nbody: add simple test figure
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@ -1,37 +1,53 @@
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library(ggplot2)
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library(ggplot2)
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library(dplyr)
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library(dplyr)
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library(scales)
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library(scales)
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library(jsonlite)
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# Load the dataset
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args=commandArgs(trailingOnly=TRUE)
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df=read.table("data.csv", col.names=c("blocksize", "time"))
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bs_unique = unique(df$blocksize)
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# Read the timetable from args[1]
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nbs=length(bs_unique)
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input_file = "input.json"
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if (length(args)>0) { input_file = args[1] }
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# Load the dataset in NDJSON format
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dataset = jsonlite::stream_in(file(input_file)) %>%
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jsonlite::flatten()
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particles = unique(dataset$config.particles)
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# We only need the nblocks and time
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df = select(dataset, config.nblocks, config.hw.cpusPerSocket, time) %>%
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rename(nblocks=config.nblocks,
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cpusPerSocket=config.hw.cpusPerSocket)
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df = df %>% mutate(blocksPerCpu = nblocks / cpusPerSocket)
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df$nblocks = as.factor(df$nblocks)
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df$blocksPerCpuFactor = as.factor(df$blocksPerCpu)
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# Normalize the time by the median
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# Normalize the time by the median
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D=group_by(df, blocksize) %>%
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D=group_by(df, nblocks) %>%
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mutate(tnorm = time / median(time) - 1) # %>%
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mutate(tnorm = time / median(time) - 1)
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# mutate(bad = (abs(tnorm) >= 0.01)) %>%
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# mutate(color = ifelse(bad,"red","black"))
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D$bad = cut(abs(D$tnorm), breaks=c(-Inf, 0.01, +Inf), labels=c("good", "bad"))
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bs_unique = unique(df$nblocks)
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nbs=length(bs_unique)
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print(D)
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print(D)
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#ppi=300
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ppi=300
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#h=5
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h=5
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#w=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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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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#
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#
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#
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# Create the plot with the normalized time vs blocksize
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# Create the plot with the normalized time vs nblocks
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p = ggplot(D, aes(x=blocksize, y=tnorm)) +
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p = ggplot(data=D, aes(x=blocksPerCpuFactor, y=tnorm)) +
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# Labels
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# Labels
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labs(x="Block size", y="Normalized time",
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labs(x="Blocks/CPU", y="Normalized time",
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title="Nbody normalized time",
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title=sprintf("Nbody normalized time. Particles=%d", particles),
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subtitle="@expResult@") +
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subtitle=input_file) +
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# Center the title
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# Center the title
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#theme(plot.title = element_text(hjust = 0.5)) +
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#theme(plot.title = element_text(hjust = 0.5)) +
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@ -39,44 +55,49 @@ p = ggplot(D, aes(x=blocksize, y=tnorm)) +
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# Black and white mode (useful for printing)
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# Black and white mode (useful for printing)
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#theme_bw() +
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#theme_bw() +
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# Draw boxplots
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# Add the maximum allowed error lines
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geom_boxplot(aes(group=blocksize)) +
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geom_hline(yintercept=c(-0.01, 0.01),
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linetype="dashed", color="red") +
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# Use log2 scale in x
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# Draw boxplots
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scale_x_continuous(trans=log2_trans(),
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geom_boxplot() +
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breaks=bs_unique) +
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scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
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scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
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# Add the maximum allowed error lines
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theme_bw() +
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geom_hline(yintercept=c(-0.01, 0.01),
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linetype="dashed", color="red")
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = c(0.85, 0.85)) #+
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# Place each variant group in one separate plot
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#facet_wrap(~jemalloc)
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# Render the plot
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# Render the plot
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print(p)
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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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#
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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 blocksize
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## Save the png image
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#p = ggplot(D, aes(x=blocksize, y=time, color=bad)) +
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dev.off()
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#
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#
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# labs(x="Blocksize", y="Time (s)",
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png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
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# title="Nbody granularity",
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# subtitle="@expResult@") +
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#
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#
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# geom_point(shape=21, size=1.5) +
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## Create the plot with the normalized time vs nblocks
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# scale_color_manual(values=c("black", "red")) +
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p = ggplot(D, aes(x=blocksPerCpuFactor, y=time)) +
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# scale_x_continuous(trans=log2_trans(),
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# breaks=bs_unique) +
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labs(x="Blocks/CPU", y="Time (s)",
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# scale_y_continuous(trans=log2_trans())
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title=sprintf("Nbody granularity. Particles=%d", particles),
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#
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subtitle=input_file) +
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## Render the plot
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theme_bw() +
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#print(p)
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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_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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# Save the png image
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#dev.off()
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dev.off()
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14
overlay.nix
14
overlay.nix
@ -335,10 +335,11 @@ let
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};
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};
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# Datasets used in the figures
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# Datasets used in the figures
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ds = with self.bsc.garlic; {
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ds = with self.bsc.garlic; with pp; {
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nbody = {
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nbody = with exp.nbody; {
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jemalloc = with exp.nbody; pp.merge [ baseline jemalloc ];
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test = merge [ baseline ];
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freeCpu = with exp.nbody; pp.merge [ baseline freeCpu ];
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jemalloc = merge [ baseline jemalloc ];
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freeCpu = merge [ baseline freeCpu ];
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};
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};
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};
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};
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@ -346,6 +347,11 @@ let
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fig = with self.bsc.garlic; {
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fig = with self.bsc.garlic; {
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nbody = {
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nbody = {
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test = pp.rPlot {
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script = ./garlic/fig/nbody/test.R;
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dataset = ds.nbody.test;
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};
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jemalloc = pp.rPlot {
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jemalloc = pp.rPlot {
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script = ./garlic/fig/nbody/jemalloc.R;
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script = ./garlic/fig/nbody/jemalloc.R;
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dataset = ds.nbody.jemalloc;
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dataset = ds.nbody.jemalloc;
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