How To Multithread Safely and Efficiently in .NET


Multithreading can be utilized to drastically velocity up the efficiency of your software, however no speedup is free—managing parallel threads requires cautious programming, and with out the right precautions, you possibly can run into race situations, deadlocks, and even crashes.

What Makes Multithreading Hard?

Unless you inform your program in any other case, your whole code executes on the “Main Thread.” From the entrypoint of your software, it runs by way of and executes all of your features one after one other. This has a restrict to efficiency, since clearly you possibly can solely accomplish that a lot if it’s important to course of every little thing one by one. Most fashionable CPUs have six or extra cores with 12 or extra threads, so there’s efficiency left on the desk in the event you’re not using them.

However, it’s not so simple as simply “turning on multithreading.” Only particular issues (similar to loops) may be correctly multithreaded, and there’s a variety of issues to consider when doing so.

The first and most necessary concern is race situations. These usually happen throughout write operations, when one thread is modifying a useful resource that’s shared by a number of threads. This results in conduct the place the output of this system depends upon which thread finishes or modifies one thing first, which may result in random and surprising conduct.

These may be very, quite simple—for instance, perhaps it’s worthwhile to maintain a operating rely of one thing between the loops. The most blatant manner to do that is making a variable and incrementing it, however this isn’t thread protected.

This race situation happens as a result of it’s not simply “adding one to the variable” in an summary sense; the CPU is loading the worth of quantity into the register, including one to that worth, and then storing the consequence as the brand new worth of the variable. It doesn’t know that, in the meantime, one other thread was additionally attempting to do precisely the identical, and loaded a soon-to-be incorrect worth of quantity. The two threads battle, and on the finish of the loop, quantity might not be equal to 100.

.NET gives a characteristic to assist handle this: the lock key phrase. This doesn’t forestall making modifications outright, nevertheless it helps handle concurrency by solely permitting one thread at a time to acquire the lock. If one other thread tries to enter a lock assertion whereas one other thread is processing, it can look forward to as much as 300ms earlier than continuing.

You’re solely capable of lock reference varieties, so a standard sample is making a lock object beforehand, and utilizing that instead to locking the worth sort.

However, you could discover that there’s now one other downside: deadlocks. This code is a worst case instance, however right here, it’s nearly precisely the identical as simply doing a daily for loop (truly a bit slower, since additional threads and locks are additional overhead). Each thread tries to acquire the lock, however solely one by one can have the lock, so just one thread at a time can truly run the code contained in the lock. In this case, that’s your complete code of the loop, so the lock assertion is eradicating all the advantages of threading, and simply making every little thing slower.

Generally, you need to lock as wanted at any time when it’s worthwhile to make writes. However, you’ll need to maintain concurrency in thoughts when selecting what to lock, as a result of reads aren’t all the time thread protected both. If one other thread is writing to the item, studying it from one other thread can provide an incorrect worth, or trigger a specific situation to return an improper consequence.

Luckily, there are a couple of methods to doing this correctly the place you possibly can steadiness the velocity of multithreading whereas utilizing locks to keep away from race situations.

Use Interlocked For Atomic Operations

For primary operations, utilizing the lock assertion may be overkill. While it’s very helpful for locking earlier than advanced modifications, it’s an excessive amount of overhead for one thing so simple as including or changing a worth.

Interlocked is a category that wraps some reminiscence operations like addition, changing, and comparability. The underlying strategies are carried out on the CPU degree and assured to be atomic, and a lot quicker than the usual lock assertion. You’ll need to use them at any time when potential, although they received’t totally exchange locking.

In the instance above, changing the lock with a name to Interlocked.Add() will velocity up the operation rather a lot. While this easy instance isn’t quicker than simply not utilizing Interlocked, it’s helpful as part of a bigger operation and continues to be a speedup.

There’s additionally Increment and Decrement for ++ and -- operations, which can prevent a stable two keystrokes. They actually wrap Add(ref rely, 1) underneath the hood, so there’s no particular speedup to utilizing them.

