x=nby, y=tnorm, color=bad)) +
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# Labels
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labs(x="nb{y-z}", y="Normalized time",
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title=sprintf("Saiph-Heat3D normalized time"),
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subtitle=input_file) +
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# Center the title
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#theme(plot.title = element_text(hjust = 0.5)) +
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# Black and white mode (useful for printing)
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#theme_bw() +
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# Add the maximum allowed error lines
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geom_hline(yintercept=c(-0.01, 0.01),
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linetype="dashed", color="gray") +
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# Draw boxplots
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geom_boxplot() +
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scale_color_manual(values=c("black", "brown")) +
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#scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = "none")
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#theme(legend.position = c(0.85, 0.85))
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# Render the plot
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print(p)
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## Save the png image
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dev.off()
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#
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png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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## Create the plot with the normalized time vs nblocks
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p = ggplot(D, aes(x=nby, y=time)) +
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labs(x="nb{y-z}", y="Time (s)",
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title=sprintf("Saiph-Heat3D blocking-granularity"),
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subtitle=input_file) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = c(0.5, 0.88)) +
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geom_point(shape=21, size=3) +
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#scale_x_continuous(trans=log2_trans()) +
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scale_y_continuous(trans=log2_trans())
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# Render the plot
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print(p)
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# Save the png image
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dev.off()
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@@ -1,77 +0,0 @@
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library(ggplot2)
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library(dplyr)
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library(scales)
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library(jsonlite)
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args=commandArgs(trailingOnly=TRUE)
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# Read the timetable from args[1]
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input_file = "input1.json"
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if (length(args)>0) { input_file = args[1] }
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input_file2 = "input2.json"
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if (length(args)>0) { input_file2 = args[1] }
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# Load the dataset in NDJSON format
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dataset = jsonlite::stream_in(file(input_file)) %>%
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jsonlite::flatten()
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dataset2 = jsonlite::stream_in(file(input_file2)) %>%
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jsonlite::flatten()
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# We only need the nblocks and time
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df = select(dataset, config.nby, time) %>%
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rename(nby=config.nby)
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df$nby = as.factor(df$nby)
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df2 = select(dataset2, config.nbz, time) %>%
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rename(nbz=config.nbz)
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df2$nbz = as.factor(df2$nbz)
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# Normalize the time by the median
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D=group_by(df, nby) %>%
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mutate(tnorm = time / median(time) - 1) %>%
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mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
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D$bad = as.factor(D$bad)
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print(D)
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D2=group_by(df2, nbz) %>%
