fwi: update strong scaling figure script
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99c6196734
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94
garlic/fig/fwi/ss.R
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94
garlic/fig/fwi/ss.R
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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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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.blocksize,
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config.gitBranch,
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config.nodes,
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time) %>%
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rename(blocksize=config.blocksize,
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nodes=config.nodes,
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gitBranch=config.gitBranch) %>%
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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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mutate(time.nodes = time * nodes) %>%
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mutate(unit = as.factor(unit)) %>%
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mutate(gitBranch = as.factor(gitBranch)) %>%
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mutate(branch = as.factor(branch)) %>%
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mutate(blocksize = as.factor(blocksize)) %>%
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mutate(nodes = as.factor(nodes)) %>%
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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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ungroup()
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dpi = 300
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h = 6
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w = 6
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main_title = "FWI strong scaling"
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# ---------------------------------------------------------------------
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p = ggplot(df, aes(x=nodes, y=normalized.time)) +
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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=sprintf("%s: normalized time", main_title),
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subtitle=input_file) +
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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(y="Time (s)",
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title=sprintf("%s: time", main_title),
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subtitle=input_file) +
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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=time.nodes, color=branch)) +
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geom_point(shape=21, size=3) +
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geom_line(aes(y=median.time.nodes, group=branch)) +
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theme_bw() +
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#facet_wrap(branch ~ .) +
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labs(x="nodes", y="Time * nodes (s)",
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title=sprintf("%s: time * nodes", main_title),
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subtitle=input_file) +
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theme(plot.subtitle=element_text(size=8))
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ggsave("time.nodes.png", plot=p, width=w, height=h, dpi=dpi)
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ggsave("time.nodes.pdf", plot=p, width=w, height=h, dpi=dpi)
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@ -1,120 +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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# Select block size to display
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useBlocksize = 2
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# We only need the nblocks and time
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df = select(dataset, config.blocksize, config.gitBranch, config.nodes, time) %>%
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rename(
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blocksize=config.blocksize,
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gitBranch=config.gitBranch,
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nodes=config.nodes
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) %>%
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filter(blocksize == useBlocksize | blocksize == 0) %>%
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group_by(nodes, gitBranch) %>%
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mutate(mtime = median(time)) %>%
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mutate(nxmtime = mtime * nodes) %>%
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mutate(nxtime = time * nodes) %>%
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ungroup()
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df$gitBranch = as.factor(df$gitBranch)
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df$blocksize = as.factor(df$blocksize)
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df$nodes = as.factor(df$nodes)
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ppi=300
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h=5
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w=5
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####################################################################
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### Line plot (time)
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####################################################################
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png("time.png", width=w*ppi, height=h*ppi, res=ppi)
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p = ggplot(df, aes(x=nodes, y=time, group=gitBranch, color=gitBranch)) +
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geom_point() +
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geom_line() +
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theme_bw() +
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labs(x="Nodes", y="Time (s)", title="FWI strong scaling",
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subtitle=input_file) +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = c(0.6, 0.75))
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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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### Line plot (time x nodes)
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####################################################################
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png("nxtime.png", width=w*ppi, height=h*ppi, res=ppi)
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p = ggplot(df, aes(x=nodes, y=nxtime, group=gitBranch, color=gitBranch)) +
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geom_point() +
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geom_line() +
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theme_bw() +
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labs(x="Nodes", y="Time * Nodes (s)", title="FWI strong scaling",
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subtitle=input_file) +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = c(0.15, 0.80)) +
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theme(legend.text = element_text(size = 7))
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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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### Line plot (median time)
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####################################################################
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png("mediantime.png", width=w*ppi, height=h*ppi, res=ppi)
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p = ggplot(df, aes(x=nodes, y=mtime, group=gitBranch, color=gitBranch)) +
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geom_point() +
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geom_line() +
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theme_bw() +
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labs(x="Nodes", y="Median Time (s)", title="FWI strong scaling",
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subtitle=input_file) +
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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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# 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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### Line plot (nodes x median time)
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####################################################################
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png("nxmtime.png", width=w*ppi, height=h*ppi, res=ppi)
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p = ggplot(df, aes(x=nodes, y=nxmtime, group=gitBranch, color=gitBranch)) +
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geom_point() +
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geom_line() +
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theme_bw() +
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labs(x="Nodes", y="Median Time * Nodes (s)", title="FWI strong scaling",
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subtitle=input_file) +
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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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# 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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