163 lines
4.6 KiB
R
163 lines
4.6 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.nbly, config.nodes, time, total_time, config.gitCommit) %>%
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# rename(nbly=config.nbly, nnodes=config.nodes, gitCommit=config.gitCommit)
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df = select(dataset, config.nbly, config.nblz, config.nbltotal, config.nodes, time, total_time) %>%
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rename(nbly=config.nbly, nblz=config.nblz, nbltotal=config.nbltotal, nnodes=config.nodes)
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df2 = df[df$nblz == 1 | df$nblz == 2 | df$nblz == 4, ]
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df3 = df[df$nbly == 1 | df$nbly == 2 | df$nbly == 4, ]
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# df2 data frame
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df2$nblsetZ = as.factor(df2$nblz)
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df2$nblPerProcZ = as.factor(df2$nbltotal / 24)
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df2$nbltotal = as.factor(df2$nbltotal)
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df2$nodes = as.factor(df2$nnodes)
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# df3 data frame
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df3$nblsetY = as.factor(df3$nbly)
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df3$nblPerProcY = as.factor(df3$nbltotal / 24)
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df3$nbltotalY = as.factor(df3$nbltotal)
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df3$nodes = as.factor(df3$nnodes)
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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$nblPerProc = as.factor(df$nbltotal / 24)
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df$nbltotal = as.factor(df$nbltotal)
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df$nodes = as.factor(df$nnodes)
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#df$gitCommit = as.factor(df$gitCommit)
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# Normalize the time by the median
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#D=group_by(df, nbly, nodes, gitCommit) %>%
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D=group_by(df, nbly, nblz, nbltotal, 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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print(D)
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ppi=300
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h=5
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w=8
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png("scatter_nbly.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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## Create the plot with the normalized time vs nblocks
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p = ggplot() +
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geom_point(data=df2, aes(x=nblPerProcZ, y=time, color=nblsetZ), shape=21, size=3, show.legend=TRUE) +
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geom_point(data=df3, aes(x=nblPerProcY, y=time, color=nblsetY), shape=4, size=2, show.legend=TRUE) +
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labs(x="nblPerProc", y="Time (s)",
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title=sprintf("Saiph-Heat3D granularity per nodes"),
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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, 0.5)) +
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scale_y_continuous(trans=log2_trans()) +
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facet_wrap( ~ nodes)
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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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png("scatter_nbly.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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## Create the plot with the normalized time vs nblocks
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p = ggplot() +
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geom_point(data=df2, aes(x=nblPerProcZ, y=time, color=nblsetZ), shape=21, size=3, show.legend=TRUE) +
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geom_point(data=df3, aes(x=nblPerProcY, y=time, color=nblsetY), shape=4, size=2, show.legend=TRUE) +
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labs(x="nblPerProc", y="Time (s)",
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title=sprintf("Saiph-Heat3D granularity per nodes"),
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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, 0.5)) +
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scale_y_continuous(trans=log2_trans()) +
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facet_wrap( ~ nodes)
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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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png("test1.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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## Create the plot with the normalized time vs nblocks
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p = ggplot(D, aes(x=nblPerProc, y=tn)) +
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labs(x="nblPerProc", y="Time (s) * nodes",
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title=sprintf("Saiph-Heat3D granularity per nbly blocks"),
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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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geom_point(shape=21, size=3) +
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geom_line(aes(color=nodes, group=nodes)) +
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#scale_x_continuous(trans=log2_trans()) +
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scale_y_continuous(trans=log2_trans()) +
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facet_wrap( ~ nbly)
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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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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=15, title.position="top")) +
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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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heatmap_plot(D, "tmedian", "time")
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