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nbody: plot nb/cpu rather than nb

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This commit is contained in:
Rodrigo Arias 2020-11-02 10:37:22 +01:00
parent 376ab9b32a
commit de46366985
1 changed files with 10 additions and 10 deletions
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20
garlic/fig/nbody/test.R → garlic/fig/nbody/baseline.R
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@ -22,6 +22,7 @@ df = select(dataset, config.nblocks, config.hw.cpusPerSocket, time) %>%
df = df %>% mutate(blocksPerCpu = nblocks / cpusPerSocket)
df$nblocks = as.factor(df$nblocks)
df$blocksPerCpuFactor = as.factor(df$blocksPerCpu)
# Normalize the time by the median
D=group_by(df, nblocks) %>%
@ -41,10 +42,10 @@ png("box.png", width=w*ppi, height=h*ppi, res=ppi)
#
#
# Create the plot with the normalized time vs nblocks
p = ggplot(data=D, aes(x=nblocks, y=tnorm)) +
p = ggplot(data=D, aes(x=blocksPerCpuFactor, y=tnorm)) +
# Labels
labs(x="Blocks", y="Normalized time",
labs(x="Num blocks", y="Normalized time",
title=sprintf("Nbody normalized time. Particles=%d", particles),
subtitle=input_file) +
@ -55,8 +56,8 @@ p = ggplot(data=D, aes(x=nblocks, y=tnorm)) +
#theme_bw() +
# Add the maximum allowed error lines
#geom_hline(yintercept=c(-0.01, 0.01),
# linetype="dashed", color="red") +
geom_hline(yintercept=c(-0.01, 0.01),
linetype="dashed", color="red") +
# Draw boxplots
geom_boxplot() +
@ -69,8 +70,6 @@ p = ggplot(data=D, aes(x=nblocks, y=tnorm)) +
theme(legend.position = c(0.85, 0.85)) #+
# Place each variant group in one separate plot
#facet_wrap(~jemalloc)
@ -83,17 +82,18 @@ dev.off()
png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
#
## Create the plot with the normalized time vs nblocks
p = ggplot(D, aes(x=nblocks, y=time)) +
p = ggplot(D, aes(x=blocksPerCpuFactor, y=time)) +
labs(x="Blocks", y="Time (s)",
labs(x="Blocks/CPU", y="Time (s)",
title=sprintf("Nbody granularity. Particles=%d", particles),
subtitle=input_file) +
theme_bw() +
theme(plot.subtitle=element_text(size=8)) +
theme(legend.position = c(0.5, 0.88)) +
geom_point(shape=21, size=3) # +
#scale_y_continuous(trans=log2_trans())
geom_point(shape=21, size=3) +
#scale_x_continuous(trans=log2_trans()) +
scale_y_continuous(trans=log2_trans())
# Render the plot
print(p)