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mutate(tnorm = time / median(time) - 1) %>%
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mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
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D2$bad = as.factor(D2$bad)
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print(D)
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print(D2)
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png("scatter-blockY8Z_yZ8.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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## Create the plot with the normalized time vs nblocks
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p = ggplot() +
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geom_point(data=D, aes(x=nby, y=time, colour="nby blocks - nbz = 8"), shape=1, size=3) +
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geom_point(data=D2, aes(x=nbz, y=time, colour="nby = 8 - nbz blocks"), shape=1, size=3) +
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labs(x="nb", y="Time (s)",
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title=sprintf("Saiph-Heat3D blockingY/Z"),
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subtitle=input_file) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = "right") +
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geom_point(shape=21, size=3) +
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scale_colour_discrete("Blocked directions")
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#+ scale_x_continuous(trans=log2_trans())
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#+ scale_y_continuous(trans=log2_trans())
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# Render the plot
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print(p)
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# Save the png image
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dev.off()
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@@ -1,100 +0,0 @@
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library(ggplot2)
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library(dplyr)
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library(scales)
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library(jsonlite)
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args=commandArgs(trailingOnly=TRUE)
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# Read the timetable from args[1]
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input_file = "input.json"
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if (length(args)>0) { input_file = args[1] }
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# Load the dataset in NDJSON format
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dataset = jsonlite::stream_in(file(input_file)) %>%
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jsonlite::flatten()
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# We only need the nblocks and time
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df = select(dataset, config.nby, time) %>%
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rename(nby=config.nby)
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df$nby = as.factor(df$nby)
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# Normalize the time by the median
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D=group_by(df, nby) %>%
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mutate(tnorm = time / median(time) - 1) %>%
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mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
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D$bad = as.factor(D$bad)
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print(D)
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ppi=300
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h=5
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w=5
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png("box.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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#
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#
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# Create the plot with the normalized time vs nblocks
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p = ggplot(data=D, aes(x=nby, y=tnorm, color=bad)) +
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# Labels
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labs(x="nby", y="Normalized time",
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title=sprintf("Saiph-Heat3D normalized time"),
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subtitle=input_file) +
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# Center the title
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#theme(plot.title = element_text(hjust = 0.5)) +
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# Black and white mode (useful for printing)
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#theme_bw() +
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# Add the maximum allowed error lines
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geom_hline(yintercept=c(-0.01, 0.01),
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linetype="dashed", color="gray") +
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# Draw boxplots
