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| # Plot the data | |
| # Imports | |
| library(reshape2) | |
| library(ggplot2) | |
| # Load the data | |
| # Change path to file | |
| # data_path <- '../parallel/analysis.csv' # You can use this instead | |
| data_path <- '/home/reynaldo/Documents/School/Spring2017/HPC/project/parallel_mcmc/parallel/complete_big_analysis.csv' | |
| perf_data <- read.csv(file=data_path, header=TRUE, sep=",") | |
| melted_data <- melt(perf_data, id=c("trial", "block", "thread")) | |
| five_hundred_trials <- melted_data[which(melted_data$trial == 500), ] | |
| five_hundred_trials$thread = as.factor(five_hundred_trials$thread) | |
| # Find which combination of blocks and threads works best | |
| ours_five_hundred_trials <- five_hundred_trials[which(five_hundred_trials$variable == "our_time"), ] | |
| ours_five_hundred_trials <- ours_five_hundred_trials[which(!is.na(ours_five_hundred_trials$thread)), ] | |
| line_base <- ggplot(ours_five_hundred_trials, aes(x=block, y=value, group=thread, shape=thread, color=thread)) + | |
| geom_line() + | |
| geom_point() | |
| line_base <- line_base + xlab("Blocks") + ylab("Time (seconds)") + labs(colour = "Num Threads", shape="Num Threads") | |
| line_base <- line_base + ggtitle("Blocks vs Time vs Threads") + theme(plot.title = element_text(hjust = 0.5)) | |
| line_base |