Aleix Roca Nonell
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.
72 lines
1.9 KiB
R
72 lines
1.9 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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# We only need the nblocks and time
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df = select(dataset, config.blocksize, config.gitBranch, time) %>%
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rename(blocksize=config.blocksize, gitBranch=config.gitBranch) %>%
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group_by(blocksize, gitBranch) %>%
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mutate(mtime = median(time)) %>%
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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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ppi=300
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h=5
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w=5
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####################################################################
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### Line Graph
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####################################################################
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png("mtime.png", width=w*ppi, height=h*ppi, res=ppi)
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## Create the plot with the normalized time vs nblocks
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p = ggplot(df, aes(x = blocksize, 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="Blocksize", y="Median Time (s)", title="FWI granularity",
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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 Graph
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####################################################################
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png("time.png", width=w*ppi, height=h*ppi, res=ppi)
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## Create the plot with the normalized time vs nblocks
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p = ggplot(df, aes(x = blocksize, 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="Blocksize", y="Time (s)", title="FWI granularity",
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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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