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geom_boxplot() +
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scale_color_manual(values=c("black", "brown")) +
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#scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = "none")
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#theme(legend.position = c(0.85, 0.85))
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# Render the plot
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print(p)
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## Save the png image
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dev.off()
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#
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png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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## Create the plot with the normalized time vs nblocks
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p = ggplot(D, aes(x=nby, y=time)) +
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labs(x="nby", y="Time (s)",
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title=sprintf("Saiph-Heat3D blockingY"),
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subtitle=input_file) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = c(0.5, 0.88)) +
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geom_point(shape=21, size=3)
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#+ scale_x_continuous(trans=log2_trans())
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#+ scale_y_continuous(trans=log2_trans())
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# Render the plot
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print(p)
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# Save the png image
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dev.off()
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@@ -1,100 +0,0 @@
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library(ggplot2)
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library(dplyr)
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library(scales)
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library(jsonlite)
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args=commandArgs(trailingOnly=TRUE)
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# Read the timetable from args[1]
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input_file = "input.json"
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if (length(args)>0) { input_file = args[1] }
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# Load the dataset in NDJSON format
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dataset = jsonlite::stream_in(file(input_file)) %>%
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jsonlite::flatten()
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# We only need the nblocks and time
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df = select(dataset, config.nbz, time) %>%
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rename(nbz=config.nbz)
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df$nbz = as.factor(df$nbz)
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# Normalize the time by the median
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D=group_by(df, nbz) %>%
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mutate(tnorm = time / median(time) - 1) %>%
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mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
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D$bad = as.factor(D$bad)
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print(D)
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ppi=300
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h=5
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w=5
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png("box.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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#
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#
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# Create the plot with the normalized time vs nblocks
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p = ggplot(data=D, aes(x=nbz, y=tnorm, color=bad)) +
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# Labels
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labs(x="nbz", y="Normalized time",
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title=sprintf("Saiph-Heat3D normalized time - nby = 8"),
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subtitle=input_file) +
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# Center the title
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#theme(plot.title = element_text(hjust = 0.5)) +
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# Black and white mode (useful for printing)
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#theme_bw() +
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# Add the maximum allowed error lines
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geom_hline(yintercept=c(-0.01, 0.01),
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linetype="dashed", color="gray") +
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# Draw boxplots
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geom_boxplot() +
