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fwi: adjust input size to meet timing constraints

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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.
This commit is contained in:
Aleix Roca Nonell
2021-04-07 12:35:44 +02:00
parent 3e5a56ebdb
commit 989f6ee018
14 changed files with 96 additions and 267 deletions
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31
garlic/fig/fwi/granularity.R
View File

@@ -30,7 +30,7 @@ w=5
####################################################################
### Line Graph
####################################################################
png("time.png", width=w*ppi, height=h*ppi, res=ppi)
png("mtime.png", width=w*ppi, height=h*ppi, res=ppi)
## Create the plot with the normalized time vs nblocks
p = ggplot(df, aes(x = blocksize, y=mtime, group=gitBranch, color=gitBranch)) +
@@ -49,22 +49,23 @@ print(p)
dev.off()
####################################################################
### Boxplot
### Line Graph
####################################################################
png("box.png", width=w*ppi, height=h*ppi, res=ppi)
# Create the plot with the normalized time vs nblocks
p = ggplot(df, aes(x=blocksize, y=time, group=gitBranch, colour=gitBranch)) +
# Labels
labs(x="Blocksize", y="Normalized time",
title=sprintf("FWI Time"),
subtitle=input_file) +
# Draw boxplots
geom_boxplot() +
theme_bw() +
theme(plot.subtitle=element_text(size=8)) +
theme(legend.position = c(0.5, 0.88))
png("time.png", width=w*ppi, height=h*ppi, res=ppi)
## Create the plot with the normalized time vs nblocks
p = ggplot(df, aes(x = blocksize, y=time, group=gitBranch, color=gitBranch)) +
geom_point() +
geom_line() +
theme_bw() +
labs(x="Blocksize", y="Time (s)", title="FWI granularity",
subtitle=input_file) +
theme(plot.subtitle=element_text(size=8)) +
theme(legend.position = c(0.5, 0.88))
# Render the plot
print(p)
## Save the png image
# Save the png image
dev.off()

4
garlic/fig/fwi/strong_scaling.R
View File

@@ -14,7 +14,7 @@ dataset = jsonlite::stream_in(file(input_file)) %>%
jsonlite::flatten()
# Select block size to display
useBlocksize = 1
useBlocksize = 2
# We only need the nblocks and time
df = select(dataset, config.blocksize, config.gitBranch, config.nodes, time) %>%
@@ -59,7 +59,7 @@ print(p)
dev.off()
####################################################################
### Line plot (timei x nodes)
### Line plot (time x nodes)
####################################################################
png("nxtime.png", width=w*ppi, height=h*ppi, res=ppi)

46
garlic/fig/fwi/test.R
View File

@@ -1,46 +0,0 @@
library(ggplot2)
library(dplyr)
library(scales)
library(jsonlite)
args=commandArgs(trailingOnly=TRUE)
# Read the timetable from args[1]
input_file = "input.json"
if (length(args)>0) { input_file = args[1] }
# Load the dataset in NDJSON format
dataset = jsonlite::stream_in(file(input_file)) %>%
jsonlite::flatten()
# We only need the nblocks and time
df = select(dataset, config.blocksize, config.gitBranch, time) %>%
rename(blocksize=config.blocksize, gitBranch=config.gitBranch) %>%
group_by(blocksize, gitBranch) %>%
mutate(mtime = median(time)) %>%
ungroup()
df$gitBranch = as.factor(df$gitBranch)
df$blocksize = as.factor(df$blocksize)
ppi=300
h=5
w=5
png("time.png", width=w*ppi, height=h*ppi, res=ppi)
#
## Create the plot with the normalized time vs nblocks
p = ggplot(df, aes(x=blocksize, y=time)) +
geom_point() +
geom_line(aes(y=mtime, group=gitBranch, color=gitBranch)) +
theme_bw() +
labs(x="Blocksize", y="Time (s)", title="FWI granularity",
subtitle=input_file) +
theme(plot.subtitle=element_text(size=8)) +
theme(legend.position = c(0.5, 0.88))
# Render the plot
print(p)
# Save the png image
dev.off()

8
garlic/fig/index.nix
View File

@@ -62,10 +62,12 @@ in
};
fwi = with exp.fwi; {
test = stdPlot ./fwi/test.R [ test ];
strong_scaling = stdPlot ./fwi/strong_scaling.R [ strong_scaling_task strong_scaling_forkjoin strong_scaling_mpionly ];
strong_scaling_io = stdPlot ./fwi/strong_scaling_io.R [ strong_scaling_io ];
granularity = stdPlot ./fwi/granularity.R [ granularity ];
strong_scaling = stdPlot ./fwi/strong_scaling.R [ strong_scaling_task strong_scaling_forkjoin ];
#strong_scaling = stdPlot ./fwi/strong_scaling.R [ strong_scaling_task strong_scaling_forkjoin strong_scaling_mpionly ];
data_reuse = stdPlot ./fwi/granularity.R [ data_reuse ];
strong_scaling_io = stdPlot ./fwi/strong_scaling_io.R [ strong_scaling_io ];
sync_io = stdPlot ./fwi/strong_scaling_io.R [ sync_io ];
};
osu = with exp.osu; {