creams: update figures using one single pipeline
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@ -1,71 +0,0 @@
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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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97
garlic/fig/creams/granularity.R
Normal file
97
garlic/fig/creams/granularity.R
Normal file
@ -0,0 +1,97 @@
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library(ggplot2)
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library(dplyr, warn.conflicts = FALSE)
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library(scales)
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library(jsonlite)
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library(viridis, warn.conflicts = FALSE)
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library(stringr)
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args = commandArgs(trailingOnly=TRUE)
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# Set the input dataset if given in argv[1], or use "input" as default
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if (length(args)>0) { input_file = args[1] } else { input_file = "input" }
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df = jsonlite::stream_in(file(input_file), verbose=FALSE) %>%
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jsonlite::flatten() %>%
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select(unit,
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config.nodes,
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config.gitBranch,
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config.granul,
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config.iterations,
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time,
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total_time) %>%
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rename(nodes=config.nodes,
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gitBranch=config.gitBranch,
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granul=config.granul,
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iterations=config.iterations) %>%
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# Remove the "garlic/" prefix from the gitBranch
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mutate(branch = str_replace(gitBranch, "garlic/", "")) %>%
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# Computations before converting to factor
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mutate(time.iter = time / iterations) %>%
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# Convert to factors
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mutate(unit = as.factor(unit)) %>%
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mutate(nodesFactor = as.factor(nodes)) %>%
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mutate(gitBranch = as.factor(gitBranch)) %>%
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mutate(granul = as.factor(granul)) %>%
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mutate(iterations = as.factor(iterations)) %>%
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mutate(unit = as.factor(unit)) %>%
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# Compute median times
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group_by(unit) %>%
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mutate(median.time = median(time)) %>%
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mutate(normalized.time = time / median.time - 1) %>%
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mutate(log.median.time = log(median.time)) %>%
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mutate(median.time.iter = median(time.iter)) %>%
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ungroup()
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dpi = 300
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h = 6
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w = 6
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# ---------------------------------------------------------------------
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p = ggplot(df, aes(x=granul, y=normalized.time)) +
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geom_boxplot() +
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geom_hline(yintercept=c(-0.01, 0.01), linetype="dashed", color="red") +
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theme_bw() +
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facet_wrap(branch ~ .) +
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labs(x="granul", y="Normalized time",
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title="Creams granularity: normalized time",
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subtitle=input_file) +
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theme(plot.subtitle=element_text(size=8))
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ggsave("normalized.time.png", plot=p, width=w, height=h, dpi=dpi)
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ggsave("normalized.time.pdf", plot=p, width=w, height=h, dpi=dpi)
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# ---------------------------------------------------------------------
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p = ggplot(df, aes(x=granul, y=time)) +
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geom_point(shape=21, size=3) +
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geom_line(aes(y=median.time, group=iterations)) +
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theme_bw() +
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facet_wrap(branch ~ .) +
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labs(x="granul", y="Time (s)", title="Creams granularity: time",
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subtitle=input_file) +
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theme(plot.subtitle=element_text(size=8))
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ggsave("time.png", plot=p, width=w, height=h, dpi=dpi)
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ggsave("time.pdf", plot=p, width=w, height=h, dpi=dpi)
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# ---------------------------------------------------------------------
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p = ggplot(df, aes(x=granul, y=time.iter, color=iterations)) +
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geom_point(shape=21, size=3) +
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geom_line(aes(y=median.time.iter, group=iterations)) +
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theme_bw() +
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facet_wrap(branch ~ .) +
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labs(x="granul", y="Time (s)", title="Creams granularity: time / iterations",
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subtitle=input_file) +
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theme(plot.subtitle=element_text(size=8))
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ggsave("time.iter.png", plot=p, width=w, height=h, dpi=dpi)
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ggsave("time.iter.pdf", plot=p, width=w, height=h, dpi=dpi)
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@ -1,107 +1,127 @@
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library(ggplot2)
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library(dplyr)
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library(dplyr, warn.conflicts = FALSE)
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library(scales)
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library(jsonlite)
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library(viridis, warn.conflicts = FALSE)
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library(stringr)
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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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# Set the input dataset if given in argv[1], or use "input" as default
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if (length(args)>0) { input_file = args[1] } else { input_file = "input" }
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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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df = jsonlite::stream_in(file(input_file), verbose=FALSE) %>%
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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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jsonlite::flatten() %>%
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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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select(unit,
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config.nodes,
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config.gitBranch,
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config.granul,
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config.iterations,
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time,
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total_time) %>%
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rename(nodes=config.nodes,
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gitBranch=config.gitBranch,
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granul=config.granul,
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iterations=config.iterations) %>%
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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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mutate(branch = str_replace(gitBranch, "garlic/", "")) %>%