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scale_color_manual(values=c("black", "brown")) +
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#scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = "none")
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#theme(legend.position = c(0.85, 0.85))
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# Render the plot
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print(p)
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## Save the png image
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dev.off()
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#
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png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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## Create the plot with the normalized time vs nblocks
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p = ggplot(D, aes(x=nbz, y=time)) +
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labs(x="nbz", y="Time (s)",
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title=sprintf("Saiph-Heat3D blockingZ - nby = 8"),
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subtitle=input_file) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = c(0.5, 0.88)) +
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geom_point(shape=21, size=3)
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#+ scale_x_continuous(trans=log2_trans())
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#+ scale_y_continuous(trans=log2_trans())
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# Render the plot
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print(p)
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# Save the png image
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dev.off()
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@@ -1,100 +0,0 @@
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library(ggplot2)
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library(dplyr)
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library(scales)
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library(jsonlite)
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args=commandArgs(trailingOnly=TRUE)
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# Read the timetable from args[1]
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input_file = "input.json"
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if (length(args)>0) { input_file = args[1] }
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# Load the dataset in NDJSON format
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dataset = jsonlite::stream_in(file(input_file)) %>%
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jsonlite::flatten()
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# We only need the nblocks and time
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df = select(dataset, config.nbz, time) %>%
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rename(nbz=config.nbz)
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df$nbz = as.factor(df$nbz)
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# Normalize the time by the median
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D=group_by(df, nbz) %>%
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mutate(tnorm = time / median(time) - 1) %>%
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mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
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D$bad = as.factor(D$bad)
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print(D)
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ppi=300
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h=5
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w=5
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png("box.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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#
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#
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# Create the plot with the normalized time vs nblocks
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p = ggplot(data=D, aes(x=nbz, y=tnorm, color=bad)) +
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# Labels
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labs(x="nbz", y="Normalized time",
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title=sprintf("Saiph-Heat3D normalized time"),
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subtitle=input_file) +
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# Center the title
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#theme(plot.title = element_text(hjust = 0.5)) +
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||||
# Black and white mode (useful for printing)
|
||||
#theme_bw() +
|
||||
|
||||
# Add the maximum allowed error lines
|
||||
geom_hline(yintercept=c(-0.01, 0.01),
|
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linetype="dashed", color="gray") +
|
||||
|
||||
# Draw boxplots
|
||||
geom_boxplot() +
|
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scale_color_manual(values=c("black", "brown")) +
|
||||
|
||||
#scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
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||||
|
||||
theme_bw() +
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|
