72 lines
2.0 KiB
R
72 lines
2.0 KiB
R
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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), 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,
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config.granul, time, total_time) %>%
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rename(nodes=config.nodes, gitBranch=config.gitBranch,
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granul=config.granul)
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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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df$granul = as.factor(df$granul)
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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("time.png", width=w*1.5*ppi, height=h*ppi, res=ppi)
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p = ggplot(D, aes(x=granul, y=mtime, linetype=gitBranch, shape=nodes)) +
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geom_line(aes(group=interaction(nodes, gitBranch))) +
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geom_point(aes(y=time)) +
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scale_y_continuous(trans=log2_trans()) +
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labs(x="Granularity", y="Time (s)",
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title="Creams 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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print(p)
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dev.off()
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