forked from rarias/jungle
108 lines
3.3 KiB
R
108 lines
3.3 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), verbose=FALSE) %>%
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jsonlite::flatten()
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# We only need some colums
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df = select(dataset, unit, config.nodes, config.gitBranch, time) %>%
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rename(nodes=config.nodes, gitBranch=config.gitBranch)
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df$unit = as.factor(df$unit)
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df$nnodes = df$nodes
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df$nodes = as.factor(df$nodes)
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df$gitBranch = as.factor(df$gitBranch)
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# Remove the "garlic/" prefix from the gitBranch
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levels(df$gitBranch) <- substring((levels(df$gitBranch)), 8)
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# Compute new columns
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D=group_by(df, unit) %>%
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mutate(tnorm = time / median(time) - 1) %>%
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mutate(bad = ifelse(max(abs(tnorm)) >= 0.01, 1, 0)) %>%
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mutate(variability = ifelse(bad > 0, "large", "ok")) %>%
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mutate(mtime = median(time)) %>%
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mutate(nmtime = mtime*nnodes) %>%
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mutate(ntime = time*nnodes) %>%
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ungroup() %>%
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mutate(min_nmtime = min(nmtime)) %>%
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mutate(rnmtime = nmtime / min_nmtime) %>%
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mutate(rntime = ntime / min_nmtime) %>%
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mutate(rmeff = 1.0 / rnmtime) %>%
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mutate(reff = 1.0 / rntime) %>%
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group_by(gitBranch) %>%
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mutate(tmax = max(mtime)) %>%
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mutate(speedup=tmax/time) %>%
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mutate(eff=speedup/nnodes) %>%
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mutate(mspeedup=tmax/mtime) %>%
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mutate(meff=mspeedup/nnodes) %>%
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ungroup()
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D$bad = as.factor(D$bad > 0)
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D$variability = as.factor(D$variability)
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ppi=300
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h=5
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w=5
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png("variability.png", width=1.5*w*ppi, height=h*ppi, res=ppi)
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p = ggplot(data=D, aes(x=nodes, y=tnorm, color=variability)) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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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(aes(fill=gitBranch)) +
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scale_color_manual(values=c("brown", "black")) +
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# Labels
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labs(x="Nodes", y="Normalized time", title="Creams strong scaling",
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subtitle=input_file)
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print(p)
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dev.off()
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png("time.png", width=w*1.5*ppi, height=h*ppi, res=ppi)
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p = ggplot(D, aes(x=nodes, y=mtime, color=gitBranch)) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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geom_line(aes(group=gitBranch)) +
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#geom_point() +
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geom_point(aes(shape=variability), size=3) +
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scale_shape_manual(values=c(21, 19)) +
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# position=position_dodge(width=0.3)) +
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#scale_x_continuous(trans=log2_trans()) +
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scale_y_continuous(trans=log2_trans()) +
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labs(x="Nodes", y="Time (s)",
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title="Creams strong scaling (lower is better)",
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subtitle=input_file)
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print(p)
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dev.off()
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png("refficiency.png", width=w*1.5*ppi, height=h*ppi, res=ppi)
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p = ggplot(D, aes(x=nodes, y=rmeff, color=gitBranch)) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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geom_line(aes(group=gitBranch)) +
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geom_point(aes(shape=variability), size=3) +
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#geom_boxplot(aes(y=reff),
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# position=position_dodge(width=0.0)) +
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scale_shape_manual(values=c(21, 19)) +
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#geom_point(aes(y=rntime),
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# position=position_dodge(width=0.3)) +
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#scale_x_continuous(trans=log2_trans()) +
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#scale_y_continuous(trans=log2_trans()) +
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labs(x="Nodes", y="Relative efficiency (to best)",
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title="Creams strong scaling (higher is better)",
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subtitle=input_file)
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print(p)
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dev.off()
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