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = "none")
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#theme(legend.position = c(0.85, 0.85))
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||||
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# Render the plot
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print(p)
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## Save the png image
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dev.off()
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#
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||||
png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
|
||||
#
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||||
## Create the plot with the normalized time vs nblocks
|
||||
p = ggplot(D, aes(x=nbz, y=time)) +
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labs(x="nbz", y="Time (s)",
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title=sprintf("Saiph-Heat3D blockingZ"),
|
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subtitle=input_file) +
|
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theme_bw() +
|
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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
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||||
print(p)
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||||
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||||
# Save the png image
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||||
dev.off()
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||||
@@ -1,100 +0,0 @@
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library(ggplot2)
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||||
library(dplyr)
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||||
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()
|
||||
@@ -1,67 +0,0 @@
|
||||
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()
|
||||
155
garlic/fig/saiph/granularity-saiph.R
Normal file
155
garlic/fig/saiph/granularity-saiph.R
Normal file
@@ -0,0 +1,155 @@
|
||||
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")
|
||||
|
||||
@@ -1,100 +0,0 @@
|
||||
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 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()
|
||||
@@ -1,100 +0,0 @@
|
||||
library(ggplot2)
|
||||
library(dplyr)
|
||||
library(scales)
|
||||
library(jsonlite)
|
||||
|
||||
args=commandArgs(trailingOnly=TRUE)
|
||||
|
||||
# Read the timetable from args[1]
|
||||
input_file = "nov24Gran.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 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()
|
||||
156
garlic/fig/saiph/scalability-saiph.R
Normal file
156
garlic/fig/saiph/scalability-saiph.R
Normal file
@@ -0,0 +1,156 @@
|
||||
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")
|
||||
|
||||
@@ -1,210 +0,0 @@
|
||||
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.nbly, config.nodes, time, total_time, config.gitCommit) %>%
|
||||
# rename(nbly=config.nbly, nnodes=config.nodes, gitCommit=config.gitCommit)
|
||||
|
||||
df = select(dataset, config.nbly, config.nblz, config.nbltotal, config.nodes, time, total_time) %>%
|
||||
rename(nbly=config.nbly, nblz=config.nblz, nbltotal=config.nbltotal, nnodes=config.nodes)
|
||||
|
||||
df$nbly = as.factor(df$nbly)
|
||||
df$nblz = as.factor(df$nblz)
|
||||
df$nblPerProc = as.factor(df$nbltotal / 24)
|
||||
df$nbltotal = as.factor(df$nbltotal)
|
||||
df$nodes = as.factor(df$nnodes)
|
||||
#df$gitCommit = as.factor(df$gitCommit)
|
||||
|
||||
# Normalize the time by the median
|
||||
#D=group_by(df, nbly, nodes, gitCommit) %>%
|
||||
D=group_by(df, nbly, nblz, nbltotal, 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)
|
||||
|
||||
print(D)
|
||||
|
||||
ppi=300
|
||||
h=5
|
||||
w=8
|
||||
|
||||
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=nbly, y=tnorm, color=bad)) +
|
||||
|
||||
# Labels
|
||||
labs(x="nbly", 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=nbltotal, y=time)) +
|
||||
|
||||
labs(x="nbltotal", 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_x_continuous(trans=log2_trans()) +
|
||||
scale_y_continuous(trans=log2_trans())
|
||||
# facet_wrap( ~ gitCommit)
|
||||
|
||||
|
||||
# Render the plot
|
||||
print(p)
|
||||
|
||||
# Save the png image
|
||||
dev.off()
|
||||
|
||||
png("scatter1.png", width=w*ppi, height=h*ppi, res=ppi)
|
||||
#
|
||||
## Create the plot with the normalized time vs nblocks
|
||||
p = ggplot(D, aes(x=nblPerProc, y=time)) +
|
||||
|
||||
labs(x="nblPerProc", y="Time (s)",
|
||||
title=sprintf("Saiph-Heat3D granularity per nodes"),
|
||||
subtitle=input_file) +
|
||||
theme_bw() +
|
||||
theme(plot.subtitle=element_text(size=8)) +
|
||||
theme(legend.position = c(0.5, 0.5)) +
|
||||
|
||||
geom_point(aes(color=nblz), shape=21, size=3) +
|
||||
#scale_x_continuous(trans=log2_trans()) +
|
||||
scale_y_continuous(trans=log2_trans()) +
|
||||
facet_wrap( ~ nodes)
|
||||
|
||||
|
||||
# Render the plot
|
||||
print(p)
|
||||
|
||||
# Save the png image
|
||||
dev.off()
|
||||
|
||||
|
||||
png("wasted.png", width=w*ppi, height=h*ppi, res=ppi)
|
||||
#
|
||||
## Create the plot with the normalized time vs nblocks
|
||||
p = ggplot(D, aes(x=nbly, y=time)) +
|
||||
|
||||
labs(x="nbly", y="Time (s)",
|
||||
title=sprintf("Saiph-Heat3D granularity"),
|
||||
subtitle=input_file) +
|
||||
theme_bw() +
|
||||
