122 lines
3.8 KiB
R
122 lines
3.8 KiB
R
library(ggplot2)
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library(dplyr)
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library(scales)
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library(jsonlite)
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library(viridis)
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library(tidyr)
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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.cbs, config.rbs,
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ctf_mode.runtime,
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ctf_mode.task,
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ctf_mode.dead,
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config.cpusPerTask,
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time) %>%
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rename(
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cbs=config.cbs,
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rbs=config.rbs,
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runtime=ctf_mode.runtime,
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task=ctf_mode.task,
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dead=ctf_mode.dead,
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cpusPerTask=config.cpusPerTask,
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)
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df$cbs = as.factor(df$cbs)
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df$rbs = as.factor(df$rbs)
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# Normalize the time by the median
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df = df %>%
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mutate(runtime = runtime * 1e-9 / cpusPerTask) %>%
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mutate(dead = dead * 1e-9 / cpusPerTask) %>%
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mutate(task = task * 1e-9 / cpusPerTask) %>%
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group_by(cbs, rbs) %>%
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mutate(median.time = median(time)) %>%
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mutate(log.median.time = log(median.time)) %>%
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mutate(median.dead = median(dead)) %>%
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mutate(median.runtime = median(runtime)) %>%
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mutate(median.task = median(task)) %>%
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ungroup() #%>%
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print(df)
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heatmap_plot = function(df, colname, title) {
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p = ggplot(df, aes(x=cbs, y=rbs, fill=!!ensym(colname))) +
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geom_raster() +
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#scale_fill_gradient(high="black", low="white") +
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scale_fill_viridis(option="plasma") +
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coord_fixed() +
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theme_bw() +
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theme(axis.text.x=element_text(angle = -45, hjust = 0)) +
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theme(plot.subtitle=element_text(size=8)) +
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#guides(fill = guide_colorbar(barwidth=15, title.position="top")) +
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guides(fill = guide_colorbar(barwidth=12, title.vjust=0.8)) +
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labs(x="cbs", y="rbs",
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title=sprintf("Heat granularity: %s", title),
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subtitle=input_file) +
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theme(legend.position="bottom")
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k=1
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ggsave(sprintf("%s.png", colname), plot=p, width=4.8*k, height=5*k, dpi=300)
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ggsave(sprintf("%s.pdf", colname), plot=p, width=4.8*k, height=5*k, dpi=300)
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}
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heatmap_plot(df, "median.runtime", "runtime")
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heatmap_plot(df, "median.dead", "not used")
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heatmap_plot(df, "median.task", "task")
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cutlevel = 0.5
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# To plot the median.time we crop the larger values:
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df_filtered = filter(df, between(median.time,
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median(time) - (cutlevel * sd(time)),
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median(time) + (cutlevel * sd(time))))
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heatmap_plot(df, "median.time", "execution time (seconds)")
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heatmap_plot(df, "log.median.time", "execution time")
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df_square = filter(df, cbs == rbs) %>%
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gather(key = time.from, value = acc.time,
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c("median.dead", "median.runtime", "median.task"))
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# Colors similar to Paraver
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colors <- c("median.dead" = "gray",
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"median.runtime" = "blue",
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"median.task" = "red")
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p = ggplot(df_square, aes(x=cbs, y=acc.time)) +
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geom_area(aes(fill=time.from, group=time.from)) +
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scale_fill_manual(values = colors) +
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geom_point(aes(y=median.time, color="black")) +
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geom_line(aes(y=median.time, group=0, color="black")) +
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theme_bw() +
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theme(legend.position=c(0.5, 0.7)) +
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scale_color_identity(breaks = c("black"),
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labels = c("Total time"), guide = "legend") +
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labs(x="Blocksize (side)", y="Time (s)",
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fill="Estimated", color="Direct measurement",
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title="Heat granularity: time distribution", subtitle=input_file)
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ggsave("area.time.png", plot=p, width=6, height=6, dpi=300)
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ggsave("area.time.pdf", plot=p, width=6, height=6, dpi=300)
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p = ggplot(df_square, aes(x=cbs, y=acc.time)) +
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geom_col(aes(fill=time.from, group=time.from)) +
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scale_fill_manual(values = colors) +
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theme_bw() +
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theme(legend.position=c(0.5, 0.7)) +
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labs(x="Blocksize (side)", y="Time (s)",
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fill="Estimated", color="Direct measurement",
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title="Heat granularity: time distribution", subtitle=input_file)
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ggsave("col.time.png", plot=p, width=6, height=6, dpi=300)
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ggsave("col.time.pdf", plot=p, width=6, height=6, dpi=300)
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