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() particles = unique(dataset$config.particles) # We only need the cpu bind, nblocks and time df = select(dataset, config.freeCpu, config.nblocks, config.hw.cpusPerSocket, time) %>% rename(nblocks=config.nblocks, freeCpu=config.freeCpu, cpusPerSocket=config.hw.cpusPerSocket) df = df %>% mutate(blocksPerCpu = nblocks / cpusPerSocket) df$freeCpu = as.factor(df$freeCpu) df$nblocks = as.factor(df$nblocks) df$blocksPerCpuFactor = as.factor(df$blocksPerCpu) # Split by malloc variant D=df %>% group_by(freeCpu, nblocks) %>% mutate(tnorm = time / median(time) - 1) bs_unique = unique(df$nblocks) nbs=length(bs_unique) print(D) ppi=300 h=5 w=5 png("box.png", width=w*ppi, height=h*ppi, res=ppi) # # # # Create the plot with the normalized time vs nblocks p = ggplot(data=D, aes(x=blocksPerCpuFactor, y=tnorm)) + # Labels labs(x="Blocks/CPU", y="Normalized time", title=sprintf("Nbody normalized time. Particles=%d", particles), subtitle=input_file) + # Center the title #theme(plot.title = element_text(hjust = 0.5)) + # Black and white mode (useful for printing) #theme_bw() + # Add the maximum allowed error lines geom_hline(yintercept=c(-0.01, 0.01), linetype="dashed", color="red") + # Draw boxplots geom_boxplot(aes(fill=freeCpu)) + # # Use log2 scale in x # scale_x_continuous(trans=log2_trans(), # breaks=bs_unique) + # scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) + theme_bw() + theme(plot.subtitle=element_text(size=8)) + theme(legend.position = c(0.85, 0.85)) #+ # Place each variant group in one separate plot #facet_wrap(~freeCpu) # Render the plot print(p) ## Save the png image dev.off() # png("scatter.png", width=w*ppi, height=h*ppi, res=ppi) # ## Create the plot with the normalized time vs nblocks p = ggplot(D, aes(x=blocksPerCpuFactor, y=time, color=freeCpu)) + labs(x="Blocks/CPU", y="Time (s)", title=sprintf("Nbody granularity. Particles=%d", particles), subtitle=input_file) + theme_bw() + theme(plot.subtitle=element_text(size=8)) + theme(legend.position = c(0.5, 0.88)) + geom_point(shape=21, size=3) + #scale_x_continuous(trans=log2_trans()) + scale_y_continuous(trans=log2_trans()) # Render the plot print(p) # Save the png image dev.off()