989f6ee018
The previous iniput size for both granularity and strong scaling tests where too big to meet the timing constrains needed for garlic. This patch sets a new, smaller, input size. Also, a minor cleanup is applied to the rest of the fwi experiments and figures.
121 lines
3.3 KiB
R
121 lines
3.3 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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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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# Select block size to display
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useBlocksize = 2
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# We only need the nblocks and time
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df = select(dataset, config.blocksize, config.gitBranch, config.nodes, time) %>%
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rename(
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blocksize=config.blocksize,
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gitBranch=config.gitBranch,
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nodes=config.nodes
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) %>%
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filter(blocksize == useBlocksize | blocksize == 0) %>%
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group_by(nodes, gitBranch) %>%
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mutate(mtime = median(time)) %>%
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mutate(nxmtime = mtime * nodes) %>%
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mutate(nxtime = time * nodes) %>%
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ungroup()
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df$gitBranch = as.factor(df$gitBranch)
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df$blocksize = as.factor(df$blocksize)
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df$nodes = as.factor(df$nodes)
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ppi=300
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h=5
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w=5
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####################################################################
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### Line plot (time)
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####################################################################
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png("time.png", width=w*ppi, height=h*ppi, res=ppi)
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p = ggplot(df, aes(x=nodes, y=time, group=gitBranch, color=gitBranch)) +
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geom_point() +
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geom_line() +
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theme_bw() +
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labs(x="Nodes", y="Time (s)", title="FWI strong scaling",
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subtitle=input_file) +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = c(0.6, 0.75))
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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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### Line plot (time x nodes)
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####################################################################
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png("nxtime.png", width=w*ppi, height=h*ppi, res=ppi)
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p = ggplot(df, aes(x=nodes, y=nxtime, group=gitBranch, color=gitBranch)) +
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geom_point() +
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geom_line() +
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theme_bw() +
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labs(x="Nodes", y="Time * Nodes (s)", title="FWI strong scaling",
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subtitle=input_file) +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = c(0.15, 0.80)) +
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theme(legend.text = element_text(size = 7))
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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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### Line plot (median time)
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####################################################################
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png("mediantime.png", width=w*ppi, height=h*ppi, res=ppi)
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p = ggplot(df, aes(x=nodes, y=mtime, group=gitBranch, color=gitBranch)) +
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geom_point() +
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geom_line() +
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theme_bw() +
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labs(x="Nodes", y="Median Time (s)", title="FWI strong scaling",
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subtitle=input_file) +
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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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# 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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### Line plot (nodes x median time)
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####################################################################
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png("nxmtime.png", width=w*ppi, height=h*ppi, res=ppi)
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p = ggplot(df, aes(x=nodes, y=nxmtime, group=gitBranch, color=gitBranch)) +
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geom_point() +
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geom_line() +
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theme_bw() +
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labs(x="Nodes", y="Median Time * Nodes (s)", title="FWI strong scaling",
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subtitle=input_file) +
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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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# 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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