代码测试cpu分析.docx

编码测试cpu的运行速度实验人:时间时间：代码来自某大神的博客圈！！编码工具:VS2010，需要对.NET4.0支持。编码语言:c#测试原理：字符串相关度计算是文本处理和数据挖掘中一个不可或缺的方法，Levenshtein Distance可以用来比较两个字符串的相似度，即两个字符串之间的“距离”。这个“距离”其实就是从源字符串变换到目标字符串需要进行的删除、插入和替换的次数。算法简图如下：即：随机生成1000个1000长度的字符串，并比较字符串相关度。实验代码：C#:/*比较1000长度字符串的相关度，计算cpu并串行计算时间*性能比计算为10000d/运算所用时间*运算时间越短，性能比值越大,cpu性能越高*/namespace SpeedTest{using System;using System.Diagnostics;using System.Text;using System.Threading.Tasks;internalclassProgram { #region Methodsprivatestaticvoid Main(string[] args) {var watch =newStopwatch();constlong count = 1000;constint length = 1000;string comparestring = StringDistance.GenerateRandomString(length);var strlist = newstring[count]; var steps = newint[count];// prepare string[] for comparison Parallel.For(0, count, i = strlist[i] = StringDistance.GenerateRandomString(length));Console.WriteLine(已经生成 + count + 个长度为 + length + 的字符串); watch.Start();for (int i = 0; i count; i++) { steps[i] = StringDistance.LevenshteinDistance(comparestring, strlist[i]); } watch.Stop();Console.WriteLine(完成非并行计算，耗时(ms)+watch.ElapsedMilliseconds);Console.WriteLine(性能比 + 100000d/watch.ElapsedMilliseconds); watch.Reset(); watch.Start();Parallel.For( 0, count, delegate(long i) { steps[i] = StringDistance.LevenshteinDistance(comparestring, strlist[i]); }); watch.Stop();Console.WriteLine(完成并行计算，耗时(ms) + watch.ElapsedMilliseconds);Console.WriteLine(性能比+ 100000d / watch.ElapsedMilliseconds);Console.ReadKey(); } #endregion }internalclassStringDistance { #region Public Methodspublicstaticstring GenerateRandomString(int length) {var r = newRandom((int)DateTime.Now.Ticks);var sb = newStringBuilder(length);for (int i = 0; i length; i++) {int c = r.Next(97, 123); sb.Append(Char.ConvertFromUtf32(c)); }return sb.ToString(); }publicstaticint LevenshteinDistance(string str1, string