Created
October 25, 2009 14:40
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# Adaptive benchmarking code I copied from the blog of Mauricio Fernandez | |
module Adaptive | |
class Benchmark | |
# The version of the adaptive-benchmark library | |
VERSION = '0.1.0' | |
# Default options | |
REPORT_OPT = {:precision => 0.1, :confidence => 0.95} | |
def initialize(field_length, minimum_iterations) | |
@field_length = field_length | |
@minimum_iterations = minimum_iterations | |
end | |
def self.bm(field_length = 10, minimum_iterations = 10) | |
puts "#{" " * field_length}\t\t\tstddev\t\truns\tinterval\tconfidence" | |
yield new(field_length, minimum_iterations) | |
end | |
def report(name, options = {}) | |
old_sync, $stdout.sync = $stdout.sync, true | |
opt = REPORT_OPT.clone.update(options) | |
sample_avg = sample_variance = 0 | |
tms_to_total = lambda{ |tms| tms.utime + tms.stime } | |
take_sample = lambda do |i| | |
GC.start | |
t0 = tms_to_total[Process.times] | |
yield | |
exec_time = tms_to_total[Process.times] - t0 | |
new_sample_avg = sample_avg + (exec_time - sample_avg) / i | |
if i == 1 | |
sample_avg = new_sample_avg | |
next | |
end | |
sample_variance = (1 - 1.0/i) * sample_variance + (i+1) * (new_sample_avg - sample_avg) ** 2 | |
sample_avg = new_sample_avg | |
end | |
(1..@minimum_iterations).each{ |i| take_sample[i] } | |
population_variance = 1.0 * @minimum_iterations / (@min_runs - 1) * sample_variance | |
a = opt[:precision] * sample_avg | |
num_runs = (sample_variance / (a ** 2 * (1 - opt[:confidence]))).ceil | |
total_runs = @minimum_iterations + num_runs | |
(@minimum_iterations + 1..total_runs + 1).each{ |i| take_sample[i] } | |
population_variance = 1.0 * total_runs / (total_runs - 1) * sample_variance | |
puts "%-#{@field_length}s %8.6f\t%8.6f\t%-6d\t%8.6f\t%3d%%" % | |
[name, sample_avg, Math.sqrt(population_variance), total_runs, a, opt[:confidence] * 100] | |
ensure | |
$stdout.sync = old_sync | |
end | |
end | |
end |
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