多线程那点事—Parallel.for
奔跑的男人 · 665浏览 · 发布于2021-01-04
先看段代码：
for (int i = 0; i < 10; i++)
{
Task.Factory.StartNew(()=>Console.WriteLine($"{Thread.CurrentThread.ManagedThreadId} ~ {i}"));4 }从代码上可以看出我们预期是打印1~10，但实际的打印结果是：
7 ~ 10 4 ~ 10 10 ~ 10 9 ~ 10 4 ~ 10 3 ~ 10 5 ~ 10 9 ~ 10 6 ~ 10 8 ~ 10
与预期的不一致，我们预期是打印数字1到10，但实际打印出来的是10次10。因为这几个lambda表达式中使用了同一个变量，并且这些匿名函数共享变量值。
再来看下面这段代码：
Action<int> displayNumber = n => Console.WriteLine(n); int i = 5; Task taskOne = Task.Factory.StartNew(() => displayNumber(i)); i = 7; Task taskTwo = Task.Factory.StartNew(() => displayNumber(i)); Task.WaitAll(taskOne,taskTwo);
输出结果：
7 7
当闭包通过lambda表达式捕获可变变量时，lambda捕获变量的引用，而不是捕获该变量的当前值。因此，如果任务在变量的引用值更改后运行，则该值将是内存中最新的值，而不是捕获变量时的值。
为解决该问题，我们引入Parallel类来解决问题：
Parallel.For(0,10,i=>Console.WriteLine($"{Thread.CurrentThread.ManagedThreadId} ~ {i}"));打印结果：
1 ~ 0 1 ~ 2 3 ~ 1 3 ~ 4 3 ~ 7 3 ~ 8 3 ~ 9 1 ~ 3 5 ~ 5 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：
// this needs to be in try-block because it can throw in BuggyScheduler.MaxConcurrencyLevel
rootTask = new ParallelForReplicatingTask(
parallelOptions,
delegate
{
//
// first thing we do upon enterying the task is to register as a new "RangeWorker" with the
// shared RangeManager instance.
//
// If this call returns a RangeWorker struct which wraps the state needed by this task
//
// We need to call FindNewWork32() on it to see whether there's a chunk available.
//
// Cache some information about the current task
Task currentWorkerTask = Task.InternalCurrent;
bool bIsRootTask = (currentWorkerTask == rootTask);
RangeWorker currentWorker = new RangeWorker();
Object savedStateFromPreviousReplica = currentWorkerTask.SavedStateFromPreviousReplica;
if (savedStateFromPreviousReplica is RangeWorker)
currentWorker = (RangeWorker)savedStateFromPreviousReplica;
else
currentWorker = rangeManager.RegisterNewWorker();
// These are the local index values to be used in the sequential loop.
// Their values filled in by FindNewWork32
int nFromInclusiveLocal;
int nToExclusiveLocal;
if (currentWorker.FindNewWork32(out nFromInclusiveLocal, out nToExclusiveLocal) == false ||
sharedPStateFlags.ShouldExitLoop(nFromInclusiveLocal) == true)
{
return; // no need to run
}
// ETW event for ParallelFor Worker Fork
if (TplEtwProvider.Log.IsEnabled())
{
TplEtwProvider.Log.ParallelFork((currentWorkerTask != null ? currentWorkerTask.m_taskScheduler.Id : TaskScheduler.Current.Id), (currentWorkerTask != null ? currentWorkerTask.Id : 0),
forkJoinContextID);
}
TLocal localValue = default(TLocal);
bool bLocalValueInitialized = false; // Tracks whether localInit ran without exceptions, so that we can skip localFinally if it wasn't
try
{
// Create a new state object that references the shared "stopped" and "exceptional" flags
// If needed, it will contain a new instance of thread-local state by invoking the selector.
