先看段代码：
1 for (int i = 0; i < 10; i++)
2 {
3 Task.Factory.StartNew(()=>Console.WriteLine($"{Thread.CurrentThread.ManagedThreadId} ~ {i}"));
4 }
从代码上可以看出我们预期是打印1~10，但实际的打印结果是：
1 7 ~ 10 2 4 ~ 10 3 10 ~ 10 4 9 ~ 10 5 4 ~ 10 6 3 ~ 10 7 5 ~ 10 8 9 ~ 10 9 6 ~ 10 10 8 ~ 10
与预期的不一致，我们预期是打印数字1到10，但实际打印出来的是10次10。因为这几个lambda表达式中使用了同一个变量，并且这些匿名函数共享变量值。
再来看下面这段代码：
1 Action<int> displayNumber = n => Console.WriteLine(n); 2 int i = 5; 3 Task taskOne = Task.Factory.StartNew(() => displayNumber(i)); 4 i = 7; 5 Task taskTwo = Task.Factory.StartNew(() => displayNumber(i)); 6 Task.WaitAll(taskOne,taskTwo);
输出结果：
7 7
当闭包通过lambda表达式捕获可变变量时，lambda捕获变量的引用，而不是捕获该变量的当前值。因此，如果任务在变量的引用值更改后运行，则该值将是内存中最新的值，而不是捕获变量时的值。
为解决该问题，我们引入Parallel类来解决问题：
1 Parallel.For(0,10,i=>Console.WriteLine($"{Thread.CurrentThread.ManagedThreadId} ~ {i}"));
打印结果：
1 1 ~ 0 2 1 ~ 2 3 3 ~ 1 4 3 ~ 4 5 3 ~ 7 6 3 ~ 8 7 3 ~ 9 8 1 ~ 3 9 5 ~ 5 10 4 ~ 6
Parallel 类 提供对并行循环和区域的支持， 现在我们看下Parallel.for的代码：
1 // this needs to be in try-block because it can throw in BuggyScheduler.MaxConcurrencyLevel
2 rootTask = new ParallelForReplicatingTask(
3 parallelOptions,
4 delegate
5 {
6 //
7 // first thing we do upon enterying the task is to register as a new "RangeWorker" with the
8 // shared RangeManager instance.
9 //
10 // If this call returns a RangeWorker struct which wraps the state needed by this task
11 //
12 // We need to call FindNewWork32() on it to see whether there's a chunk available.
13 //
14 // Cache some information about the current task
15 Task currentWorkerTask = Task.InternalCurrent;
16 bool bIsRootTask = (currentWorkerTask == rootTask);
17 RangeWorker currentWorker = new RangeWorker();
18 Object savedStateFromPreviousReplica = currentWorkerTask.SavedStateFromPreviousReplica;
19 if (savedStateFromPreviousReplica is RangeWorker)
20 currentWorker = (RangeWorker)savedStateFromPreviousReplica;
21 else
22 currentWorker = rangeManager.RegisterNewWorker();
23 // These are the local index values to be used in the sequential loop.
24 // Their values filled in by FindNewWork32
25 int nFromInclusiveLocal;
26 int nToExclusiveLocal;
27 if (currentWorker.FindNewWork32(out nFromInclusiveLocal, out nToExclusiveLocal) == false ||
28 sharedPStateFlags.ShouldExitLoop(nFromInclusiveLocal) == true)
29 {
30 return; // no need to run
31 }
32 // ETW event for ParallelFor Worker Fork
33 if (TplEtwProvider.Log.IsEnabled())
34 {
35 TplEtwProvider.Log.ParallelFork((currentWorkerTask != null ? currentWorkerTask.m_taskScheduler.Id : TaskScheduler.Current.Id), (currentWorkerTask != null ? currentWorkerTask.Id : 0),
36 forkJoinContextID);
37 }
38 TLocal localValue = default(TLocal);
39 bool bLocalValueInitialized = false; // Tracks whether localInit ran without exceptions, so that we can skip localFinally if it wasn't
40 try
41 {
42 // Create a new state object that references the shared "stopped" and "exceptional" flags
43 // If needed, it will contain a new instance of thread-local state by invoking the selector.
44 ParallelLoopState32 state = null;
45 if (bodyWithState != null)
46 {
47 Contract.Assert(sharedPStateFlags != null);
48 state = new ParallelLoopState32(sharedPStateFlags);
49 }
50 else if (bodyWithLocal != null)
51 {
52 Contract.Assert(sharedPStateFlags != null);
53 state = new ParallelLoopState32(sharedPStateFlags);
54 if (localInit != null)
55 {
56 localValue = localInit();
57 bLocalValueInitialized = true;
58 }
59 }
60 // initialize a loop timer which will help us decide whether we should exit early
61 LoopTimer loopTimer = new LoopTimer(rootTask.ActiveChildCount);
62 // Now perform the loop itself.
