152 lines
3.5 KiB
R
152 lines
3.5 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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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, config.nodes, time, total_time) %>%
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rename(nby=config.nby, nnodes=config.nodes)
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df$nby = as.factor(df$nby)
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df$nodes = as.factor(df$nnodes)
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# Normalize the time by the median
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D=group_by(df, nby, nodes) %>%
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mutate(tmedian = median(time)) %>%
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mutate(ttmedian = median(total_time)) %>%
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mutate(tnorm = time / tmedian - 1) %>%
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mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0))) %>%
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mutate(tn = tmedian * nnodes) %>%
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ungroup()
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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 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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png("wasted.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 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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geom_point(shape=21, size=3) +
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geom_point(aes(y=total_time), shape=1, size=3, color="red") +
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geom_line(aes(y=tmedian, color=nodes, group=nodes)) +
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geom_line(aes(y=ttmedian, color=nodes, group=nodes)) +
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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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png("test.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=tn)) +
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labs(x="nby", y="Time (s) * nodes",
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title=sprintf("Saiph-Heat3D 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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geom_point(shape=21, size=3) +
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geom_line(aes(color=nodes, group=nodes)) +
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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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