saiph: simplify and update figure scripts
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@ -42,8 +42,8 @@ in
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};
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saiph = with exp.saiph; {
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granularity-saiph = stdPlot ./saiph/granularity-saiph.R [ granularity-saiph ];
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scalability-saiph = stdPlot ./saiph/scalability-saiph.R [ scalability-saiph ];
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granularity = stdPlot ./saiph/granularity.R [ granularity ];
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ss = stdPlot ./saiph/ss.R [ ss ];
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};
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heat = with exp.heat; {
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@ -1,155 +0,0 @@
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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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# Create a data frame selecting the desired variables from the data set
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df = select(dataset, config.nbly, config.nblz, config.nodes, time, total_time) %>%
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rename(nbly=config.nbly, nblz=config.nblz, nnodes=config.nodes)
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# Declare variables as factors
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# --> R does not allow to operate with factors: operate before casting to factors
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df$nblPerProc = as.factor(round((df$nbly * df$nblz) / 24, digits = 2))
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df$biggernbly = as.factor(df$nbly > df$nblz)
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df$nbly = as.factor(df$nbly)
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df$nblz = as.factor(df$nblz)
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df$nodes = as.factor(df$nnodes)
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# Create a new data frame including statistics
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D=group_by(df, nbly, nblz, nblPerProc, nodes) %>%
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mutate(tmedian = median(time)) %>%
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mutate(ttmedian = median(total_time)) %>%
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mutate(tnorm = time / tmedian - 1) %>%
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mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0))) %>%
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mutate(tn = tmedian * nnodes) %>%
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ungroup()
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D$bad = as.factor(D$bad)
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### Std output data frame D
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print(D)
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### Output figure size
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ppi=300
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h=5
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w=8
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####################################################################
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### Boxplot
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####################################################################
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png("box.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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#
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# Create the plot with the normalized time vs nblocks
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p = ggplot(data=D, aes(x=nblPerProc, y=tnorm, color=bad)) +
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# Labels
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labs(x="nblPerProc", y="Normalized time",
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title=sprintf("Saiph-Heat3D normalized time"),
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subtitle=input_file) +
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# Add the maximum allowed error lines
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geom_hline(yintercept=c(-0.01, 0.01),
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linetype="dashed", color="gray") +
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# Draw boxplots
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geom_boxplot() +
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scale_color_manual(values=c("black", "brown")) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = "none")
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# Render the plot
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print(p)
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## Save the png image
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dev.off()
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####################################################################
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### XY Scatter plot - measured_time & total_time vs tasks per cpu
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####################################################################
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####################################################################
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### XY Scatter plot - time vs tasks per cpu
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####################################################################
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png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
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## Create the plot with the normalized time vs nblocks per proc
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p = ggplot(D, aes(x=nblPerProc, y=time)) +
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labs(x="nblPerProc", y="Time (s)",
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title=sprintf("Saiph-Heat3D granularity"),
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subtitle=input_file) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = c(0.5, 0.88)) +
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geom_point(shape=21, size=3) +
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scale_y_continuous(trans=log2_trans())
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# Render the plot
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print(p)
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## Save the png image
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dev.off()
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####################################################################
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### XY Scatter plot - median time vs tasks per cpu
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####################################################################
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png("scatter2.png", width=w*ppi, height=h*ppi, res=ppi)
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## Create the plot with the normalized time vs nblocks per proc
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p = ggplot(D, aes(x=nblPerProc, y=tmedian)) +
