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Goldfrapp - Head first -Universal. Head First. Our earlier prime number calculator might perform poorly with range partitioning.

An example of when range partitioning would do well is in calculating the sum of the square roots of the first 10 million integers:. For instance, if there are two workers, one worker might process odd-numbered elements while the other processes even-numbered elements. The TakeWhile operator is almost certain to trigger a striping strategy to avoid unnecessarily processing elements later in the sequence.

The following demonstrates how Aggregate can do the work of Sum :. The first argument to Aggregate is the seed , from which accumulation starts. The second argument is an expression to update the accumulated value, given a fresh element. You can optionally supply a third argument to project the final result value from the accumulated value. Most problems for which Aggregate has been designed can be solved as easily with a foreach loop — and with more familiar syntax.

The advantage of Aggregate is precisely that large or complex aggregations can be parallelized declaratively with PLINQ. You can omit the seed value when calling Aggregate , in which case the first element becomes the implicit seed, and aggregation proceeds from the second element.

We can better illustrate the difference by multiplying instead of adding:. However, there is a trap with unseeded aggregations: the unseeded aggregation methods are intended for use with delegates that are commutative and associative. If used otherwise, the result is either unintuitive with ordinary queries or nondeterministic in the case that you parallelize the query with PLINQ.

For example, consider the following function:. This is neither commutative nor associative. To illustrate, if we denote our aggregation function as follows:. There are two good solutions. The first is to turn this into a seeded aggregation — with zero as the seed. The second solution is to restructure the query such that the aggregation function is commutative and associative:.

Of course, in such simple scenarios you can and should use the Sum operator instead of Aggregate :. You can actually go quite far just with Sum and Average. For instance, you can use Average to calculate a root-mean-square:.

We just saw that for unseeded aggregations, the supplied delegate must be associative and commutative. PLINQ will give incorrect results if this rule is violated, because it draws multiple seeds from the input sequence in order to aggregate several partitions of the sequence simultaneously. Explicitly seeded aggregations might seem like a safe option with PLINQ, but unfortunately these ordinarily execute sequentially because of the reliance on a single seed.

To mitigate this, PLINQ provides another overload of Aggregate that lets you specify multiple seeds — or rather, a seed factory function. For each thread, it executes this function to generate a separate seed, which becomes a thread-local accumulator into which it locally aggregates elements. You must also supply a function to indicate how to combine the local and main accumulators. Finally, this Aggregate overload somewhat gratuitously expects a delegate to perform any final transformation on the result you can achieve this as easily by running some function on the result yourself afterward.

So, here are the four delegates, in the order they are passed:. In simple scenarios, you can specify a seed value instead of a seed factory. This tactic fails when the seed is a reference type that you wish to mutate, because the same instance will then be shared by each thread. This example is contrived in that we could get the same answer just as efficiently using simpler approaches such as an unseeded aggregate, or better, the Sum operator.

To give a more realistic example, suppose we wanted to calculate the frequency of each letter in the English alphabet in a given string. A simple sequential solution might look like this:.

An example of when the input text might be very long is in gene sequencing. To parallelize this, we could replace the foreach statement with a call to Parallel. And locking around accessing that array would all but kill the potential for parallelization.

Aggregate offers a tidy solution. The accumulator, in this case, is an array just like the letterFrequencies array in our preceding example. Notice that the local accumulation function mutates the localFrequencies array. This ability to perform this optimization is important — and is legitimate because localFrequencies is local to each thread.

PFX provides a basic form of structured parallelism via three static methods in the Parallel class:. All three methods block until all work is complete.

As with PLINQ , after an unhandled exception, remaining workers are stopped after their current iteration and the exception or exceptions are thrown back to the caller — wrapped in an AggregateException.

Invoke executes an array of Action delegates in parallel, and then waits for them to complete. The simplest version of the method is defined as follows:. On the surface, this seems like a convenient shortcut for creating and waiting on two Task objects or asynchronous delegates.

Invoke still works efficiently if you pass in an array of a million delegates. This is because it partitions large numbers of elements into batches which it assigns to a handful of underlying Task s — rather than creating a separate Task for each delegate.

This means you need to keep thread safety in mind. The following, for instance, is thread-unsafe:. Locking around adding to the list would resolve this, although locking would create a bottleneck if you had a much larger array of quickly executing delegates. A better solution is to use a thread-safe collection such as ConcurrentBag would be ideal in this case. Invoke is also overloaded to accept a ParallelOptions object:.

With ParallelOptions , you can insert a cancellation token , limit the maximum concurrency, and specify a custom task scheduler. Any already-executing delegates will, however, continue to completion. See Cancellation for an example of how to use cancellation tokens. For and Parallel. ForEach perform the equivalent of a C for and foreach loop, but with each iteration executing in parallel instead of sequentially. Here are their simplest signatures:. To give a practical example, if we import the System.

As with Parallel. Invoke , we can feed Parallel. ForEach usually work best on outer rather than inner loops. Parallelizing both inner and outer loops is usually unnecessary. Incrementing a shared variable, however, is not thread-safe in a parallel context. You must instead use the following version of ForEach :. The following code loads up a dictionary along with an array of a million words to test:.

We can perform the spellcheck on our wordsToTest array using the indexed version of Parallel. ForEach as follows:. Notice that we had to collate the results into a thread-safe collection: having to do this is the disadvantage when compared to using PLINQ.

So, to parallelize this:. You can see from the output that loop bodies may complete in a random order. Aside from this difference, calling Break yields at least the same elements as executing the loop sequentially: this example will always output at least the letters H , e , l , l , and o in some order. In contrast, calling Stop instead of Break forces all threads to finish right after their current iteration.

