hpcg: update figures and remove old ones
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garlic/fig/hpcg/granularity.R
Normal file
62
garlic/fig/hpcg/granularity.R
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@ -0,0 +1,62 @@
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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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args = commandArgs(trailingOnly=TRUE)
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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(config.nblocks,
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config.ncomms,
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config.hw.cpusPerSocket,
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config.blocksPerCpu,
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unit,
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time) %>%
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rename(nblocks=config.nblocks,
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ncomms=config.ncomms,
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blocksPerCpu=config.blocksPerCpu) %>%
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mutate(nblocks = as.factor(nblocks)) %>%
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mutate(blocksPerCpu = as.factor(blocksPerCpu)) %>%
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mutate(unit = as.factor(unit)) %>%
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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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ungroup()
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dpi=300
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h=5
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w=5
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p = ggplot(df, aes(x=blocksPerCpu, 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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labs(x="Blocks per CPU", y="Normalized time", title="HPCG 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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p = ggplot(df, aes(x=blocksPerCpu, y=time)) +
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geom_point(shape=21, size=3) +
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theme_bw() +
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labs(x="Blocks per CPU", y="Time (s)", title="HPCG 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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@ -1,112 +0,0 @@
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# 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, config.ncommblocks, config.hw.cpusPerSocket, unit, time) %>%
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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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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(unit = as.factor(unit)) %>%
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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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p = ggplot(df, aes(x=blocksPerCpu, y=normalized.time)) +
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# The boxplots are useful to identify outliers and problems with the
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# distribution of time
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geom_boxplot() +
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# We add a line to mark the 1% limit above and below the median
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geom_hline(yintercept=c(-0.01, 0.01), linetype="dashed", color="red") +
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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="Blocks per CPU", y="Normalized time", title="HPCG granularity: normalized time",
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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("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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# We plot the time of each run as we vary the block size
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p = ggplot(df, aes(x=blocksPerCpu, y=time)) +
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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="Blocks Per CPU", y="Time (s)", title="HPCG granularity: time",
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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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@ -1,112 +0,0 @@
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# 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, config.ncommblocks, config.hw.cpusPerSocket, unit, time) %>%
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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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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(unit = as.factor(unit)) %>%
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group_by(unit) %>%
|
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|
