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jsexecutor.js
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114 lines (110 loc) · 5.26 KB
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#!/usr/bin/env node
(function () {
var program = require('commander');
var fs = require('fs');
//var util = require('util'); this can be used for measuring used memory
var ss = require('simple-statistics');
// var anova = require('./anova');
var child = require('child_process');
// var d3 = require("d3");
var MIN_MEASURABLE_TIME = 5000000;
function warmUpAndTest(path, vmRestartsLeft, nbWarmUpRepetitions, nbMeasurementRepetitions, measuredTimes, f) {
console.log(vmRestartsLeft);
var command = 'node jsexecutorWarmUpAndTest.js ' + path + " " + nbWarmUpRepetitions + " " + nbMeasurementRepetitions;
child.exec(command, function (error, stdout, stderr) {
if (error) {
console.log('Error code: ' + error);
}
//for a longer term check the diff between .exec and .spawn and see does .spawn returns more than a string
var measuredMeanTime = parseFloat(stdout.split('mean:')[1]);
if (measuredMeanTime < MIN_MEASURABLE_TIME) {
console.log("Warning: Measured time is below the minimum measurable time: " + measuredMeanTime);
console.log(" Please increase running time by about "+Math.ceil(MIN_MEASURABLE_TIME / measuredMeanTime)+"x")
}
measuredTimes.push(measuredMeanTime);
if (vmRestartsLeft - 1 > 0) {
// schedule another run of execute
setTimeout(warmUpAndTest.bind(null, path, vmRestartsLeft - 1, nbWarmUpRepetitions, nbMeasurementRepetitions, measuredTimes, f), 0);
} else {
f(measuredTimes);
}
});
//console.log('Memory usage:'+util.inspect(process.memoryUsage()));
}
//this is the first and most simple way of comparing two alternatives described in paper: Statistically Rigorous Java Performance Evaluation
function compare2Alternatives(m1, m2, s1, s2, con_coef, N) {
var m = Math.abs(m1 - m2);
var s = Math.sqrt((Math.pow(s1, 2) + Math.pow(s2, 2)) / N);
var interval = [];
var lower = parseInt(m - con_coef * s);
var upper = parseInt(m + con_coef * s);
interval.push(lower);
interval.push(upper);
return interval;
}
function write2File(path1, path2, mean1, mean2, result) {
var message = 'Files: ' + path1.toString() + ', ' + path2.toString() + ' are executed 100 times';
fs.writeFile('output.txt', message,
function (err) {
if (err)
throw err;
});
}
program
.version('0.0.1')
.usage('<path1> <path2> <#VM invocations> <#warm up repetitions> <#measurement repetitions> <alpha>')
.parse(process.argv);
if (!program.args.length) {
program.help();
} else if (program.args.length != 6) {
console.log("Incorrect arguments. See --help.");
}
else {
//find a way to first validate inputs
var path1 = program.args[0];
var path2 = program.args[1];
var nbVMInvocations = parseInt(program.args[2]);
var nbWarmUpRepetitions = parseInt(program.args[3]);
var nbMeasurementRepetitions = parseInt(program.args[4]);
var alpha = parseFloat(program.args[5]);
//values obtained from the z table
//https://www.easycalculation.com/statistics/normal-ztable.php
var con_coef;
if (alpha === 0.90) {
con_coef = 1.65;
} else if (alpha === 0.95) {
con_coef = 1.96;
} else if (alpha === 0.99) {
con_coef = 2.58;
}
var mean1 = 0;
var mean2 = 0;
var st_dev1 = 0;
var st_dev2 = 0;
// measure execution time of first test case
warmUpAndTest(path1, nbVMInvocations, nbWarmUpRepetitions, nbMeasurementRepetitions, [], function (measuredTimes1) {
mean1 = Math.round(parseFloat(ss.mean(measuredTimes1)));
st_dev1 = ss.standard_deviation(measuredTimes1);
console.log('mean1: ' + mean1);
// measure execution time of second test case
warmUpAndTest(path2, nbVMInvocations, nbWarmUpRepetitions, nbMeasurementRepetitions, [], function (measuredTimes2) {
mean2 = Math.round(parseFloat(ss.mean(measuredTimes2)));
st_dev2 = ss.standard_deviation(measuredTimes2);
console.log('mean2: ' + mean2);
//comparing two alternatives means computing confidence interval for the difference of the means
var results = compare2Alternatives(mean1, mean2, st_dev1, st_dev2, con_coef, nbVMInvocations);
if (results[0] <= 0 && results[1] >= 0) {
console.log('the performance difference is not statistically significant');
} else {
console.log('the performance difference is statistically significant');
var diff = 100 - (mean2 * 100) / mean1;
console.log('performance gain is: ' + diff + '%');
}
}, 0);
}, 0);
/// var result = anova.anova(array1, array2, mean1, mean2,N);
// console.log('the difference is statistically significant:' + result + ' for the 0.05 significance level');
//export results to a file.....
//write2File(path1, path2, mean1, mean function (error, stdout, stderr) {
}
})(this);