ParallelLoopState32 state = null;
if (bodyWithState != null)
{
Contract.Assert(sharedPStateFlags != null);
state = new ParallelLoopState32(sharedPStateFlags);
}
else if (bodyWithLocal != null)
{
Contract.Assert(sharedPStateFlags != null);
state = new ParallelLoopState32(sharedPStateFlags);
if (localInit != null)
{
localValue = localInit();
bLocalValueInitialized = true;
}
}
// initialize a loop timer which will help us decide whether we should exit early
LoopTimer loopTimer = new LoopTimer(rootTask.ActiveChildCount);
// Now perform the loop itself.
do
{
if (body != null)
{
for (int j = nFromInclusiveLocal;
j < nToExclusiveLocal && (sharedPStateFlags.LoopStateFlags == ParallelLoopStateFlags.PLS_NONE // fast path check as SEL() doesn't inline
|| !sharedPStateFlags.ShouldExitLoop()); // the no-arg version is used since we have no state
j += 1)
{
body(j);
}
}
else if (bodyWithState != null)
{
for (int j = nFromInclusiveLocal;
j < nToExclusiveLocal && (sharedPStateFlags.LoopStateFlags == ParallelLoopStateFlags.PLS_NONE // fast path check as SEL() doesn't inline
|| !sharedPStateFlags.ShouldExitLoop(j));
j += 1)
{
state.CurrentIteration = j;
bodyWithState(j, state);
}
}
else
{
for (int j = nFromInclusiveLocal;
j < nToExclusiveLocal && (sharedPStateFlags.LoopStateFlags == ParallelLoopStateFlags.PLS_NONE // fast path check as SEL() doesn't inline
|| !sharedPStateFlags.ShouldExitLoop(j));
j += 1)
{
state.CurrentIteration = j;
localValue = bodyWithLocal(j, state, localValue);
}
}
// Cooperative multitasking hack for AppDomain fairness.
// Check if allowed loop time is exceeded, if so save current state and return. The self replicating task logic
// will detect this, and queue up a replacement task. Note that we don't do this on the root task.
if (!bIsRootTask && loopTimer.LimitExceeded())
{
currentWorkerTask.SavedStateForNextReplica = (object)currentWorker;
break;
}
}
// Exit if we can't find new work, or if the loop was stoppped.
while (currentWorker.FindNewWork32(out nFromInclusiveLocal, out nToExclusiveLocal) &&
((sharedPStateFlags.LoopStateFlags == ParallelLoopStateFlags.PLS_NONE) ||
!sharedPStateFlags.ShouldExitLoop(nFromInclusiveLocal)));
}
catch
{
// if we catch an exception in a worker, we signal the other workers to exit the loop, and we rethrow
sharedPStateFlags.SetExceptional();
throw;
}
finally
{
// If a cleanup function was specified, call it. Otherwise, if the type is
// IDisposable, we will invoke Dispose on behalf of the user.
if (localFinally != null && bLocalValueInitialized)
{
localFinally(localValue);
}
// ETW event for ParallelFor Worker Join
if (TplEtwProvider.Log.IsEnabled())
{
TplEtwProvider.Log.ParallelJoin((currentWorkerTask != null ? currentWorkerTask.m_taskScheduler.Id : TaskScheduler.Current.Id), (currentWorkerTask != null ? currentWorkerTask.Id : 0),
forkJoinContextID);
}
}
},
creationOptions, internalOptions);
rootTask.RunSynchronously(parallelOptions.EffectiveTaskScheduler); // might throw TSE
rootTask.Wait();
// If we made a cancellation registration, we need to clean it up now before observing the OCE
// Otherwise we could be caught in the middle of a callback, and observe PLS_STOPPED, but oce = null
if (parallelOptions.CancellationToken.CanBeCanceled)
{
ctr.Dispose();
}
// If we got through that with no exceptions, and we were canceled, then
// throw our cancellation exception
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 ~ 0 1 ~ 1 1 ~ 2 1 ~ 3 1 ~ 4 1 ~ 5 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~
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