benchmarking data for variable width allocation project initial microbenchmark results

benchmarking-results.r

if (!requireNamespace('tidyverse'))
  install.packages('tidyverse')
if (!requireNamespace('ggplot2'))
  install.packages('ggplot2')

library(ggplot2)
library(tidyverse)

# Uncomment these to run either set of numbers

# OPTCARROT
master_benchmark_data <- as_tibble(c(43.27430926,
                                     42.65007047,
                                     43.32562887,
                                     42.64047665,
                                     43.40328494))
branch_benchmark_data <- as_tibble(c(41.59069356,
                                     40.51252649,
                                     40.55757381,
                                     41.76160937,
                                     42.95645122))
# MICRO-BENCHMARK
# master_benchmark_data <- as_tibble(c(16.372,
#                                      16.399,
#                                      16.058,
#                                      15.98,
#                                      16.426))
# branch_benchmark_data <- as_tibble(c(16.901,
#                                      16.909,
#                                      16.695,
#                                      16.757,
#                                      16.818))

all_data <-
  bind_rows(
  master_benchmark_data %>% add_column(branch = "master"),
  branch_benchmark_data %>% add_column(branch = "feature"))

data_means <- all_data %>% group_by(branch) %>% 
  summarise(means = mean(value),
            sdev = sd(value),
            count = n(),
            serr = sd(value)/sqrt(n()))

p <- ggplot(data = data_means) + 
  aes(x = branch, y = means) +
  geom_point(data=data_means) + 
  labs(title = "Optcarrot: Mean and standard error of feature vs master branches", x = "branch", y = "frames per second") + 
  geom_errorbar(aes(ymin=means - serr, ymax=means + serr), width=.05,
                position=position_dodge(.9)) 
p

mean_diff <- data_means %>% 
  pull(means)

mean_perc_diff = ((mean_diff[1] - mean_diff[2])/mean_diff[1]) * 100

err_diffs <- data_means %>%
  summarize(branch,
            upper = means + serr,
            lower = means - serr)

f_lower = err_diffs$upper[err_diffs$branch == 'feature']
m_upper = err_diffs$lower[err_diffs$branch == 'master']

min_err_difference =  ((f_lower - m_upper) / f_lower) * 100