theme(plot.subtitle=element_text(size=8)) +
|
||||
|
||||
geom_point(shape=21, size=3) +
|
||||
geom_point(aes(y=total_time), shape=1, size=3, color="red") +
|
||||
geom_line(aes(y=tmedian, color=nodes, group=nodes)) +
|
||||
geom_line(aes(y=ttmedian, color=nodes, group=nodes)) +
|
||||
#scale_x_continuous(trans=log2_trans()) +
|
||||
scale_y_continuous(trans=log2_trans())
|
||||
# facet_wrap( ~ gitCommit)
|
||||
|
||||
# Render the plot
|
||||
print(p)
|
||||
|
||||
# Save the png image
|
||||
dev.off()
|
||||
|
||||
png("test.png", width=w*ppi, height=h*ppi, res=ppi)
|
||||
#
|
||||
## Create the plot with the normalized time vs nblocks
|
||||
p = ggplot(D, aes(x=nbltotal, y=tn)) +
|
||||
|
||||
labs(x="nbltotal", y="Time (s) * nodes",
|
||||
title=sprintf("Saiph-Heat3D granularity"),
|
||||
subtitle=input_file) +
|
||||
theme_bw() +
|
||||
theme(plot.subtitle=element_text(size=8)) +
|
||||
|
||||
geom_point(shape=21, size=3) +
|
||||
geom_line(aes(color=nodes, group=nodes)) +
|
||||
#scale_x_continuous(trans=log2_trans()) +
|
||||
scale_y_continuous(trans=log2_trans())
|
||||
# facet_wrap( ~ gitCommit)
|
||||
|
||||
# Render the plot
|
||||
print(p)
|
||||
|
||||
# Save the png image
|
||||
dev.off()
|
||||
|
||||
png("test1.png", width=w*ppi, height=h*ppi, res=ppi)
|
||||
#
|
||||
## Create the plot with the normalized time vs nblocks
|
||||
p = ggplot(D, aes(x=nblPerProc, y=tn)) +
|
||||
|
||||
labs(x="nblPerProc", y="Time (s) * nodes",
|
||||
title=sprintf("Saiph-Heat3D granularity per nblz blocks"),
|
||||
subtitle=input_file) +
|
||||
theme_bw() +
|
||||
theme(plot.subtitle=element_text(size=8)) +
|
||||
|
||||
geom_point(shape=21, size=3) +
|
||||
geom_line(aes(color=nodes, group=nodes)) +
|
||||
#scale_x_continuous(trans=log2_trans()) +
|
||||
scale_y_continuous(trans=log2_trans()) +
|
||||
facet_wrap( ~ nblz)
|
||||
|
||||
# Render the plot
|
||||
print(p)
|
||||
|
||||
# Save the png image
|
||||
dev.off()
|
||||
@@ -1,210 +0,0 @@
|
||||
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.nbly, config.nodes, time, total_time, config.gitCommit) %>%
|
||||
# rename(nbly=config.nbly, nnodes=config.nodes, gitCommit=config.gitCommit)
|
||||
|
||||
df = select(dataset, config.nbly, config.nblz, config.nbltotal, config.nodes, time, total_time) %>%
|
||||
rename(nbly=config.nbly, nblz=config.nblz, nbltotal=config.nbltotal, nnodes=config.nodes)
|
||||
|
||||
df$nbly = as.factor(df$nbly)
|
||||
df$nblz = as.factor(df$nblz)
|
||||
df$nblPerProc = as.factor(df$nbltotal / 24)
|
||||
df$nbltotal = as.factor(df$nbltotal)
|
||||
df$nodes = as.factor(df$nnodes)
|
||||
#df$gitCommit = as.factor(df$gitCommit)
|
||||
|
||||
# Normalize the time by the median
|
||||
#D=group_by(df, nbly, nodes, gitCommit) %>%
|
||||
D=group_by(df, nbly, nblz, nbltotal, 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)
|
||||
|
||||
print(D)
|
||||
|
||||
ppi=300
|
||||
h=5
|
||||
w=8
|
||||
|
||||
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=nbly, y=tnorm, color=bad)) +
|
||||
|
||||
# Labels
|
||||
labs(x="nbly", 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=nbltotal, y=time)) +
|
||||
|
||||
labs(x="nbltotal", 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_x_continuous(trans=log2_trans()) +
|
||||
scale_y_continuous(trans=log2_trans())
|
||||
# facet_wrap( ~ gitCommit)
|
||||
|
||||
|
||||
# Render the plot
|
||||
print(p)
|
||||
|
||||
# Save the png image
|
||||
dev.off()
|
||||
|
||||
png("scatter1.png", width=w*ppi, height=h*ppi, res=ppi)
|
||||
#
|
||||
## Create the plot with the normalized time vs nblocks
|
||||
p = ggplot(D, aes(x=nblPerProc, y=time)) +
|
||||
|
||||
labs(x="nblPerProc", y="Time (s)",
|
||||
title=sprintf("Saiph-Heat3D granularity per nodes"),
|
||||
subtitle=input_file) +
|
||||
theme_bw() +
|
||||
theme(plot.subtitle=element_text(size=8)) +
|
||||
theme(legend.position = c(0.5, 0.5)) +
|
||||
|
||||
geom_point(aes(color=nbly), shape=21, size=3) +
|
||||
#scale_x_continuous(trans=log2_trans()) +
|
||||
scale_y_continuous(trans=log2_trans()) +
|
||||
facet_wrap( ~ nodes)
|
||||
|
||||
|
||||
# Render the plot
|
||||
print(p)
|
||||
|
||||
# Save the png image
|
||||
dev.off()
|
||||
|
||||
|
||||
png("wasted.png", width=w*ppi, height=h*ppi, res=ppi)
|
||||
#
|
||||
## Create the plot with the normalized time vs nblocks
|
||||
p = ggplot(D, aes(x=nbly, y=time)) +
|
||||
|
||||
labs(x="nbly", y="Time (s)",
|
||||
title=sprintf("Saiph-Heat3D granularity"),
|
||||
subtitle=input_file) +
|
||||
theme_bw() +
|
||||
theme(plot.subtitle=element_text(size=8)) +
|
||||
|
||||
geom_point(shape=21, size=3) +
|
||||
geom_point(aes(y=total_time), shape=1, size=3, color="red") +
|
||||
geom_line(aes(y=tmedian, color=nodes, group=nodes)) +
|
||||
geom_line(aes(y=ttmedian, color=nodes, group=nodes)) +
|
||||
#scale_x_continuous(trans=log2_trans()) +
|
||||
scale_y_continuous(trans=log2_trans())
|
||||
# facet_wrap( ~ gitCommit)
|
||||
|
||||
# Render the plot
|
||||
print(p)
|
||||
|
||||
# Save the png image
|
||||
dev.off()
|
||||
|
||||
png("test.png", width=w*ppi, height=h*ppi, res=ppi)
|
||||
#
|
||||
## Create the plot with the normalized time vs nblocks
|
||||
p = ggplot(D, aes(x=nbltotal, y=tn)) +
|
||||
|
||||
labs(x="nbltotal", y="Time (s) * nodes",
|
||||
title=sprintf("Saiph-Heat3D granularity"),
|
||||
subtitle=input_file) +
|
||||
theme_bw() +
|
||||
theme(plot.subtitle=element_text(size=8)) +
|