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labs(x="nblPerProc", y="Median Time (s)",
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title=sprintf("Saiph-Heat3D granularity"),
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subtitle=input_file) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = c(0.5, 0.88)) +
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geom_point(aes(color=biggernbly), shape=21, size=3) +
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labs(color = "nbly > nblz")
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scale_y_continuous(trans=log2_trans())
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# Render the plot
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print(p)
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# Save the png image
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dev.off()
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####################################################################
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### Heatmap plot - median time vs tasks per cpu per dimension
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####################################################################
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heatmap_plot = function(df, colname, title) {
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p = ggplot(df, aes(x=nbly, y=nblz, 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=12, title.vjust=0.8)) +
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labs(x="nbly", y="nblz",
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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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facet_wrap( ~ nodes)
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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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# call heatmap function with colname and legend title
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heatmap_plot(D, "tmedian", "time")
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94
garlic/fig/saiph/granularity.R
Normal file
94
garlic/fig/saiph/granularity.R
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@ -0,0 +1,94 @@
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library(ggplot2)
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library(dplyr, warn.conflicts = FALSE)
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library(scales)
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library(jsonlite)
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library(viridis, warn.conflicts = FALSE)
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library(stringr)
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args = commandArgs(trailingOnly=TRUE)
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# Set the input dataset if given in argv[1], or use "input" as default
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if (length(args)>0) { input_file = args[1] } else { input_file = "input" }
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df = jsonlite::stream_in(file(input_file), verbose=FALSE) %>%
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jsonlite::flatten() %>%
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select(unit,
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config.nodes,
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config.nblx,
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config.nbly,
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config.nblz,
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config.gitBranch,
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config.blocksPerCpu,
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config.sizex,
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time,
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total_time) %>%
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rename(nodes=config.nodes,
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nblx=config.nblx,
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nbly=config.nbly,
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nblz=config.nblz,
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gitBranch=config.gitBranch,
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blocksPerCpu=config.blocksPerCpu,
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sizex=config.sizex) %>%
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# Remove the "garlic/" prefix from the gitBranch
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mutate(branch = str_replace(gitBranch, "garlic/", "")) %>%
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# Computations before converting to factor
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mutate(time.nodes = time * nodes) %>%
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# Convert to factors
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mutate(unit = as.factor(unit)) %>%
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mutate(nodes = as.factor(nodes)) %>%
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mutate(gitBranch = as.factor(gitBranch)) %>%
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mutate(nblx = as.factor(nblx)) %>%
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mutate(nbly = as.factor(nbly)) %>%
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mutate(nblz = as.factor(nblz)) %>%
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mutate(sizex = as.factor(sizex)) %>%
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mutate(unit = as.factor(unit)) %>%
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# Compute median times
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group_by(unit) %>%
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mutate(median.time = median(time)) %>%
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mutate(median.time.nodes = median(time.nodes)) %>%
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mutate(normalized.time = time / median.time - 1) %>%
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mutate(log.median.time = log(median.time)) %>%
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ungroup()
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dpi = 300
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h = 5
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w = 8
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maintitle = "Saiph-Heat3D granularity"
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# ---------------------------------------------------------------------
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p = ggplot(df, aes(x=nbly, y=normalized.time, fill=sizex)) +
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geom_boxplot() +
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geom_hline(yintercept=c(-0.01, 0.01), linetype="dashed", color="red") +
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theme_bw() +
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facet_wrap(branch ~ .) +
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labs(y="Normalized time",
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title=sprintf("%s: normalized time", maintitle),
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subtitle=input_file) +
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theme(plot.subtitle=element_text(size=8))
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ggsave("normalized.time.png", plot=p, width=w, height=h, dpi=dpi)
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ggsave("normalized.time.pdf", plot=p, width=w, height=h, dpi=dpi)