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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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# Computations before converting to factor
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mutate(time.nodes = time * nodes) %>%
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mutate(time.nodes.iter = time.nodes / iterations) %>%
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# Convert to factors
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mutate(unit = as.factor(unit)) %>%
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mutate(nodes = as.factor(nodes)) %>%
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mutate(gitBranch = as.factor(gitBranch)) %>%
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mutate(granul = as.factor(granul)) %>%
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mutate(iterations = as.factor(iterations)) %>%
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mutate(unit = as.factor(unit)) %>%
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# Compute median times
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group_by(unit) %>%
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mutate(median.time = median(time)) %>%
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mutate(median.time.nodes = median(time.nodes)) %>%
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mutate(normalized.time = time / median.time - 1) %>%
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mutate(log.median.time = log(median.time)) %>%
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mutate(median.time.nodes.iter = median(time.nodes.iter)) %>%
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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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dpi = 300
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h = 5
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w=5
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w = 8
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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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# ---------------------------------------------------------------------
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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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p = ggplot(df, aes(x=nodes, y=normalized.time, fill=granul, color=iterations)) +
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geom_boxplot() +
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geom_hline(yintercept=c(-0.01, 0.01), linetype="dashed", color="red") +
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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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facet_wrap(branch ~ .) +
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labs(x="nodes", y="Normalized time",
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title="Creams strong scaling: normalized time",
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subtitle=input_file) +
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theme(plot.subtitle=element_text(size=8))
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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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ggsave("normalized.time.png", plot=p, width=w, height=h, dpi=dpi)
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ggsave("normalized.time.pdf", plot=p, width=w, height=h, dpi=dpi)
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# ---------------------------------------------------------------------
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p = ggplot(df, aes(x=nodes, y=time, color=gitBranch)) +
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geom_point(shape=21, size=3) +
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geom_line(aes(y=median.time, group=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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# facet_wrap(branch ~ .) +
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labs(x="nodes", y="Time (s)", title="Creams strong scaling: time",
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subtitle=input_file) +
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theme(plot.subtitle=element_text(size=8))
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ggsave("time.png", plot=p, width=w, height=h, dpi=dpi)
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ggsave("time.pdf", plot=p, width=w, height=h, dpi=dpi)
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# ---------------------------------------------------------------------
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p = ggplot(df, aes(x=nodes, y=median.time.nodes, color=branch)) +
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geom_point(shape=21, size=3) +
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geom_line(aes(group=branch)) +
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theme_bw() +
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#facet_wrap(branch ~ .) +
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labs(x="nodes", y="Median time * nodes (s)", title="Creams strong scaling: median time * nodes",
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subtitle=input_file) +
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theme(plot.subtitle=element_text(size=8))
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ggsave("median.time.nodes.png", plot=p, width=w, height=h, dpi=dpi)
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ggsave("median.time.nodes.pdf", plot=p, width=w, height=h, dpi=dpi)
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# ---------------------------------------------------------------------
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p = ggplot(df, aes(x=nodes, y=time.nodes, color=branch)) +
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geom_boxplot() +
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theme_bw() +
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facet_wrap(branch ~ .) +
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labs(x="nodes", y="Time * nodes (s)", title="Creams strong scaling: time * nodes",
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subtitle=input_file) +
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theme(plot.subtitle=element_text(size=8))
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ggsave("time.nodes.boxplot.png", plot=p, width=w, height=h, dpi=dpi)
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ggsave("time.nodes.boxplot.pdf", plot=p, width=w, height=h, dpi=dpi)
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# ---------------------------------------------------------------------
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#p = ggplot(df, aes(x=nodes, y=time.nodes.iter, color=branch)) +
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# geom_point(shape=21, size=3) +
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# geom_line(aes(y=median.time.nodes.iter, group=interaction(granul,iterations))) +
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# theme_bw() +
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# #facet_wrap(branch ~ .) +
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# labs(x="nodes", y="Time * nodes / iterations (s)",
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# title="Creams strong scaling: time * nodes / iterations",
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# subtitle=input_file) +
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# theme(plot.subtitle=element_text(size=8))
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#
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#ggsave("time.nodes.iter.png", plot=p, width=w, height=h, dpi=dpi)
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#ggsave("time.nodes.iter.pdf", plot=p, width=w, height=h, dpi=dpi)
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@ -52,12 +52,12 @@ in
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};
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creams = with exp.creams; {
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ss = stdPlot ./creams/ss.R [ ss.hybrid ss.pure ];
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gran1 = stdPlot ./creams/gran.R [ gran.node1 ];
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gran2 = stdPlot ./creams/gran.R [ gran.node2 ];
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gran4 = stdPlot ./creams/gran.R [ gran.node4 ];
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gran8 = stdPlot ./creams/gran.R [ gran.node8 ];
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gran16 = stdPlot ./creams/gran.R [ gran.node16 ];
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ss = stdPlot ./creams/ss.R [ ss ];
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granularity = stdPlot ./creams/granularity.R [ granularity ];
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# Extended version (we could use another R script for those plots
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big.ss = stdPlot ./creams/ss.R [ big.ss ];
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big.granularity = stdPlot ./creams/granularity.R [ big.granularity ];
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};
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osu = with exp.osu; {
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