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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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p = ggplot(df, aes(x=blocksPerCpu, y=normalized.time)) +
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# The boxplots are useful to identify outliers and problems with the
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# distribution of time
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geom_boxplot() +
|
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|
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# We add a line to mark the 1% limit above and below the median
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geom_hline(yintercept=c(-0.01, 0.01), linetype="dashed", color="red") +
|
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# The bw theme is recommended for publications
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theme_bw() +
|
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|
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# Here we add the title and the labels of the axes
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labs(x="Blocks per CPU", y="Normalized time", title="HPCG granularity: normalized time",
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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("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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# We plot the time of each run as we vary the block size
|
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p = ggplot(df, aes(x=blocksPerCpu, y=time)) +
|
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|
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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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|
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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="Blocks Per CPU", y="Time (s)", title="HPCG granularity: time",
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subtitle=input_file) +
|
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|
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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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@ -1,116 +0,0 @@
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# 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
|
||||
# knowledge to understand how this script works is covered by this nice R book:
|
||||
#
|
||||
# Winston Chang, R Graphics Cookbook: Practical Recipes for Visualizing Data,
|
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# 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
|
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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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|
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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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|
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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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|
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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, config.ncommblocks, config.hw.cpusPerSocket, config.nodes, unit, time) %>%
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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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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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group_by(unit) %>%
|
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|
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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:
|
||||
mutate(median.time = median(time)) %>%
|
||||
mutate(normalized.time = time / median.time - 1) %>%
|
||||
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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|
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dpi=300
|
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h=5
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w=5
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|
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p = ggplot(df, aes(x=nodes, y=normalized.time, color=blocksPerCpu)) +
|
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|
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# The boxplots are useful to identify outliers and problems with the
|
||||
# distribution of time
|
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geom_boxplot() +
|
||||
|
||||
# We add a line to mark the 1% limit above and below the median
|
||||
geom_hline(yintercept=c(-0.01, 0.01), linetype="dashed", color="red") +
|
||||
|
||||
# The bw theme is recommended for publications
|
||||
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="Normalized time", title="HPCG weak scalability: normalized time",
|
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color="Blocks per CPU",
