bscpkgs/garlic/fig/heat/mode.R

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library(ggplot2)
library(dplyr)
library(scales)
library(jsonlite)
library(viridis)
library(tidyr)
args=commandArgs(trailingOnly=TRUE)
# Read the timetable from args[1]
input_file = "input.json"
if (length(args)>0) { input_file = args[1] }
# Load the dataset in NDJSON format
dataset = jsonlite::stream_in(file(input_file)) %>%
jsonlite::flatten()
# We only need the nblocks and time
df = select(dataset, config.cbs, config.rbs,
ctf_mode.runtime,
ctf_mode.task,
ctf_mode.dead,
config.cpusPerTask,
time) %>%
rename(
cbs=config.cbs,
rbs=config.rbs,
runtime=ctf_mode.runtime,
task=ctf_mode.task,
dead=ctf_mode.dead,
cpusPerTask=config.cpusPerTask,
)
df$cbs = as.factor(df$cbs)
df$rbs = as.factor(df$rbs)
# Normalize the time by the median
df = df %>%
mutate(runtime = runtime * 1e-9 / cpusPerTask) %>%
mutate(dead = dead * 1e-9 / cpusPerTask) %>%
mutate(task = task * 1e-9 / cpusPerTask) %>%
group_by(cbs, rbs) %>%
mutate(median.time = median(time)) %>%
mutate(log.median.time = log(median.time)) %>%
mutate(median.dead = median(dead)) %>%
mutate(median.runtime = median(runtime)) %>%
mutate(median.task = median(task)) %>%
ungroup() #%>%
print(df)
heatmap_plot = function(df, colname, title) {
p = ggplot(df, aes(x=cbs, y=rbs, fill=!!ensym(colname))) +
geom_raster() +
#scale_fill_gradient(high="black", low="white") +
scale_fill_viridis(option="plasma") +
coord_fixed() +
theme_bw() +
theme(axis.text.x=element_text(angle = -45, hjust = 0)) +
theme(plot.subtitle=element_text(size=8)) +
#guides(fill = guide_colorbar(barwidth=15, title.position="top")) +
guides(fill = guide_colorbar(barwidth=12, title.vjust=0.8)) +
labs(x="cbs", y="rbs",
title=sprintf("Heat granularity: %s", title),
subtitle=input_file) +
theme(legend.position="bottom")
k=1
ggsave(sprintf("%s.png", colname), plot=p, width=4.8*k, height=5*k, dpi=300)
ggsave(sprintf("%s.pdf", colname), plot=p, width=4.8*k, height=5*k, dpi=300)
}
heatmap_plot(df, "median.runtime", "runtime")
heatmap_plot(df, "median.dead", "not used")
heatmap_plot(df, "median.task", "task")
cutlevel = 0.5
# To plot the median.time we crop the larger values:
df_filtered = filter(df, between(median.time,
median(time) - (cutlevel * sd(time)),
median(time) + (cutlevel * sd(time))))
heatmap_plot(df, "median.time", "execution time (seconds)")
heatmap_plot(df, "log.median.time", "execution time")
df_square = filter(df, cbs == rbs) %>%
gather(key = time.from, value = acc.time,
c("median.dead", "median.runtime", "median.task"))
# Colors similar to Paraver
colors <- c("median.dead" = "gray",
"median.runtime" = "blue",
"median.task" = "red")
p = ggplot(df_square, aes(x=cbs, y=acc.time)) +
geom_area(aes(fill=time.from, group=time.from)) +
scale_fill_manual(values = colors) +
geom_point(aes(y=median.time, color="black")) +
geom_line(aes(y=median.time, group=0, color="black")) +
theme_bw() +
theme(legend.position=c(0.5, 0.7)) +
scale_color_identity(breaks = c("black"),
labels = c("Total time"), guide = "legend") +
labs(x="Blocksize (side)", y="Time (s)",
fill="Estimated", color="Direct measurement",
title="Heat granularity: time distribution", subtitle=input_file)
ggsave("area.time.png", plot=p, width=6, height=6, dpi=300)
ggsave("area.time.pdf", plot=p, width=6, height=6, dpi=300)
p = ggplot(df_square, aes(x=cbs, y=acc.time)) +
geom_col(aes(fill=time.from, group=time.from)) +
scale_fill_manual(values = colors) +
theme_bw() +
theme(legend.position=c(0.5, 0.7)) +
labs(x="Blocksize (side)", y="Time (s)",
fill="Estimated", color="Direct measurement",
title="Heat granularity: time distribution", subtitle=input_file)
ggsave("col.time.png", plot=p, width=6, height=6, dpi=300)
ggsave("col.time.pdf", plot=p, width=6, height=6, dpi=300)