forked from rarias/bscpkgs
137 lines
4.5 KiB
R
137 lines
4.5 KiB
R
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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if (length(args)>1) { output = args[2] } else { output = "?" }
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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.gitBranch,
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config.granul,
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config.iterations,
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config.sizeFactor,
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config.nz,
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time,
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total_time) %>%
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rename(nodes=config.nodes,
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gitBranch=config.gitBranch,
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granul=config.granul,
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sizeFactor=config.sizeFactor,
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nz=config.nz,
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iterations=config.iterations) %>%
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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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mutate(time.elem = time / sizeFactor) %>%
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mutate(time.nodes.iter = time.nodes / iterations) %>%
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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(granul = as.factor(granul)) %>%
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mutate(iterations = as.factor(iterations)) %>%
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mutate(sizeFactor = as.factor(sizeFactor)) %>%
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mutate(nz = as.factor(nz)) %>%
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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(median.time.elem = median(time.elem)) %>%
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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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mutate(log.median.time.elem = log(median.time.elem)) %>%
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mutate(median.time.nodes.iter = median(time.nodes.iter)) %>%
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ungroup() %>%
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group_by(sizeFactor) %>%
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mutate(optimal.granul = (median.time.elem == min(median.time.elem))) %>%
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ungroup()
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dfopt = df %>% filter(optimal.granul == TRUE)
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dpi = 300
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h = 4
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w = 10
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# ---------------------------------------------------------------------
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#p = ggplot(df, aes(x=sizeFactor, y=normalized.time, fill=granul, color=iterations)) +
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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(x="nodes", y="Normalized time",
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# title="Creams strong scaling: normalized time",
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# subtitle=output) +
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# theme(plot.subtitle=element_text(size=8))
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#
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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=granul, y=time.elem, color=branch)) +
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geom_point(shape=21, size=3) +
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# geom_line(aes(y=median.time, group=gitBranch)) +
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theme_bw() +
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facet_wrap(sizeFactor ~ ., labeller=label_both, nrow=1) +
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labs(x="Granularity", y="Time / k (s)",
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#title="Creams size: time per object",
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subtitle=output) +
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theme(plot.subtitle=element_text(size=8, family="mono"),
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legend.position="bottom")
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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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# ---------------------------------------------------------------------
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p = ggplot(df, aes(x=granul, y=median.time.elem, color=sizeFactor)) +
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geom_line(aes(group=sizeFactor)) +
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geom_point(data=dfopt, aes(x=granul, y=median.time.elem)) +
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theme_bw() +
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labs(x="Granularity", y="Time / k (s)",
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color="Size factor k",
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subtitle=output) +
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theme(plot.subtitle=element_text(size=8, family="mono"),
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legend.position="bottom")
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ggsave("median.time.png", plot=p, width=5, height=5, dpi=dpi)
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ggsave("median.time.pdf", plot=p, width=5, height=5, dpi=dpi)
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# ---------------------------------------------------------------------
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p = ggplot(df, aes(x=granul, y=sizeFactor, fill=log.median.time.elem)) +
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geom_raster() +
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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="Granularity", y="Size factor", fill="Time / k (s)", subtitle=output) +
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theme(plot.subtitle=element_text(size=8, family="mono"),
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legend.position="bottom")
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k=1
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ggsave("heatmap.png", plot=p, width=4.8*k, height=5*k, dpi=300)
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ggsave("heatmap.pdf", plot=p, width=4.8*k, height=5*k, dpi=300)
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