|
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subtitle=input_file) +
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|
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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))
|
||||
|
||||
# Then, we save the plot both in png and pdf
|
||||
ggsave("normalized.time.png", plot=p, width=w, height=h, dpi=dpi)
|
||||
ggsave("normalized.time.pdf", plot=p, width=w, height=h, dpi=dpi)
|
||||
|
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# We plot the time of each run as we vary the block size
|
||||
p = ggplot(df, aes(x=nodes, y=time, 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 (s)", title="HPCG weak scalability: time",
|
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color="Blocks per CPU",
|
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subtitle=input_file) +
|
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|
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# 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)
|
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ggsave("time.pdf", plot=p, width=w, height=h, dpi=dpi)
|
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|
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@ -1,109 +0,0 @@
|
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# 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)
|
||||
|
||||
@ -1,110 +0,0 @@
|
||||
# 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)) %>%
|
||||
|
||||
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
|
||||
w=3*w
|
||||
|
||||
# We plot the time of each run as we vary the block size
|
||||
p = ggplot(df, aes(x=blocksPerCpu, y=time, color=axisColor)) +
|
||||
|
||||
# We add a points (scatter plot) using circles (shape=21) a bit larger
|
||||
# than the default (size=3)
|
||||
geom_point(shape=21, size=3) +
|
||||
|
||||
facet_wrap(~ nodes, labeller="label_both") +
|
||||
|
||||
# The bw theme is recommended for publications
|
||||
theme_bw() +
|
||||
|
||||
# Here we add the title and the labels of the axes
|
||||
labs(x="Blocks Per CPU", y="Time (s)", title="HPCG weak scalability: time",
|
||||
color="Axis",
|
||||
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)
|
||||
|
||||
70
garlic/fig/hpcg/size.R
Normal file
70
garlic/fig/hpcg/size.R
Normal file
@ -0,0 +1,70 @@
|
||||
library(ggplot2)
|
||||
library(dplyr, warn.conflicts = FALSE)
|
||||
library(scales)
|
||||
library(jsonlite)
|
||||
library(viridis, warn.conflicts = FALSE)
|
||||
|
||||
args = commandArgs(trailingOnly=TRUE)
|
||||
|
||||
if (length(args)>0) { input_file = args[1] } else { input_file = "input" }
|
||||
|
||||
df = jsonlite::stream_in(file(input_file), verbose=FALSE) %>%
|
||||
|
||||
jsonlite::flatten() %>%
|
||||
|
||||
select(config.nblocks,
|
||||
config.hw.cpusPerSocket,
|
||||
config.nodes,
|
||||
config.nprocs.x,
|
||||
config.nprocs.y,
|
||||
config.nprocs.z,
|
||||
config.blocksPerCpu,
|
||||
config.sizePerCpu.z,
|
||||
unit,
|
||||
time
|
||||
) %>%
|
||||
|
||||
rename(nblocks=config.nblocks,
|
||||
cpusPerSocket=config.hw.cpusPerSocket,
|
||||
nodes=config.nodes,
|
||||
blocksPerCpu=config.blocksPerCpu,
|
||||
sizePerCpu.z=config.sizePerCpu.z,
|
||||
npx=config.nprocs.x,
|
||||
npy=config.nprocs.y,
|
||||
npz=config.nprocs.z
|
||||
) %>%
|
||||
|
||||
mutate(time.nodes = time * nodes) %>%
|
||||
mutate(time.nodes.elem = time.nodes / sizePerCpu.z) %>%
|
||||
|
||||
mutate(axisColor=as.factor(ifelse(npx != 1, "X", ifelse(npy != 1, "Y", "Z")))) %>%
|
||||
|
||||
mutate(nblocks = as.factor(nblocks)) %>%
|
||||
mutate(blocksPerCpu = as.factor(blocksPerCpu)) %>%
|
||||
mutate(sizePerCpu.z = as.factor(sizePerCpu.z)) %>%
|
||||
mutate(nodes = as.factor(nodes)) %>%
|
||||
mutate(unit = as.factor(unit)) %>%
|
||||
|
||||
group_by(unit) %>%
|
||||
|
||||
mutate(median.time = median(time)) %>%
|
||||
mutate(normalized.time = time / median.time - 1) %>%
|
||||
mutate(log.median.time = log(median.time)) %>%
|
||||
|
||||
ungroup()
|
||||
|
||||
dpi=300
|
||||
h=5
|
||||
w=5
|
||||
|
||||
p = ggplot(df, aes(x=sizePerCpu.z, y=time.nodes.elem)) +
|
||||
geom_point(shape=21, size=3) +
|
||||
theme_bw() +
|
||||
labs(x="Size per CPU in Z", y="Time * nodes / spcz (s)",
|
||||
title="HPCG size: time * nodes / spcz",
|
||||
subtitle=input_file) +
|
||||
theme(plot.subtitle=element_text(size=8),
|
||||
legend.position="bottom")
|
||||
|
||||
ggsave("time.nodes.png", plot=p, width=w, height=h, dpi=dpi)
|
||||
ggsave("time.nodes.pdf", plot=p, width=w, height=h, dpi=dpi)
|
||||
81
garlic/fig/hpcg/ss.R
Normal file
81
garlic/fig/hpcg/ss.R
Normal file
@ -0,0 +1,81 @@
|
||||
library(ggplot2)
|
||||
library(dplyr, warn.conflicts = FALSE)
|
||||
library(scales)
|
||||
library(jsonlite)
|
||||
library(viridis, warn.conflicts = FALSE)
|
||||
|
||||
args = commandArgs(trailingOnly=TRUE)
|
||||
|
||||
if (length(args)>0) { input_file = args[1] } else { input_file = "input" }
|
||||
|
||||
df = jsonlite::stream_in(file(input_file), verbose=FALSE) %>%
|
||||
|
||||
jsonlite::flatten() %>%
|
||||
|
||||
select(config.nblocks,
|
||||