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# ---------------------------------------------------------------------
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p = ggplot(df, aes(x=blocksPerCpu, y=time, color=sizex)) +
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geom_point(shape=21, size=3) +
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geom_line(aes(y=median.time, group=sizex)) +
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theme_bw() +
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scale_x_continuous(trans=log2_trans()) +
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labs(y="Time (s)",
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title=sprintf("%s: time", maintitle),
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subtitle=input_file) +
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theme(plot.subtitle=element_text(size=8))
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ggsave("time.png", plot=p, width=w, height=h, dpi=dpi)
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ggsave("time.pdf", plot=p, width=w, height=h, dpi=dpi)
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@ -1,156 +0,0 @@
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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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# Create a data frame selecting the desired variables from the data set
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df = select(dataset, config.nbly, config.nblz, config.nodes, time, total_time) %>%
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rename(nbly=config.nbly, nblz=config.nblz, nnodes=config.nodes)
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# Declare variables as factors
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# --> R does not allow to operate with factors: operate before casting to factors
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df$nblPerProc = as.factor(round((df$nbly * df$nblz) / 24, digits = 2))
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df$biggernbly = as.factor(df$nbly > df$nblz)
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df$nbly = as.factor(df$nbly)
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df$nblz = as.factor(df$nblz)
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df$nodes = as.factor(df$nnodes)
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# Create a new data frame including statistics
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D=group_by(df, nbly, nblz, nblPerProc, nodes) %>%
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mutate(tmedian = median(time)) %>%
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mutate(ttmedian = median(total_time)) %>%
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mutate(tnorm = time / tmedian - 1) %>%
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mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0))) %>%
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mutate(tn = tmedian * nnodes) %>%
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ungroup()
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D$bad = as.factor(D$bad)
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### Std output data frame D
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print(D)
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### Output figure size
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ppi=300
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h=5
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w=8
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####################################################################
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### Boxplot
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####################################################################
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png("box.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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#
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# Create the plot with the normalized time vs nblocks
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p = ggplot(data=D, aes(x=nblPerProc, y=tnorm, color=bad)) +
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# Labels
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labs(x="nblPerProc", y="Normalized time",
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title=sprintf("Saiph-Heat3D normalized time"),
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subtitle=input_file) +
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# Add the maximum allowed error lines
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geom_hline(yintercept=c(-0.01, 0.01),
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linetype="dashed", color="gray") +
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# Draw boxplots
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geom_boxplot() +
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scale_color_manual(values=c("black", "brown")) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = "none")
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# Render the plot
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print(p)
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## Save the png image
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dev.off()
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####################################################################
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### XY Scatter plot - measured_time & total_time vs tasks per cpu
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####################################################################
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####################################################################
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### XY Scatter plot - time vs tasks per cpu
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####################################################################
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png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
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## Create the plot with the normalized time vs nblocks per proc
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p = ggplot(D, aes(x=nblPerProc, y=time)) +
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labs(x="nblPerProc", y="Time (s)",
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title=sprintf("Saiph-Heat3D granularity"),
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subtitle=input_file) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = c(0.5, 0.88)) +
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geom_point(aes(color=nodes), shape=21, size=3) +
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scale_y_continuous(trans=log2_trans())
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# Render the plot
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print(p)
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## Save the png image
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dev.off()
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####################################################################
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### XY Scatter plot - median time vs tasks per cpu
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####################################################################
