forked from rarias/jungle
110 lines
3.6 KiB
R
110 lines
3.6 KiB
R
# This R program takes as argument the dataset that contains the results of the
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# execution of the heat example experiment and produces some plots. All the
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# knowledge to understand how this script works is covered by this nice R book:
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#
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# Winston Chang, R Graphics Cookbook: Practical Recipes for Visualizing Data,
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# O’Reilly Media (2020). 2nd edition
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#
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# Which can be freely read it online here: https://r-graphics.org/
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#
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# Please, search in this book before copying some random (and probably oudated)
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# reply on stack overflow.
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# We load some R packages to import the required functions. We mainly use the
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# tidyverse packages, which are very good for ploting data,
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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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# Here we simply load the arguments to find the input dataset. If nothing is
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# specified we use the file named `input` in the current directory.
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# We can run this script directly using:
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# Rscript <path-to-this-script> <input-dataset>
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# Load the arguments (argv)
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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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# Then we flatten it, as it may contain dictionaries inside the columns
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jsonlite::flatten() %>%
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# Now the dataframe contains all the configuration of the units inside the
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# columns named `config.*`, for example `config.cbs`. We first select only
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# the columns that we need:
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select(config.nblocks,
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config.ncommblocks,
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config.hw.cpusPerSocket,
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config.nodes,
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config.nprocs.x,
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config.nprocs.y,
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config.nprocs.z,
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unit,
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time
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) %>%
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# And then we rename those columns to something shorter:
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rename(nblocks=config.nblocks,
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ncommblocks=config.ncommblocks,
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cpusPerSocket=config.hw.cpusPerSocket,
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nodes=config.nodes,
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npx=config.nprocs.x,
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npy=config.nprocs.y,
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npz=config.nprocs.z
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) %>%
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mutate(axisColor=as.factor(ifelse(npx != 1, "X", ifelse(npy != 1, "Y", "Z")))) %>%
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mutate(blocksPerCpu = nblocks / cpusPerSocket) %>%
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mutate(nblocks = as.factor(nblocks)) %>%
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mutate(blocksPerCpu = as.factor(blocksPerCpu)) %>%
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mutate(nodes = as.factor(nodes)) %>%
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mutate(unit = as.factor(unit)) %>%
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mutate(timePerNprocs = time * npz) %>%
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group_by(unit) %>%
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# And compute some metrics which are applied to each group. For example we
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# compute the median time within the runs of a 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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# Then, we remove the grouping. This step is very important, otherwise the
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# plotting functions get confused:
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ungroup()
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dpi=300
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h=5
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w=5
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# We plot the time of each run as we vary the block size
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p = ggplot(df, aes(x=nodes, y=timePerNprocs, color=blocksPerCpu)) +
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# We add a points (scatter plot) using circles (shape=21) a bit larger
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# than the default (size=3)
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geom_point(shape=21, size=3) +
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# The bw theme is recommended for publications
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theme_bw() +
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# Here we add the title and the labels of the axes
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labs(x="Nodes", y="Time * Num Procs", title="HPCG strong scalability: Z axis",
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color="Blocks Per CPU",
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subtitle=input_file) +
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# And set the subtitle font size a bit smaller, so it fits nicely
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theme(plot.subtitle=element_text(size=8))
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# Then, we save the plot both in png and pdf
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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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