78 lines
2.7 KiB
R
78 lines
2.7 KiB
R
library(ggplot2)
|
|
library(dplyr)
|
|
library(scales)
|
|
library(jsonlite)
|
|
library(viridis)
|
|
|
|
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, perf.instructions, perf.cycles, time) %>%
|
|
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.time = median(time)) %>%
|
|
mutate(log.median.time = log(median.time)) %>%
|
|
mutate(median.misses = median(perf.cache_misses)) %>%
|
|
mutate(log.median.misses = log(median.misses)) %>%
|
|
mutate(median.instr= median(perf.instructions)) %>%
|
|
mutate(log.median.instr= log(median.instr)) %>%
|
|
mutate(median.cycles = median(perf.cycles)) %>%
|
|
mutate(median.cpi = median.cycles / median.instr) %>%
|
|
mutate(median.ipc = median.instr / median.cycles) %>%
|
|
mutate(median.ips = median.instr / median.time) %>%
|
|
mutate(median.cps = median.cycles / median.time) %>%
|
|
ungroup()# %>%
|
|
|
|
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.misses", "cache misses")
|
|
heatmap_plot(df, "log.median.misses", "cache misses")
|
|
heatmap_plot(df, "median.instr", "instructions")
|
|
heatmap_plot(df, "log.median.instr", "instructions")
|
|
heatmap_plot(df, "median.cycles", "cycles")
|
|
heatmap_plot(df, "median.ipc", "IPC")
|
|
heatmap_plot(df, "median.cpi", "cycles/instruction")
|
|
heatmap_plot(df, "median.ips", "instructions/second")
|
|
heatmap_plot(df, "median.cps", "cycles/second")
|
|
|
|
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_filtered, "median.time", "execution time (seconds)")
|
|
heatmap_plot(df_filtered, "log.median.time", "execution time")
|