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heat: add bar plot with time distribution

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This commit is contained in:
Rodrigo Arias 2021-03-18 20:08:24 +01:00
parent f8122f3c8b
commit 0b7e92b6f9
1 changed files with 44 additions and 4 deletions
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48
garlic/fig/heat/mode.R
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@ -3,6 +3,7 @@ library(dplyr)
library(scales) library(scales)
library(jsonlite) library(jsonlite)
library(viridis) library(viridis)
library(tidyr)
args=commandArgs(trailingOnly=TRUE) args=commandArgs(trailingOnly=TRUE)
@ -19,6 +20,7 @@ df = select(dataset, config.cbs, config.rbs,
ctf_mode.runtime, ctf_mode.runtime,
ctf_mode.task, ctf_mode.task,
ctf_mode.dead, ctf_mode.dead,
config.cpusPerTask,
time) %>% time) %>%
rename( rename(
cbs=config.cbs, cbs=config.cbs,
@ -26,6 +28,7 @@ df = select(dataset, config.cbs, config.rbs,
runtime=ctf_mode.runtime, runtime=ctf_mode.runtime,
task=ctf_mode.task, task=ctf_mode.task,
dead=ctf_mode.dead, dead=ctf_mode.dead,
cpusPerTask=config.cpusPerTask,
) )
df$cbs = as.factor(df$cbs) df$cbs = as.factor(df$cbs)
@ -33,16 +36,16 @@ df$rbs = as.factor(df$rbs)
# Normalize the time by the median # Normalize the time by the median
df = df %>% df = df %>%
mutate(runtime = runtime * 1e-9) %>% mutate(runtime = runtime * 1e-9 / cpusPerTask) %>%
mutate(dead = dead * 1e-9) %>% mutate(dead = dead * 1e-9 / cpusPerTask) %>%
mutate(task = task * 1e-9) %>% mutate(task = task * 1e-9 / cpusPerTask) %>%
group_by(cbs, rbs) %>% group_by(cbs, rbs) %>%
mutate(median.time = median(time)) %>% mutate(median.time = median(time)) %>%
mutate(log.median.time = log(median.time)) %>% mutate(log.median.time = log(median.time)) %>%
mutate(median.dead = median(dead)) %>% mutate(median.dead = median(dead)) %>%
mutate(median.runtime = median(runtime)) %>% mutate(median.runtime = median(runtime)) %>%
mutate(median.task = median(task)) %>% mutate(median.task = median(task)) %>%
ungroup()# %>% ungroup() #%>%
print(df) print(df)
@ -79,3 +82,40 @@ df_filtered = filter(df, between(median.time,
heatmap_plot(df, "median.time", "execution time (seconds)") heatmap_plot(df, "median.time", "execution time (seconds)")
heatmap_plot(df, "log.median.time", "execution time") 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)