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] } if (length(args)>1) { output = args[2] } else { output = "?" } # 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=output) + 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=output) 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=output) ggsave("col.time.png", plot=p, width=6, height=6, dpi=300) ggsave("col.time.pdf", plot=p, width=6, height=6, dpi=300)