saiph: use nby for granularity plot
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@ -15,13 +15,13 @@ dataset = jsonlite::stream_in(file(input_file)) %>%
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# We only need the nblocks and time
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df = select(dataset, config.nbx, time) %>%
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rename(nbx=config.nbx)
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df = select(dataset, config.nby, time) %>%
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rename(nby=config.nby)
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df$nbx = as.factor(df$nbx)
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df$nby = as.factor(df$nby)
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# Normalize the time by the median
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D=group_by(df, nbx) %>%
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D=group_by(df, nby) %>%
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mutate(tnorm = time / median(time) - 1) %>%
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mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0)))
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@ -39,10 +39,10 @@ png("box.png", width=w*ppi, height=h*ppi, res=ppi)
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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=nbx, y=tnorm, color=bad)) +
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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="nbx", y="Normalized time",
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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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@ -80,9 +80,9 @@ dev.off()
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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=nbx, y=time)) +
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p = ggplot(D, aes(x=nby, y=time)) +
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labs(x="nbx", y="Time (s)",
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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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