forked from rarias/jungle
78 lines
2.7 KiB
R
78 lines
2.7 KiB
R
library(ggplot2)
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library(dplyr)
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library(scales)
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library(jsonlite)
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library(viridis)
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args=commandArgs(trailingOnly=TRUE)
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# Read the timetable from args[1]
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input_file = "input.json"
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if (length(args)>0) { input_file = args[1] }
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# Load the dataset in NDJSON format
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dataset = jsonlite::stream_in(file(input_file)) %>%
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jsonlite::flatten()
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# We only need the nblocks and time
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df = select(dataset, config.cbs, config.rbs, perf.cache_misses, perf.instructions, perf.cycles, time) %>%
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rename(cbs=config.cbs, rbs=config.rbs)
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df$cbs = as.factor(df$cbs)
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df$rbs = as.factor(df$rbs)
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# Normalize the time by the median
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df=group_by(df, cbs, rbs) %>%
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mutate(median.time = median(time)) %>%
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mutate(log.median.time = log(median.time)) %>%
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mutate(median.misses = median(perf.cache_misses)) %>%
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mutate(log.median.misses = log(median.misses)) %>%
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mutate(median.instr= median(perf.instructions)) %>%
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mutate(log.median.instr= log(median.instr)) %>%
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mutate(median.cycles = median(perf.cycles)) %>%
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mutate(median.cpi = median.cycles / median.instr) %>%
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mutate(median.ipc = median.instr / median.cycles) %>%
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mutate(median.ips = median.instr / median.time) %>%
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mutate(median.cps = median.cycles / median.time) %>%
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ungroup()# %>%
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heatmap_plot = function(df, colname, title) {
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p = ggplot(df, aes(x=cbs, y=rbs, fill=!!ensym(colname))) +
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geom_raster() +
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#scale_fill_gradient(high="black", low="white") +
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scale_fill_viridis(option="plasma") +
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coord_fixed() +
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theme_bw() +
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theme(axis.text.x=element_text(angle = -45, hjust = 0)) +
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theme(plot.subtitle=element_text(size=8)) +
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#guides(fill = guide_colorbar(barwidth=15, title.position="top")) +
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guides(fill = guide_colorbar(barwidth=12, title.vjust=0.8)) +
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labs(x="cbs", y="rbs",
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title=sprintf("Heat granularity: %s", title),
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subtitle=input_file) +
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theme(legend.position="bottom")
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k=1
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ggsave(sprintf("%s.png", colname), plot=p, width=4.8*k, height=5*k, dpi=300)
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ggsave(sprintf("%s.pdf", colname), plot=p, width=4.8*k, height=5*k, dpi=300)
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}
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heatmap_plot(df, "median.misses", "cache misses")
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heatmap_plot(df, "log.median.misses", "cache misses")
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heatmap_plot(df, "median.instr", "instructions")
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heatmap_plot(df, "log.median.instr", "instructions")
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heatmap_plot(df, "median.cycles", "cycles")
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heatmap_plot(df, "median.ipc", "IPC")
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heatmap_plot(df, "median.cpi", "cycles/instruction")
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heatmap_plot(df, "median.ips", "instructions/second")
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heatmap_plot(df, "median.cps", "cycles/second")
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cutlevel = 0.5
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# To plot the median.time we crop the larger values:
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df_filtered = filter(df, between(median.time,
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median(time) - (cutlevel * sd(time)),
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median(time) + (cutlevel * sd(time))))
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heatmap_plot(df_filtered, "median.time", "execution time (seconds)")
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heatmap_plot(df_filtered, "log.median.time", "execution time")
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