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The Pillars of Concurrency


Pillar 2: Throughput and Scalability Via Concurrent Collections

Pillar 2, on the other hand, is about keeping hundreds of cores busy to compute results faster, thereby re-enabling the "free lunch"[2]. We particularly want to target operations performed on collections (any group of things, not just containers) and exploit parallelism in data and algorithm structures. Here, we use key terms like "scalability" and "throughput"; "data-driven" and "fine-grained" and "schedule"; and "side effect" and "reduction."

New hardware no longer delivers the "free lunch" of automatically running single-threaded code faster to the degree it did historically. Instead, it provides increasing capacity to run more tasks concurrently on more CPU cores and hardware threads. How can we write applications that will regain the free lunch, that we can ship today and know they will naturally execute faster on future machines having ever greater parallelism.

The key to scalability is not to divide the computation-intensive work across some fixed number of explicit threads hard-coded into the structure of the application (for instance, when a game might try to divide its computation work among a physics thread, a rendering thread, and an everything-else thread). As we'll see next month, that path leads to an application that prefers to run on some constant number K of cores, which can penalize a system with fewer than K cores and doesn't scale on a system with more than K cores. That's fine if you're targeting a known fixed hardware configuration, like a particular game console whose architecture isn't going to change until the next console generation, but it isn't scalable to hardware that supports greater parallelism.

Rather, the key to scalability is to express lots of latent concurrency in the program that scales to match its inputs (number of messages, size of data). We do this in two main ways.

The first way is to use libraries and abstractions that let you say what you want to do rather than specifically how to do it. Today, we may use tools like OpenMP to ask to execute a loop's iterations in parallel and let the runtime system decide how finely to subdivide the work to fit the number of cores available. Tomorrow, tools like parallel STL and parallel LINQ [5] will let us express queries like "select the names of all undergraduate students sorted by grade" that can be executed in parallel against an in-memory container as easily as they are routinely executed in parallel by a SQL database server.

The second way, is to explicitly express work that can be done in parallel. Today, we can do this by explicitly running work items on a thread pool (for instance, using Java ThreadPoolExecutor or .NET BackgroundWorker). Just remember that there is overhead to moving the work over to a pool, so the onus is on us to make sure the work is big enough to make that worthwhile. For example, we might implement a recursive algorithm like quicksort to at each step sort the left and right subranges in parallel if the subranges are large enough, or serially if they are small. Future runtime systems based on work stealing will make this style even easier by letting us simply express all possible parallelism without worrying if it's big enough, and rely on the runtime system to dynamically decide not to actually perform the work in parallel if it isn't worth it on a given user's machine (or with the load of other work in the system at a given time), with an acceptably low cost for the unrealized parallelism (for example, if the system decides to run it serially, we would want the performance penalty compared to if we had just written the recursive call purely serially in the first place to be similar to the overhead of calling an empty function).


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