forked from rarias/bscpkgs
Rodrigo Arias Mallo
92cd88e365
The input dataset is not enough to determine which script produced a given plot.
129 lines
4.3 KiB
R
129 lines
4.3 KiB
R
library(ggplot2)
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library(dplyr, warn.conflicts = FALSE)
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library(scales)
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library(jsonlite)
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library(viridis, warn.conflicts = FALSE)
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library(stringr)
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args = commandArgs(trailingOnly=TRUE)
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# Set the input dataset if given in argv[1], or use "input" as default
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if (length(args)>0) { input_file = args[1] } else { input_file = "input" }
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if (length(args)>1) { output = args[2] } else { output = "?" }
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df = jsonlite::stream_in(file(input_file), verbose=FALSE) %>%
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jsonlite::flatten() %>%
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select(unit,
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config.nodes,
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config.gitBranch,
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config.granul,
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config.iterations,
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time,
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total_time) %>%
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rename(nodes=config.nodes,
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gitBranch=config.gitBranch,
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granul=config.granul,
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iterations=config.iterations) %>%
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# Remove the "garlic/" prefix from the gitBranch
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mutate(branch = str_replace(gitBranch, "garlic/", "")) %>%
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# Computations before converting to factor
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mutate(time.nodes = time * nodes) %>%
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mutate(time.nodes.iter = time.nodes / iterations) %>%
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# Convert to factors
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mutate(unit = as.factor(unit)) %>%
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mutate(nodes = as.factor(nodes)) %>%
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mutate(gitBranch = as.factor(gitBranch)) %>%
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mutate(granul = as.factor(granul)) %>%
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mutate(iterations = as.factor(iterations)) %>%
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mutate(unit = as.factor(unit)) %>%
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# Compute median times
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group_by(unit) %>%
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mutate(median.time = median(time)) %>%
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mutate(median.time.nodes = median(time.nodes)) %>%
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mutate(normalized.time = time / median.time - 1) %>%
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mutate(log.median.time = log(median.time)) %>%
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mutate(median.time.nodes.iter = median(time.nodes.iter)) %>%
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ungroup()
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dpi = 300
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h = 5
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w = 8
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# ---------------------------------------------------------------------
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p = ggplot(df, aes(x=nodes, y=normalized.time, fill=granul, color=iterations)) +
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geom_boxplot() +
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geom_hline(yintercept=c(-0.01, 0.01), linetype="dashed", color="red") +
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theme_bw() +
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facet_wrap(branch ~ .) +
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labs(x="nodes", y="Normalized time",
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title="Creams strong scaling: normalized time",
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subtitle=output) +
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theme(plot.subtitle=element_text(size=8))
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ggsave("normalized.time.png", plot=p, width=w, height=h, dpi=dpi)
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ggsave("normalized.time.pdf", plot=p, width=w, height=h, dpi=dpi)
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# ---------------------------------------------------------------------
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p = ggplot(df, aes(x=nodes, y=time, color=gitBranch)) +
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geom_point(shape=21, size=3) +
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geom_line(aes(y=median.time, group=gitBranch)) +
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theme_bw() +
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# facet_wrap(branch ~ .) +
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labs(x="nodes", y="Time (s)", title="Creams strong scaling: time",
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subtitle=output) +
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theme(plot.subtitle=element_text(size=8))
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ggsave("time.png", plot=p, width=w, height=h, dpi=dpi)
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ggsave("time.pdf", plot=p, width=w, height=h, dpi=dpi)
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# ---------------------------------------------------------------------
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p = ggplot(df, aes(x=nodes, y=median.time.nodes, color=branch)) +
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geom_point(shape=21, size=3) +
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geom_line(aes(group=branch)) +
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theme_bw() +
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#facet_wrap(branch ~ .) +
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labs(x="nodes", y="Median time * nodes (s)", title="Creams strong scaling: median time * nodes",
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subtitle=output) +
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theme(plot.subtitle=element_text(size=8))
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ggsave("median.time.nodes.png", plot=p, width=w, height=h, dpi=dpi)
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ggsave("median.time.nodes.pdf", plot=p, width=w, height=h, dpi=dpi)
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# ---------------------------------------------------------------------
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p = ggplot(df, aes(x=nodes, y=time.nodes, color=branch)) +
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geom_boxplot() +
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theme_bw() +
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facet_wrap(branch ~ .) +
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labs(x="nodes", y="Time * nodes (s)", title="Creams strong scaling: time * nodes",
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subtitle=output) +
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theme(plot.subtitle=element_text(size=8))
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ggsave("time.nodes.boxplot.png", plot=p, width=w, height=h, dpi=dpi)
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ggsave("time.nodes.boxplot.pdf", plot=p, width=w, height=h, dpi=dpi)
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# ---------------------------------------------------------------------
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#p = ggplot(df, aes(x=nodes, y=time.nodes.iter, color=branch)) +
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# geom_point(shape=21, size=3) +
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# geom_line(aes(y=median.time.nodes.iter, group=interaction(granul,iterations))) +
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# theme_bw() +
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# #facet_wrap(branch ~ .) +
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# labs(x="nodes", y="Time * nodes / iterations (s)",
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# title="Creams strong scaling: time * nodes / iterations",
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# subtitle=output) +
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# theme(plot.subtitle=element_text(size=8))
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#
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#ggsave("time.nodes.iter.png", plot=p, width=w, height=h, dpi=dpi)
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#ggsave("time.nodes.iter.pdf", plot=p, width=w, height=h, dpi=dpi)
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