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## Managed Open Source blog code # http://blog.managedopensource.com # http://blog.managedopensource.com/2007/10/31/reversal-of-code-performance-optimizations-from-ruby-1-8-to-1-9 ## R analysis code for benchmark results > for_real_totals <- c(5.962244, 5.916540, 5.986721, 5.900563) > each_real_totals <- c(6.158466, 6.165264, 6.135532, 6.131811) > list_real_totals <- c(6.855109, 6.918924, 6.871511, 6.912522) > collect_real_totals <- c(6.423387, 6.398015, 6.392726, 6.407939) On to ratios... > c1_to_c2_ratio <- for_real_totals / each_real_totals > c1_to_c2_ratio [1] 0.9681378 0.9596572 0.9757460 0.9622872 > c3_to_c4_ratio <- list_real_totals / collect_real_totals > c3_to_c4_ratio [1] 1.067211 1.081417 1.074895 1.078743 Since c4 (aka "collect") is faster take the inverse ratio... > c4_to_c3_ratio <- collect_real_totals / list_real_totals > c4_to_c3_ratio [1] 0.9370219 0.9247124 0.9303232 0.9270045 On to standard deviations.... > stdev.for <- sd(for_real_totals) > stdev.for [1] 0.0398919 > stdev.each <- sd(each_real_totals) > stdev.each [1] 0.01658215 > stdev.list <- sd(list_real_totals) > stdev.list [1] 0.03110267 > stdev.collect <- sd(collect_real_totals) > stdev.collect [1] 0.01347952 > avg.for <- mean(for_real_totals) > avg.for [1] 5.941517 > avg.each <- mean(each_real_totals) > avg.each [1] 6.147768 > avg.list <- mean(list_real_totals) > avg.list [1] 6.889517 > avg.collect <- mean(collect_real_totals) > avg.collect [1] 6.405517 > avg.c1_to_c2_ratio <- avg.for / avg.each > avg.c1_to_c2_ratio [1] 0.966451 > avg.c3_to_c4_ratio <- avg.list / avg.collect > avg.c3_to_c4_ratio [1] 1.075560 > avg.c4_to_c3_ratio <- avg.collect / avg.list > avg.c4_to_c3_ratio [1] 0.9297484

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