52 lines
1.2 KiB
R
52 lines
1.2 KiB
R
|
library(ggplot2)
|
||
|
|
library(dplyr)
|
||
|
|
library(scales)
|
||
|
|
library(jsonlite)
|
||
|
|
|
||
|
|
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, perf.cache_misses) %>%
|
||
|
|
rename(cbs=config.cbs, rbs=config.rbs)
|
||
|
|
|
||
|
|
df$cbs = as.factor(df$cbs)
|
||
|
|
df$rbs = as.factor(df$rbs)
|
||
|
|
|
||
|
|
# Normalize the time by the median
|
||
|
|
df=group_by(df, cbs, rbs) %>%
|
||
|
|
mutate(median.misses = median(perf.cache_misses)) %>%
|
||
|
|
mutate(log.median.misses = log(median.misses)) %>%
|
||
|
|
ungroup()
|
||
|
|
|
||
|
|
ppi=300
|
||
|
|
h=5
|
||
|
|
w=5
|
||
|
|
|
||
|
|
|
||
|
|
png("heatmap.png", width=1.5*w*ppi, height=h*ppi, res=ppi)
|
||
|
|
#
|
||
|
|
## Create the plot with the normalized time vs nblocks
|
||
|
|
p = ggplot(df, aes(x=cbs, y=rbs, fill=log.median.misses)) +
|
||
|
|
geom_raster() +
|
||
|
|
scale_fill_gradient(high="black", low="white") +
|
||
|
|
coord_fixed() +
|
||
|
|
theme_bw() +
|
||
|
|
theme(plot.subtitle=element_text(size=8)) +
|
||
|
|
labs(x="cbs", y="rbs",
|
||
|
|
title=sprintf("Heat granularity: cache misses"),
|
||
|
|
subtitle=input_file)
|
||
|
|
|
||
|
|
# Render the plot
|
||
|
|
print(p)
|
||
|
|
|
||
|
|
# Save the png image
|
||
|
|
dev.off()
|