saiph: add scaling experiment
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@ -34,6 +34,7 @@
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saiph = {
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numcomm = callPackage ./saiph/numcomm.nix { };
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granularity = callPackage ./saiph/granularity.nix { };
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scaling = callPackage ./saiph/scaling.nix { };
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};
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creams = rec {
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73
garlic/exp/saiph/scaling.nix
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73
garlic/exp/saiph/scaling.nix
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@ -0,0 +1,73 @@
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{
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stdenv
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, stdexp
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, bsc
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, targetMachine
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, stages
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}:
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with stdenv.lib;
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let
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# Initial variable configuration
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varConf = with bsc; {
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nb = [ 1 2 4 8 16 32 64 ];
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nodes = [ 1 2 4 ];
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gitCommit = [
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"3ecae7c209ec3e33d1108ae4783d7e733d54f2ca"
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];
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};
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# Generate the complete configuration for each unit
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genConf = with bsc; c: targetMachine.config // rec {
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expName = "saiph";
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unitName = "${expName}-N${toString nodes}" +
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"-nbx${toString nbx}-nby${toString nby}";
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inherit (targetMachine.config) hw;
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# saiph options
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nbx = 1;
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nby = c.nb;
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nbz = c.nb;
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mpi = impi;
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gitBranch = "garlic/tampi+isend+oss+task+simd";
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inherit (c) gitCommit;
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# Repeat the execution of each unit 50 times
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loops = 10;
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# Resources
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qos = "bsc_cs";
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ntasksPerNode = 1;
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nodes = c.nodes;
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cpusPerTask = hw.cpusPerSocket;
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jobName = "${unitName}";
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};
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# Compute the array of configurations
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configs = stdexp.buildConfigs {
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inherit varConf genConf;
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};
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exec = {nextStage, conf, ...}: with conf; stages.exec {
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inherit nextStage;
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env = ''
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export OMP_NUM_THREADS=${toString hw.cpusPerSocket}
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export ASAN_SYMBOLIZER_PATH=${bsc.clangOmpss2Unwrapped}/bin/llvm-symbolizer
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'';
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};
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program = {nextStage, conf, ...}:
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let
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customPkgs = stdexp.replaceMpi conf.mpi;
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in
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customPkgs.apps.saiph.override {
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inherit (conf) nbx nby nbz mpi gitBranch gitCommit;
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};
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pipeline = stdexp.stdPipeline ++ [ exec program ];
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in
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stdexp.genExperiment { inherit configs pipeline; }
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@ -42,7 +42,8 @@ in
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};
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saiph = with exp.saiph; {
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granularity = stdPlot ./saiph/granularity.R [ granularity ];
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granularity = rPlotExp ./saiph/granularity.R [ granularity ];
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scaling = rPlotExp ./saiph/scaling.R [ scaling ];
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};
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heat = with exp.heat; {
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151
garlic/fig/saiph/scaling.R
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151
garlic/fig/saiph/scaling.R
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@ -0,0 +1,151 @@
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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.nby, config.nodes, time, total_time) %>%
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rename(nby=config.nby, nnodes=config.nodes)
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df$nby = as.factor(df$nby)
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df$nodes = as.factor(df$nnodes)
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# Normalize the time by the median
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D=group_by(df, nby, nodes) %>%
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mutate(tmedian = median(time)) %>%
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mutate(ttmedian = median(total_time)) %>%
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mutate(tnorm = time / tmedian - 1) %>%
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mutate(bad = max(ifelse(abs(tnorm) >= 0.01, 1, 0))) %>%
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mutate(tn = tmedian * nnodes) %>%
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ungroup()
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D$bad = as.factor(D$bad)
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print(D)
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ppi=300
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h=5
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w=5
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png("box.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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#
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#
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# Create the plot with the normalized time vs nblocks
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p = ggplot(data=D, aes(x=nby, y=tnorm, color=bad)) +
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# Labels
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labs(x="nby", y="Normalized time",
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title=sprintf("Saiph-Heat3D normalized time"),
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subtitle=input_file) +
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# Center the title
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#theme(plot.title = element_text(hjust = 0.5)) +
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# Black and white mode (useful for printing)
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#theme_bw() +
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# Add the maximum allowed error lines
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geom_hline(yintercept=c(-0.01, 0.01),
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linetype="dashed", color="gray") +
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# Draw boxplots
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geom_boxplot() +
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scale_color_manual(values=c("black", "brown")) +
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#scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = "none")
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#theme(legend.position = c(0.85, 0.85))
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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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png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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## Create the plot with the normalized time vs nblocks
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p = ggplot(D, aes(x=nby, y=time)) +
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labs(x="nby", y="Time (s)",
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title=sprintf("Saiph-Heat3D granularity"),
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subtitle=input_file) +
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theme_bw() +
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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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geom_point(shape=21, size=3) +
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#scale_x_continuous(trans=log2_trans()) +
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scale_y_continuous(trans=log2_trans())
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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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png("wasted.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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## Create the plot with the normalized time vs nblocks
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p = ggplot(D, aes(x=nby, y=time)) +
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labs(x="nby", y="Time (s)",
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title=sprintf("Saiph-Heat3D granularity"),
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subtitle=input_file) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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geom_point(shape=21, size=3) +
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geom_point(aes(y=total_time), shape=1, size=3, color="red") +
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geom_line(aes(y=tmedian, color=nodes, group=nodes)) +
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geom_line(aes(y=ttmedian, color=nodes, group=nodes)) +
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#scale_x_continuous(trans=log2_trans()) +
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scale_y_continuous(trans=log2_trans())
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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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png("test.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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## Create the plot with the normalized time vs nblocks
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p = ggplot(D, aes(x=nby, y=tn)) +
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labs(x="nby", y="Time (s) * nodes",
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title=sprintf("Saiph-Heat3D granularity"),
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subtitle=input_file) +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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geom_point(shape=21, size=3) +
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geom_line(aes(color=nodes, group=nodes)) +
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#scale_x_continuous(trans=log2_trans()) +
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scale_y_continuous(trans=log2_trans())
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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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