63 do
64 {
65 if (body != null)
66 {
67 for (int j = nFromInclusiveLocal;
68 j < nToExclusiveLocal && (sharedPStateFlags.LoopStateFlags == ParallelLoopStateFlags.PLS_NONE // fast path check as SEL() doesn't inline
69 || !sharedPStateFlags.ShouldExitLoop()); // the no-arg version is used since we have no state
70 j += 1)
71 {
72 body(j);
73 }
74 }
75 else if (bodyWithState != null)
76 {
77 for (int j = nFromInclusiveLocal;
78 j < nToExclusiveLocal && (sharedPStateFlags.LoopStateFlags == ParallelLoopStateFlags.PLS_NONE // fast path check as SEL() doesn't inline
79 || !sharedPStateFlags.ShouldExitLoop(j));
80 j += 1)
81 {
82 state.CurrentIteration = j;
83 bodyWithState(j, state);
84 }
85 }
86 else
87 {
88 for (int j = nFromInclusiveLocal;
89 j < nToExclusiveLocal && (sharedPStateFlags.LoopStateFlags == ParallelLoopStateFlags.PLS_NONE // fast path check as SEL() doesn't inline
90 || !sharedPStateFlags.ShouldExitLoop(j));
91 j += 1)
92 {
93 state.CurrentIteration = j;
94 localValue = bodyWithLocal(j, state, localValue);
95 }
96 }
97 // Cooperative multitasking hack for AppDomain fairness.
98 // Check if allowed loop time is exceeded, if so save current state and return. The self replicating task logic
99 // will detect this, and queue up a replacement task. Note that we don't do this on the root task.
100 if (!bIsRootTask && loopTimer.LimitExceeded())
101 {
102 currentWorkerTask.SavedStateForNextReplica = (object)currentWorker;
103 break;
104 }
105 }
106 // Exit if we can't find new work, or if the loop was stoppped.
107 while (currentWorker.FindNewWork32(out nFromInclusiveLocal, out nToExclusiveLocal) &&
108 ((sharedPStateFlags.LoopStateFlags == ParallelLoopStateFlags.PLS_NONE) ||
109 !sharedPStateFlags.ShouldExitLoop(nFromInclusiveLocal)));
110 }
111 catch
112 {
113 // if we catch an exception in a worker, we signal the other workers to exit the loop, and we rethrow
114 sharedPStateFlags.SetExceptional();
115 throw;
116 }
117 finally
118 {
119 // If a cleanup function was specified, call it. Otherwise, if the type is
120 // IDisposable, we will invoke Dispose on behalf of the user.
121 if (localFinally != null && bLocalValueInitialized)
122 {
123 localFinally(localValue);
124 }
125 // ETW event for ParallelFor Worker Join
126 if (TplEtwProvider.Log.IsEnabled())
127 {
128 TplEtwProvider.Log.ParallelJoin((currentWorkerTask != null ? currentWorkerTask.m_taskScheduler.Id : TaskScheduler.Current.Id), (currentWorkerTask != null ? currentWorkerTask.Id : 0),
129 forkJoinContextID);
130 }
131 }
132 },
133 creationOptions, internalOptions);
134 rootTask.RunSynchronously(parallelOptions.EffectiveTaskScheduler); // might throw TSE
135 rootTask.Wait();
136 // If we made a cancellation registration, we need to clean it up now before observing the OCE
137 // Otherwise we could be caught in the middle of a callback, and observe PLS_STOPPED, but oce = null
138 if (parallelOptions.CancellationToken.CanBeCanceled)
139 {
140 ctr.Dispose();
141 }
142 // If we got through that with no exceptions, and we were canceled, then
143 // throw our cancellation exception
144 if (oce != null) throw oce;
body对于迭代范围 (的每个值调用一次委托 fromInclusive ， toExclusive) 。提供两个参数：
1、一个 Int32 值，该值表示迭代次数。
2、ParallelLoopState可用于提前中断循环的实例。ParallelLoopState对象是由编译器创建的; 它不能在用户代码中实例化。
继续来看：
Parallel.For(0, 10, (i,state) =>
{
if (i > 5)
state.Break();
Console.WriteLine($"{Thread.CurrentThread.ManagedThreadId} ~ {i}");
} );
输出：
1 1 ~ 0 2 1 ~ 1 3 1 ~ 2 4 1 ~ 3 5 1 ~ 4 6 1 ~ 5 7 1 ~ 6
在上面的方法中我们使用了 break方法。
调用 Break 方法会通知 for 操作，在当前的迭代之后，无需执行迭代。不过，如果所有迭代尚未执行，则仍必须执行当前的所有迭代。
因此，调用 Break 类似于 for c# 等语言中的传统循环内的中断操作，但它并不是完美的替代方法：例如，无法保证当前的迭代不会执行。
今天就先写道这里~