||||
|
||||
geom_point(shape=21, size=3) +
|
||||
geom_line(aes(color=nodes, group=nodes)) +
|
||||
#scale_x_continuous(trans=log2_trans()) +
|
||||
scale_y_continuous(trans=log2_trans())
|
||||
# facet_wrap( ~ gitCommit)
|
||||
|
||||
# Render the plot
|
||||
print(p)
|
||||
|
||||
# Save the png image
|
||||
dev.off()
|
||||
|
||||
png("test1.png", width=w*ppi, height=h*ppi, res=ppi)
|
||||
#
|
||||
## Create the plot with the normalized time vs nblocks
|
||||
p = ggplot(D, aes(x=nblPerProc, y=tn)) +
|
||||
|
||||
labs(x="nblPerProc", y="Time (s) * nodes",
|
||||
title=sprintf("Saiph-Heat3D granularity per nbly blocks"),
|
||||
subtitle=input_file) +
|
||||
theme_bw() +
|
||||
theme(plot.subtitle=element_text(size=8)) +
|
||||
|
||||
geom_point(shape=21, size=3) +
|
||||
geom_line(aes(color=nodes, group=nodes)) +
|
||||
#scale_x_continuous(trans=log2_trans()) +
|
||||
scale_y_continuous(trans=log2_trans()) +
|
||||
facet_wrap( ~ nbly)
|
||||
|
||||
# Render the plot
|
||||
print(p)
|
||||
|
||||
# Save the png image
|
||||
dev.off()
|
||||
@@ -1,162 +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()
|
||||
|
||||
|
||||
# We only need the nblocks and time
|
||||
#df = select(dataset, config.nbly, config.nodes, time, total_time, config.gitCommit) %>%
|
||||
# rename(nbly=config.nbly, nnodes=config.nodes, gitCommit=config.gitCommit)
|
||||
|
||||
df = select(dataset, config.nbly, config.nblz, config.nbltotal, config.nodes, time, total_time) %>%
|
||||
rename(nbly=config.nbly, nblz=config.nblz, nbltotal=config.nbltotal, nnodes=config.nodes)
|
||||
|
||||
df2 = df[df$nblz == 1 | df$nblz == 2 | df$nblz == 4, ]
|
||||
df3 = df[df$nbly == 1 | df$nbly == 2 | df$nbly == 4, ]
|
||||
|
||||
# df2 data frame
|
||||
df2$nblsetZ = as.factor(df2$nblz)
|
||||
df2$nblPerProcZ = as.factor(df2$nbltotal / 24)
|
||||
df2$nbltotal = as.factor(df2$nbltotal)
|
||||
df2$nodes = as.factor(df2$nnodes)
|
||||
|
||||
# df3 data frame
|
||||
df3$nblsetY = as.factor(df3$nbly)
|
||||
df3$nblPerProcY = as.factor(df3$nbltotal / 24)
|
||||
df3$nbltotalY = as.factor(df3$nbltotal)
|
||||
df3$nodes = as.factor(df3$nnodes)
|
||||
|
||||
df$nbly = as.factor(df$nbly)
|
||||
df$nblz = as.factor(df$nblz)
|
||||
df$nblPerProc = as.factor(df$nbltotal / 24)
|
||||
df$nbltotal = as.factor(df$nbltotal)
|
||||
df$nodes = as.factor(df$nnodes)
|
||||
#df$gitCommit = as.factor(df$gitCommit)
|
||||
|
||||
# Normalize the time by the median
|
||||
#D=group_by(df, nbly, nodes, gitCommit) %>%
|
||||
D=group_by(df, nbly, nblz, nbltotal, 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)
|
||||
|
||||
print(D)
|
||||
|
||||
ppi=300
|
||||
h=5
|
||||
w=8
|
||||
|
||||
|
||||
png("scatter_nbly.png", width=w*ppi, height=h*ppi, res=ppi)
|
||||
#
|
||||
## Create the plot with the normalized time vs nblocks
|
||||
p = ggplot() +
|
||||
geom_point(data=df2, aes(x=nblPerProcZ, y=time, color=nblsetZ), shape=21, size=3, show.legend=TRUE) +
|
||||
geom_point(data=df3, aes(x=nblPerProcY, y=time, color=nblsetY), shape=4, size=2, show.legend=TRUE) +
|
||||
labs(x="nblPerProc", y="Time (s)",
|
||||
title=sprintf("Saiph-Heat3D granularity per nodes"),
|
||||
subtitle=input_file) +
|
||||
theme_bw() +
|
||||
theme(plot.subtitle=element_text(size=8)) +
|
||||
theme(legend.position = c(0.5, 0.5)) +
|
||||
scale_y_continuous(trans=log2_trans()) +
|
||||
facet_wrap( ~ nodes)
|
||||
|
||||
|
||||
# Render the plot
|
||||
print(p)
|
||||
|
||||
# Save the png image
|
||||
dev.off()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
png("scatter_nbly.png", width=w*ppi, height=h*ppi, res=ppi)
|
||||
#
|
||||
## Create the plot with the normalized time vs nblocks
|
||||
p = ggplot() +
|
||||
geom_point(data=df2, aes(x=nblPerProcZ, y=time, color=nblsetZ), shape=21, size=3, show.legend=TRUE) +
|
||||
geom_point(data=df3, aes(x=nblPerProcY, y=time, color=nblsetY), shape=4, size=2, show.legend=TRUE) +
|
||||
labs(x="nblPerProc", y="Time (s)",
|
||||
title=sprintf("Saiph-Heat3D granularity per nodes"),
|
||||
subtitle=input_file) +
|
||||
theme_bw() +
|
||||
theme(plot.subtitle=element_text(size=8)) +
|
||||
theme(legend.position = c(0.5, 0.5)) +
|
||||
scale_y_continuous(trans=log2_trans()) +
|
||||
facet_wrap( ~ nodes)
|
||||
|
||||
|
||||
# Render the plot
|
||||
print(p)
|
||||
|
||||
# Save the png image
|
||||
dev.off()
|
||||
|
||||
png("test1.png", width=w*ppi, height=h*ppi, res=ppi)
|
||||
#
|
||||
## Create the plot with the normalized time vs nblocks
|
||||
p = ggplot(D, aes(x=nblPerProc, y=tn)) +
|
||||
|
||||
labs(x="nblPerProc", y="Time (s) * nodes",
|
||||
title=sprintf("Saiph-Heat3D granularity per nbly blocks"),
|
||||
subtitle=input_file) +
|
||||
theme_bw() +
|
||||
theme(plot.subtitle=element_text(size=8)) +
|
||||
|
||||
geom_point(shape=21, size=3) +
|
||||
geom_line(aes(color=nodes, group=nodes)) +
|
||||
#scale_x_continuous(trans=log2_trans()) +
|
||||
scale_y_continuous(trans=log2_trans()) +
|
||||
facet_wrap( ~ nbly)
|
||||
|
||||
# Render the plot
|
||||
print(p)
|
||||
|
||||
# Save the png image
|
||||
dev.off()
|
||||
|
||||
|
||||
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=15, title.position="top")) +
|
||||
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)
|
||||
}
|
||||
|
||||
heatmap_plot(D, "tmedian", "time")
|
||||
@@ -1,100 +0,0 @@
|
||||
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()
|
||||
Reference in New Issue
Block a user