config.hw.cpusPerSocket,
|
||||
config.nodes,
|
||||
config.nprocs.x,
|
||||
config.nprocs.y,
|
||||
config.nprocs.z,
|
||||
config.blocksPerCpu,
|
||||
config.sizePerCpu.z,
|
||||
unit,
|
||||
time
|
||||
) %>%
|
||||
|
||||
rename(nblocks=config.nblocks,
|
||||
cpusPerSocket=config.hw.cpusPerSocket,
|
||||
nodes=config.nodes,
|
||||
blocksPerCpu=config.blocksPerCpu,
|
||||
sizePerCpu.z=config.sizePerCpu.z,
|
||||
npx=config.nprocs.x,
|
||||
npy=config.nprocs.y,
|
||||
npz=config.nprocs.z
|
||||
) %>%
|
||||
|
||||
mutate(time.sizeZ = time / sizePerCpu.z) %>%
|
||||
mutate(time.nodes = time * nodes) %>%
|
||||
mutate(axisColor=as.factor(ifelse(npx != 1, "X", ifelse(npy != 1, "Y", "Z")))) %>%
|
||||
|
||||
mutate(nblocks = as.factor(nblocks)) %>%
|
||||
mutate(blocksPerCpu = as.factor(blocksPerCpu)) %>%
|
||||
mutate(nodes = as.factor(nodes)) %>%
|
||||
mutate(unit = as.factor(unit)) %>%
|
||||
mutate(sizePerCpu.z = as.factor(sizePerCpu.z)) %>%
|
||||
|
||||
group_by(unit) %>%
|
||||
|
||||
mutate(median.time = median(time)) %>%
|
||||
mutate(normalized.time = time / median.time - 1) %>%
|
||||
mutate(log.median.time = log(median.time)) %>%
|
||||
|
||||
ungroup()
|
||||
|
||||
dpi=300
|
||||
h=7
|
||||
w=7
|
||||
|
||||
p = ggplot(df, aes(x=nodes, y=time.nodes)) +
|
||||
geom_boxplot() +
|
||||
theme_bw() +
|
||||
labs(x="Nodes", y="Time * nodes (s)",
|
||||
title="HPCG strong scalability in Z",
|
||||
subtitle=input_file) +
|
||||
theme(plot.subtitle=element_text(size=8),
|
||||
legend.position="bottom")
|
||||
|
||||
ggsave("time.nodes.png", plot=p, width=w, height=h, dpi=dpi)
|
||||
ggsave("time.nodes.pdf", plot=p, width=w, height=h, dpi=dpi)
|
||||
|
||||
p = ggplot(df, aes(x=nodes, y=time.sizeZ, fill=sizePerCpu.z)) +
|
||||
geom_boxplot() +
|
||||
theme_bw() +
|
||||
labs(x="Nodes", y="Time / npcz (s)", title="HPCG strong scalability in Z",
|
||||
color="Size per CPU in Z",
|
||||
subtitle=input_file) +
|
||||
theme(plot.subtitle=element_text(size=8),
|
||||
legend.position="bottom")
|
||||
|
||||
ggsave("time.size.png", plot=p, width=w, height=h, dpi=dpi)
|
||||
ggsave("time.size.pdf", plot=p, width=w, height=h, dpi=dpi)
|
||||
70
garlic/fig/hpcg/ws.R
Normal file
70
garlic/fig/hpcg/ws.R
Normal file
@ -0,0 +1,70 @@
|
||||
library(ggplot2)
|
||||
library(dplyr, warn.conflicts = FALSE)
|
||||
library(scales)
|
||||
library(jsonlite)
|
||||
library(viridis, warn.conflicts = FALSE)
|
||||
|
||||
args = commandArgs(trailingOnly=TRUE)
|
||||
|
||||
if (length(args)>0) { input_file = args[1] } else { input_file = "input" }
|
||||
|
||||
df = jsonlite::stream_in(file(input_file), verbose=FALSE) %>%
|
||||
|
||||
jsonlite::flatten() %>%
|
||||
|
||||
select(config.nblocks,
|
||||
config.hw.cpusPerSocket,
|
||||
config.nodes,
|
||||
config.nprocs.x,
|
||||
config.nprocs.y,
|
||||
config.nprocs.z,
|
||||
config.blocksPerCpu,
|
||||
config.sizePerCpu.z,
|
||||
unit,
|
||||
time
|
||||
) %>%
|
||||
|
||||
rename(nblocks=config.nblocks,
|
||||
cpusPerSocket=config.hw.cpusPerSocket,
|
||||
nodes=config.nodes,
|
||||
blocksPerCpu=config.blocksPerCpu,
|
||||
sizePerCpu.z=config.sizePerCpu.z,
|
||||
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(time.sizeZ = time / sizePerCpu.z) %>%
|
||||
|
||||
mutate(nblocks = as.factor(nblocks)) %>%
|
||||
mutate(blocksPerCpu = as.factor(blocksPerCpu)) %>%
|
||||
mutate(nodes = as.factor(nodes)) %>%
|
||||
mutate(unit = as.factor(unit)) %>%
|
||||
mutate(sizePerCpu.z = as.factor(sizePerCpu.z)) %>%
|
||||
|
||||
mutate(timePerNprocs = time * npz) %>%
|
||||
|
||||
group_by(unit) %>%
|
||||
|
||||
mutate(median.time = median(time)) %>%
|
||||
mutate(normalized.time = time / median.time - 1) %>%
|
||||
mutate(log.median.time = log(median.time)) %>%
|
||||
|
||||
ungroup()
|
||||
|
||||
dpi=300
|
||||
h=7
|
||||
w=7
|
||||
|
||||
p = ggplot(df, aes(x=nodes, y=time, fill=sizePerCpu.z)) +
|
||||
geom_boxplot() +
|
||||
theme_bw() +
|
||||
labs(x="Nodes", y="Time (s)", title="HPCG weak scaling in Z",
|
||||
color="Size per CPU in Z",
|
||||
subtitle=input_file) +
|
||||
theme(plot.subtitle=element_text(size=8),
|
||||
legend.position="bottom")
|
||||
|
||||
ggsave("time.nodes.png", plot=p, width=w, height=h, dpi=dpi)
|
||||
ggsave("time.nodes.pdf", plot=p, width=w, height=h, dpi=dpi)
|
||||
@ -38,14 +38,10 @@ in
|
||||
};
|
||||
|
||||
hpcg = with exp.hpcg; {
|
||||
# /nix/store/8dr191vch1nw7vfz8nj36d5nhwnbdnf3-plot
|
||||
ossGranularity = stdPlot ./hpcg/oss.granularity.R [ ossGranularity ];
|
||||
|
||||
# /nix/store/a3x76fbnfbacn2xhz3q65fklfp0qbb6p-plot
|
||||
ossWeakscalingPerAxisPerBlock = stdPlot ./hpcg/oss.slices.weakscaling.R [ ossSlicesWeakscaling ];
|
||||
|
||||
# /nix/store/096rl6344pbz5wrzgxgqn651pysfkkjc-plot
|
||||
ossStrongscalingPerBlock = stdPlot ./hpcg/oss.slices.strongscaling.R [ ossSlicesStrongscaling ];
|
||||
ss = stdPlot ./hpcg/ss.R [ ss ];
|
||||
ws = stdPlot ./hpcg/ws.R [ ws ];
|
||||
size = stdPlot ./hpcg/size.R [ size ];
|
||||
granularity = stdPlot ./hpcg/granularity.R [ granularity ];
|
||||
};
|
||||
|
||||
saiph = with exp.saiph; {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user