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
|
|||
|
|
# execution of the heat example experiment and produces some plots. All the
|
|||
|
|
# knowledge to understand how this script works is covered by this nice R book:
|
|||
|
|
#
|
|||
|
|
# Winston Chang, R Graphics Cookbook: Practical Recipes for Visualizing Data,
|
|||
|
|
# O’Reilly Media (2020). 2nd edition
|
|||
|
|
#
|
|||
|
|
# Which can be freely read it online here: https://r-graphics.org/
|
|||
|
|
#
|
|||
|
|
# Please, search in this book before copying some random (and probably oudated)
|
|||
|
|
# reply on stack overflow.
|
|||
|
|
|
|||
|
|
# We load some R packages to import the required functions. We mainly use the
|
|||
|
|
# tidyverse packages, which are very good for ploting data,
|
|||
|
|
library(ggplot2)
|
|||
|
|
library(dplyr, warn.conflicts = FALSE)
|
|||
|
|
library(scales)
|
|||
|
|
library(jsonlite)
|
|||
|
|
library(viridis, warn.conflicts = FALSE)
|
|||
|
|
|
|||
|
|
# Here we simply load the arguments to find the input dataset. If nothing is
|
|||
|
|
# specified we use the file named `input` in the current directory.
|
|||
|
|
# We can run this script directly using:
|
|||
|
|
# Rscript <path-to-this-script> <input-dataset>
|
|||
|
|
|
|||
|
|
# Load the arguments (argv)
|
|||
|
|
args = commandArgs(trailingOnly=TRUE)
|
|||
|
|
|
|||
|
|
# Set the input dataset if given in argv[1], or use "input" as default
|
|||
|
|
if (length(args)>0) { input_file = args[1] } else { input_file = "input" }
|
|||
|
|
|
|||
|
|
df = jsonlite::stream_in(file(input_file), verbose=FALSE) %>%
|
|||
|
|
|
|||
|
|
# Then we flatten it, as it may contain dictionaries inside the columns
|
|||
|
|
jsonlite::flatten() %>%
|
|||
|
|
|
|||
|
|
# Now the dataframe contains all the configuration of the units inside the
|
|||
|
|
# columns named `config.*`, for example `config.cbs`. We first select only
|
|||
|
|
# the columns that we need:
|
|||
|
|
select(config.nblocks,
|
|||
|
|
config.ncommblocks,
|
|||
|
|
config.hw.cpusPerSocket,
|
|||
|
|
config.nodes,
|
|||
|
|
config.nprocs.x,
|
|||
|
|
config.nprocs.y,
|
|||
|
|
config.nprocs.z,
|
|||
|
|
unit,
|
|||
|
|
time
|
|||
|
|
) %>%
|
|||
|
|
|
|||
|
|
# And then we rename those columns to something shorter:
|
|||
|
|
rename(nblocks=config.nblocks,
|
|||
|
|
ncommblocks=config.ncommblocks,
|
|||
|
|
cpusPerSocket=config.hw.cpusPerSocket,
|
|||
|
|
nodes=config.nodes,
|
|||
|
|
npx=config.nprocs.x,
|
|||
|
|
npy=config.nprocs.y,
|
|||
|
|
npz=config.nprocs.z
|
|||
|
|
) %>%
|
|||
|
|
|
|||
|
|
mutate(axisColor=as.factor(ifelse(npx != 1, "X", ifelse(npy != 1, "Y", "Z")))) %>%
|
|||
|
|
|
|||
|
|
mutate(blocksPerCpu = nblocks / cpusPerSocket) %>%
|
|||
|
|
|
|||
|
|
mutate(nblocks = as.factor(nblocks)) %>%
|
|||
|
|
mutate(blocksPerCpu = as.factor(blocksPerCpu)) %>%
|
|||
|
|
mutate(nodes = as.factor(nodes)) %>%
|
|||
|
|
mutate(unit = as.factor(unit)) %>%
|
|||
|
|
|
|||
|
|
mutate(timePerNprocs = time * npz) %>%
|
|||
|
|
|
|||
|
|
group_by(unit) %>%
|
|||
|
|
|
|||
|
|
# And compute some metrics which are applied to each group. For example we
|
|||
|
|
# compute the median time within the runs of a unit:
|
|||
|
|
mutate(median.time = median(time)) %>%
|
|||
|
|
mutate(normalized.time = time / median.time - 1) %>%
|
|||
|
|
mutate(log.median.time = log(median.time)) %>%
|
|||
|
|
|
|||
|
|
# Then, we remove the grouping. This step is very important, otherwise the
|
|||
|
|
# plotting functions get confused:
|
|||
|
|
ungroup()
|
|||
|
|
|
|||
|
|
dpi=300
|
|||
|
|
h=5
|
|||
|
|
w=5
|
|||
|
|
|
|||
|
|
# We plot the time of each run as we vary the block size
|
|||
|
|
p = ggplot(df, aes(x=nodes, y=timePerNprocs, color=blocksPerCpu)) +
|
|||
|
|
|
|||
|
|
# We add a points (scatter plot) using circles (shape=21) a bit larger
|
|||
|
|
# than the default (size=3)
|
|||
|
|
geom_point(shape=21, size=3) +
|
|||
|
|
|
|||
|
|
# The bw theme is recommended for publications
|
|||
|
|
theme_bw() +
|
|||
|
|
|
|||
|
|
# Here we add the title and the labels of the axes
|
|||
|
|
labs(x="Nodes", y="Time * Num Procs", title="HPCG strong scalability: Z axis",
|
|||
|
|
color="Blocks Per CPU",
|
|||
|
|
subtitle=input_file) +
|
|||
|
|
|
|||
|
|
# And set the subtitle font size a bit smaller, so it fits nicely
|
|||
|
|
theme(plot.subtitle=element_text(size=8))
|
|||
|
|
|
|||
|
|
# Then, we save the plot both in png and pdf
|
|||
|
|
ggsave("time.png", plot=p, width=w, height=h, dpi=dpi)
|
|||
|
|
ggsave("time.pdf", plot=p, width=w, height=h, dpi=dpi)
|
|||
|
|
|