In our example, calling Stop could give us a subset of the letters H , e , l , l , and o if another thread was lagging behind. The Parallel. These tell you whether the loop ran to completion, and if not, at what cycle the loop was broken. If your loop body is long, you might want other threads to break partway through the method body in case of an early Break or Stop.

You can do this by polling the ShouldExitCurrentIteration property at various places in your code; this property becomes true immediately after a Stop — or soon after a Break. ShouldExitCurrentIteration also becomes true after a cancellation request — or if an exception is thrown in the loop. IsExceptional lets you know whether an exception has occurred on another thread. ForEach each offer a set of overloads that feature a generic type argument called TLocal.

These overloads are designed to help you optimize the collation of data with iteration-intensive loops. The simplest is this:.

These methods are rarely needed in practice because their target scenarios are covered mostly by PLINQ which is fortunate because these overloads are somewhat intimidating! Essentially, the problem is this: suppose we want to sum the square roots of the numbers 1 through 10,, Calculating 10 million square roots is easily parallelizable, but summing their values is troublesome because we must lock around updating the total:. The gain from parallelization is more than offset by the cost of obtaining 10 million locks — plus the resultant blocking.

Imagine a team of volunteers picking up a large volume of litter. If all workers shared a single trash can, the travel and contention would make the process extremely inefficient.

The volunteers are internal worker threads, and the local value represents a local trash can. In order for Parallel to do this job, you must feed it two additional delegates that indicate:. Additionally, instead of the body delegate returning void , it should return the new aggregate for the local value.

We must still lock, but only around aggregating the local value to the grand total. This makes the process dramatically more efficient. Notice that we used ParallelEnumerable to force range partitioning : this improves performance in this case because all numbers will take equally long to process. If you supplied a local seed factory, the situation would be somewhat analogous to providing a local value function with Parallel. Task parallelism is the lowest-level approach to parallelization with PFX.

The classes for working at this level are defined in the System. Tasks namespace and comprise the following:. Essentially, a task is a lightweight object for managing a parallelizable unit of work.

Tasks can be used whenever you want to execute something in parallel. Tasks do more than just provide an easy and efficient way into the thread pool.

They also provide some powerful features for managing units of work, including the ability to:. Tasks also implement local work queues , an optimization that allows you to efficiently create many quickly executing child tasks without incurring the contention overhead that would otherwise arise with a single work queue.

The Task Parallel Library lets you create hundreds or even thousands of tasks with minimal overhead. This is equivalent to the Threads window, but for tasks. The Parallel Stacks window also has a special mode for tasks. As we described in Part 1 in our discussion of thread pooling , you can create and start a Task by calling Task. StartNew , passing in an Action delegate:.

StartNew creates and starts a task in one step. You can decouple these operations by first instantiating a Task object, and then calling Start :.

A task that you create in this manner can also be run synchronously on the same thread by calling RunSynchronously instead of Start. When instantiating a task or calling Task. StartNew , you can specify a state object, which is passed to the target method. This is useful should you want to call a method directly rather than using a lambda expression:. Given that we have lambda expressions in C , we can put the state object to better use, which is to assign a meaningful name to the task.

We can then use the AsyncState property to query its name:. TaskCreationOptions is a flags enum with the following combinable values:. LongRunning suggests to the scheduler to dedicate a thread to the task. LongRunning is also good for blocking tasks. The task queuing problem arises because the task scheduler ordinarily tries to keep just enough tasks active on threads at once to keep each CPU core busy.

Not oversubscribing the CPU with too many active threads avoids the degradation in performance that would occur if the operating system was forced to perform a lot of expensive time slicing and context switching. PreferFairness tells the scheduler to try to ensure that tasks are scheduled in the order they were started.

It may ordinarily do otherwise, because it internally optimizes the scheduling of tasks using local work-stealing queues. This optimization is of practical benefit with very small fine-grained tasks. When one task starts another, you can optionally establish a parent-child relationship by specifying TaskCreationOptions. AttachedToParent :. A child task is special in that when you wait for the parent task to complete, it waits for any children as well. You can also wait on multiple tasks at once — via the static methods Task.

WaitAll waits for all the specified tasks to finish and Task. WaitAny waits for just one task to finish. WaitAll is similar to waiting out each task in turn, but is more efficient in that it requires at most just one context switch. Also, if one or more of the tasks throw an unhandled exception, WaitAll still waits out every task — and then rethrows a single AggregateException that accumulates the exceptions from each faulted task. As well as a timeout, you can also pass in a cancellation token to the Wait methods: this lets you cancel the wait — not the task itself.

When you wait for a task to complete by calling its Wait method or accessing its Result property , any unhandled exceptions are conveniently rethrown to the caller, wrapped in an AggregateException object. This usually avoids the need to write code within task blocks to handle unexpected exceptions; instead we can do this:. You still need to exception-handle detached autonomous tasks unparented tasks that are not waited upon in order to prevent an unhandled exception taking down the application when the task drops out of scope and is garbage-collected subject to the following note.

The static TaskScheduler. UnobservedTaskException event provides a final last resort for dealing with unhandled task exceptions.

By handling this event, you can intercept task exceptions that would otherwise end the application — and provide your own logic for dealing with them. For parented tasks, waiting on the parent implicitly waits on the children — and any child exceptions then bubble up:. You can optionally pass in a cancellation token when starting a task. This lets you cancel tasks via the cooperative cancellation pattern described previously :.

To detect a canceled task, catch an AggregateException and check the inner exception as follows:. If you want to explicitly throw an OperationCanceledException rather than calling token. You can either invest in upskilling your tech teams to drive business, or you can cross your fingers and hope.

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