并行是.net4.5主打的技术，同时被封装到了System.Threading.Tasks命名空间下，对外提供了静态类Parallel，我们可以直接使用它的静态方法，它可以并行一个委托数组，或者一个IEnumerable的迭代，而今天主要通过一个访问数据库的代码，来说说并发Parallel对整个程序的提升是那么大，我们主要从数据库连接数和程序运行时间上就可以很清晰的看到效果。
简单的测试代码
[TestMethod]
public void Read()
{
Stopwatch sw = new Stopwatch();
sw.Start();
var actions = new List<Action>(); for (int i = ; i < ; i++)
{
actions.Add(() =>
{
using (var db = new am20160316Entities())
{
var repository = new Lind.DDD.Repositories.EF.EFRepository<ad_contract>(db);
var list = repository.GetModel().ToList();
}
});
}
Parallel.Invoke(actions.ToArray());
sw.Stop();
Console.WriteLine(sw.ElapsedMilliseconds);
}
上面代码为并行执行[可用的线程数据并行]（并不是说1000个任务，就必须开1000个线程去干这事，由于.net自己也线程池的概念，所有它会根据系统使用情况去为我们这个进程分配，如果你的各方面请求需要.net分配更多的线程，那就才会现时有多个线程启动（如果是连接数据库，它和你数据连接串的MaxPoolSize和MinPoolSize都是关系的）。
程序与服务器之前的连接
程序与数据库进行通讯时，同时开了一些连接，我们可以通过netstat -nba | findstr 192.168.2.123指令来查看与指定服务器通讯的情况，当我们修改MinPooSize的时候，会影响这个程序与数据库建立通讯的次数，当然同时建立通讯次数越多，性能越好，当然这也要看数据库服务器的能力了，呵呵。
aaarticlea/png;base64,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" alt="" />
事实上，如果我们的程序是单线程串行执行的，上面这个图只要显示一个活动的连接，性能与同时10几个连接，那肯定是没法比的，这个很容易理解，就像先10万的人过桥一下，大桥和独木桥来说，10万人通过的话，时间一定是不一样的，呵呵。
aaarticlea/png;base64,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" alt="" />
上面是并行处理1000次连接的时间，3427毫秒，而对于单线程串行来说，我们看一下它的处理时间
代码
[TestMethod]
public void SignalRead()
{
Stopwatch sw = new Stopwatch();
sw.Start();
for (int i = ; i < ; i++)
{ using (var db = new am20160316Entities())
{
var repository = new Lind.DDD.Repositories.EF.EFRepository<ad_contract>(db);
var list = repository.GetModel().ToList();
}
}
sw.Stop();
Console.WriteLine(sw.ElapsedMilliseconds);
}
与数据库建立单个连接
aaarticlea/png;base64,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" alt="" />
程序运行的时间
aaarticlea/png;base64,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" alt="" />
通过上面的截图我们可以看到，串行比并行慢了足足1000多毫秒，这个数值会随着并发量的提升而升高。
所以，在多核时代，在操作系统处理位数高的时代，我们应该尽量多用并行！
感谢各位的阅读！