library(ggplot2) library(dplyr) library(scales) library(jsonlite) library(egg) 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$config.particles) # We only need the nblocks and time df = select(dataset, config.nblocks, config.hw.cpusPerSocket, config.nodes, config.blocksize, config.particles, config.gitBranch, time) %>% rename(nblocks=config.nblocks, nodes=config.nodes, blocksize=config.blocksize, particles=config.particles, gitBranch=config.gitBranch, cpusPerSocket=config.hw.cpusPerSocket) df = df %>% mutate(blocksPerCpu = nblocks / cpusPerSocket) df$nblocks = as.factor(df$nblocks) df$nodesFactor = as.factor(df$nodes) df$blocksPerCpuFactor = as.factor(df$blocksPerCpu) df$blocksizeFactor = as.factor(df$blocksize) df$particlesFactor = as.factor(df$particles) df$gitBranch = as.factor(df$gitBranch) # Normalize the time by the median D=group_by(df, nblocks, nodesFactor, gitBranch) %>% mutate(tmedian = median(time)) %>% mutate(tnorm = time / median(time) - 1) %>% mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0))) %>% ungroup() %>% group_by(nodesFactor, gitBranch) %>% mutate(tmedian_min = min(tmedian)) %>% ungroup() %>% group_by(gitBranch) %>% mutate(tmin_max = max(tmedian_min)) %>% mutate(tideal = tmin_max / nodes) %>% ungroup() D$bad = as.factor(D$bad) #D$bad = as.factor(ifelse(abs(D$tnorm) >= 0.01, 2, # ifelse(abs(D$tnorm) >= 0.005, 1, 0))) 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, color=bad)) + # 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="gray") + # Draw boxplots geom_boxplot(aes(fill=nodesFactor)) + scale_color_manual(values=c("black", "brown")) + facet_grid(gitBranch ~ .) + #scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) + #theme(legend.position = "none") #theme(legend.position = c(0.85, 0.85)) theme_bw()+ theme(plot.subtitle=element_text(size=8)) # Render the plot print(p) dev.off() p1 = ggplot(D, aes(x=blocksizeFactor, y=tmedian)) + labs(x="Blocksize", 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, 0.8)) + geom_line(aes(y=tmedian, group=interaction(gitBranch, nodesFactor), color=nodesFactor)) + geom_point(aes(color=nodesFactor), size=3) + facet_grid(gitBranch ~ .) + scale_shape_manual(values=c(21, 22)) + scale_y_continuous(trans=log2_trans()) png("time-blocksize.png", width=1.5*w*ppi, height=1.5*h*ppi, res=ppi) print(p1) dev.off() p2 = ggplot(D, aes(x=blocksPerCpuFactor, y=tmedian)) + 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)) + geom_line(aes(y=tmedian, group=interaction(gitBranch, nodesFactor), color=nodesFactor)) + geom_point(aes(color=nodesFactor), size=3) + facet_grid(gitBranch ~ .) + scale_shape_manual(values=c(21, 22)) + scale_y_continuous(trans=log2_trans()) png("time-blocks-per-cpu.png", width=1.5*w*ppi, height=1.5*h*ppi, res=ppi) print(p2) dev.off() #p = ggarrange(p1, p2, ncol=2) #png("time-gra.png", width=2*w*ppi, height=h*ppi, res=ppi) #print(p) #dev.off() png("exp-space.png", width=w*ppi, height=h*ppi, res=ppi) p = ggplot(data=df, aes(x=nodesFactor, y=particlesFactor)) + labs(x="Nodes", y="Particles", title="Nbody: Experiment space") + geom_line(aes(group=particles)) + geom_point(aes(color=nodesFactor), size=3) + facet_grid(gitBranch ~ .) + theme_bw() print(p) dev.off() png("gra-space.png", width=w*ppi, height=h*ppi, res=ppi) p = ggplot(data=D, aes(x=nodesFactor, y=blocksPerCpuFactor)) + labs(x="Nodes", y="Blocks/CPU", title="Nbody: Granularity space") + geom_line(aes(group=nodesFactor)) + geom_point(aes(color=nodesFactor), size=3) + facet_grid(gitBranch ~ .) + theme_bw() print(p) dev.off() png("performance.png", width=1.5*w*ppi, height=1.5*h*ppi, res=ppi) p = ggplot(D, aes(x=nodesFactor)) + labs(x="Nodes", y="Time (s)", title="Nbody strong scaling") + theme_bw() + geom_line(aes(y=tmedian, linetype=blocksPerCpuFactor, group=interaction(gitBranch, blocksPerCpuFactor))) + geom_line(aes(y=tideal, group=gitBranch), color="red") + geom_point(aes(y=tmedian, color=nodesFactor), size=3) + facet_grid(gitBranch ~ .) + scale_shape_manual(values=c(21, 22)) + scale_y_continuous(trans=log2_trans()) print(p) dev.off()