You may use Exchange, a generic methodology that may set a variable equal to the worth handed to it. Though, you need to be cautious with this one—in the event you’re setting it to a worth you computed utilizing the unique worth, this isn’t thread protected, because the previous worth may have been modified earlier than operating Interlocked.Exchange.

CompareExchange will verify two values for equality, and exchange the worth in the event that they’re equal.

Use Thread Safe Collections

The default collections in System.Collections.Generic can be utilized with multithreading, however they aren’t totally thread protected. Microsoft provides thread-safe implementations of some collections in System.Collections.Concurrent.

Among these embrace the ConcurrentBag, an unordered generic assortment, and ConcurrentDictionary, a thread-safe Dictionary. There are additionally concurrent queues and stacks, and OrderablePartitioner, which may break up orderable information sources like Lists into separate partitions for every thread.

Look to Parallelize Loops

Often, the best place to multithread is in huge, costly loops. If you possibly can execute a number of choices in parallel, you may get an enormous speedup in the general operating time.

The finest technique to deal with that is with System.Threading.Tasks.Parallel. This class gives replacements for for and foreach loops that execute the loop our bodies on separate threads. It’s easy to make use of, although requires barely totally different syntax:

Obviously, the catch right here is that it’s worthwhile to ensure that DoSomething() is thread protected, and doesn’t intrude with any shared variables. However, that isn’t all the time as simple as simply changing the loop with a parallel loop, and in many circumstances you could lock shared objects to make modifications.

To alleviate among the points with deadlocks, Parallel.For and Parallel.ForEach present additional options for coping with state. Basically, not each iteration goes to run on a separate thread—when you have 1000 components, it’s not going to create 1000 threads; it’s going to make as many threads as your CPU can deal with, and run a number of iterations per thread. This implies that in the event you’re computing a complete, you don’t must lock for each iteration. You can merely cross round a subtotal variable, and on the very finish, lock the item and make modifications as soon as. This drastically reduces the overhead on very massive lists.

Let’s check out an instance. The following code takes an enormous checklist of objects, and must serialize every one individually to JSON, ending up with a List<string> of all of the objects. JSON serialization is a really sluggish course of, so splitting every factor over a number of threads is an enormous speedup.

There are a bunch of arguments, and rather a lot to unpack right here:

  • The first argument takes an IEnumerable, which defines the info it’s looping over. This is a ForEach loop, however the identical idea works for primary For loops.
  • The first motion initializes the native subtotal variable. This variable will likely be shared over every iteration of the loop, however solely inside the identical thread. Other threads can have their very own subtotals. Here, we’re initializing it to an empty checklist. If you had been computing a numeric complete, you may return 0 right here.
  • The second motion is the principle loop physique. The first argument is the present factor (or the index in a For loop), the second is a ParallelLoopState object that you should utilize to name .Break(), and the final is the subtotal variable.
    • In this loop, you possibly can function on the factor, and modify the subtotal. The worth you come will exchange the subtotal for the following loop. In this case, we serialize the factor to a string, then add the string to the subtotal, which is a List.
  • Finally, the final motion takes the subtotal ‘result’ after all of the executions have completed, permitting you to lock and modify a useful resource primarily based on the ultimate complete. This motion runs as soon as, on the very finish, nevertheless it nonetheless runs on a separate thread, so you’ll need to lock or use Interlocked strategies to change assets. Here, we name AddVary() to append the subtotal checklist to the ultimate checklist.

Unity Multithreading

One remaining notice—in the event you’re utilizing the Unity sport engine, you’ll need to watch out with multithreading. You can’t name any Unity APIs, or else the sport will crash. It’s potential to make use of it sparingly by doing API operations on the principle thread and switching again and forth at any time when it’s worthwhile to parallelize one thing.

Mostly, this is applicable to operations that work together with the scene or physics engine. Vector3 math is unaffected, and you’re free to make use of it from a separate thread with out points. You’re additionally free to change fields and properties of your individual objects, supplied that they don’t name any Unity operations underneath the hood.



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