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png("scatter2.png", width=w*ppi, height=h*ppi, res=ppi)
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## Create the plot with the normalized time vs nblocks per proc
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p = ggplot(D, aes(x=nblPerProc, y=tn)) +
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labs(x="nblPerProc", y="Median Time (s) * nodes",
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title=sprintf("Saiph-Heat3D granularity"),
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subtitle=input_file) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = c(0.5, 0.88)) +
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geom_point(aes(color=nodes), shape=21, size=3) +
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labs(color = "nbly > nblz")
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scale_y_continuous(trans=log2_trans())
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# Render the plot
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print(p)
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# Save the png image
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dev.off()
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####################################################################
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### Heatmap plot - median time vs tasks per cpu per dimension
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####################################################################
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heatmap_plot = function(df, colname, title) {
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p = ggplot(df, aes(x=nbly, y=nblz, 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=12, title.vjust=0.8)) +
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labs(x="nbly", y="nblz",
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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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facet_wrap( ~ nodes)
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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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# call heatmap function with colname and legend title
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heatmap_plot(D, "tmedian", "time")
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109
garlic/fig/saiph/ss.R
Normal file
109
garlic/fig/saiph/ss.R
Normal file
@ -0,0 +1,109 @@
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library(ggplot2)
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library(dplyr, warn.conflicts = FALSE)
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library(scales)
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library(jsonlite)
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library(viridis, warn.conflicts = FALSE)
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library(stringr)
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args = commandArgs(trailingOnly=TRUE)
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# Set the input dataset if given in argv[1], or use "input" as default
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if (length(args)>0) { input_file = args[1] } else { input_file = "input" }
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df = jsonlite::stream_in(file(input_file), verbose=FALSE) %>%
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jsonlite::flatten() %>%
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select(unit,
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config.nodes,
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config.nblx,
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config.nbly,
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config.nblz,
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config.gitBranch,
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config.blocksPerCpu,
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config.sizex,
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time,
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total_time) %>%
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rename(nodes=config.nodes,
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nblx=config.nblx,
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nbly=config.nbly,
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nblz=config.nblz,
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gitBranch=config.gitBranch,
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blocksPerCpu=config.blocksPerCpu,
|
||||
sizex=config.sizex) %>%
|
||||
|
||||
# Remove the "garlic/" prefix from the gitBranch
|
||||
mutate(branch = str_replace(gitBranch, "garlic/", "")) %>%
|
||||
|
||||
# Computations before converting to factor
|
||||
mutate(time.nodes = time * nodes) %>%
|
||||
|
||||
# Convert to factors
|
||||
mutate(unit = as.factor(unit)) %>%
|
||||
mutate(nodes = as.factor(nodes)) %>%
|
||||
mutate(gitBranch = as.factor(gitBranch)) %>%
|
||||
mutate(nblx = as.factor(nblx)) %>%
|
||||
mutate(nbly = as.factor(nbly)) %>%
|
||||
mutate(nblz = as.factor(nblz)) %>%
|
||||
mutate(sizex = as.factor(sizex)) %>%
|
||||
mutate(unit = as.factor(unit)) %>%
|
||||
|
||||
# Compute median times
|
||||
group_by(unit) %>%
|
||||
mutate(median.time = median(time)) %>%
|
||||
mutate(median.time.nodes = median(time.nodes)) %>%
|
||||
mutate(normalized.time = time / median.time - 1) %>%
|
||||
mutate(log.median.time = log(median.time)) %>%
|
||||
ungroup()
|
||||
|
||||
dpi = 300
|
||||
h = 5
|
||||
w = 8
|
||||
|
||||
maintitle = "Saiph-Heat3D strong scaling"
|
||||
|
||||
# ---------------------------------------------------------------------
|
||||
|
||||
p = ggplot(df, aes(x=nodes, y=normalized.time, fill=sizex)) +
|
||||
geom_boxplot() +
|
||||
geom_hline(yintercept=c(-0.01, 0.01), linetype="dashed", color="red") +
|
||||
theme_bw() +
|
||||
facet_wrap(branch ~ .) +
|
||||
labs(x="nodes", y="Normalized time",
|
||||
title=sprintf("%s: normalized time", maintitle),
|
||||
subtitle=input_file) +
|
||||
theme(plot.subtitle=element_text(size=8))
|
||||
|
||||
ggsave("normalized.time.png", plot=p, width=w, height=h, dpi=dpi)
|
||||
ggsave("normalized.time.pdf", plot=p, width=w, height=h, dpi=dpi)
|
||||
|
||||
# ---------------------------------------------------------------------
|
||||
|
||||
p = ggplot(df, aes(x=nodes, y=time, color=sizex)) +
|
||||
geom_point(shape=21, size=3) +
|
||||
geom_line(aes(y=median.time, group=sizex)) +
|
||||
theme_bw() +
|
||||
# facet_wrap(branch ~ .) +
|
||||
labs(x="nodes", y="Time (s)",
|
||||
title=sprintf("%s: time", maintitle),
|
||||
subtitle=input_file) +
|
||||
theme(plot.subtitle=element_text(size=8))
|
||||
|
||||
ggsave("time.png", plot=p, width=w, height=h, dpi=dpi)
|
||||
ggsave("time.pdf", plot=p, width=w, height=h, dpi=dpi)
|
||||
|
||||
# ---------------------------------------------------------------------
|
||||
|
||||
p = ggplot(df, aes(x=nodes, y=time.nodes, color=sizex)) +
|
||||
geom_point(shape=21, size=3) +
|
||||
geom_line(aes(y=median.time.nodes, group=sizex)) +
|
||||
theme_bw() +
|
||||
#facet_wrap(branch ~ .) +
|
||||
labs(x="nodes", y="Time * nodes (s)",
|
||||
title=sprintf("%s: time * nodes", maintitle),
|
||||
subtitle=input_file) +
|
||||
theme(plot.subtitle=element_text(size=8))
|
||||
|
||||
ggsave("time.nodes.png", plot=p, width=w, height=h, dpi=dpi)
|
||||
ggsave("time.nodes.pdf", plot=p, width=w, height=h, dpi=dpi)
|
||||
Loading…
Reference in New Issue
Block a user