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13.3 Look, Ma, No (Default) Sharing

In the wake of the recent hardware and software developments, D chose to make a radical departure from other imperative languages: yes, D does support threads, but they do not share any mutable data by default—they are isolated from each other. Isolation is not achieved via hardware as in the case of processes, and it is not achieved through runtime checks; it is a natural consequence of the way D's type system is designed.

Such a decision is inspired by functional languages, which also strive to disallow all mutation and consequently mutable sharing. There are two differences. First, D programs can still use mutation freely—it's just that mutable data is not unwittingly accessible to other threads. Second, no sharing is a default choice, not the only one. To define data as being shared across threads, you must qualify its type with shared. Consider, for example, two simple module-scope definitions:

int perThread;
shared int perProcess;

In most languages, the first definition (or its syntactic equivalent) would introduce a global variable used by all threads; however, in D, perThread has a separate copy for each thread. The second declaration allocates only one int that is shared across all threads, so in a way it is closer (but not identical) to a traditional global variable.

The variable perThread is stored using an operating system facility known as thread-local storage (TLS). The access speed of TLS-allocated data is dependent upon the compiler implementation and the underlying operating system. Generally it is negligibly slower than accessing a regular global variable in a C program, for example. In the rare cases when that may be a concern, you may want to load the global into a stack variable in access-intensive loops.

This setup has two important advantages. First, default-share languages must carefully synchronize access around global data; that is not necessary for perThread because it is private to each thread. Second, the shared qualifier means that the type system and the human user are both in the know that perProcess is accessed by multiple threads simultaneously. In particular, the type system will actively guard the use of shared data and disallow uses that are obviously mistaken. This turns the traditional setup on its head: under a default-share regime, the programmer must keep track manually of which data is shared and which isn't, and indeed most concurrency-related bugs are caused by undue or unprotected sharing. Under the explicit shared regime, the programmer knows for sure that data not marked as shared is never indeed visible to more than one thread. (To ensure that guarantee, shared values undergo additional checks that we'll get to soon.)

Using shared data remains an advanced topic because although low-level coherence is automatically ensured by the type system, high-level invariants may not be. To provide safe, simple, and efficient communication between threads, the preferred method is to use a paradigm known as message passing. Memory-isolated threads communicate by sending each other asynchronous messages, which consist simply of D values packaged together.

Isolated workers communicating via simple channels are a very robust, time-proven approach to concurrency. Erlang has done that for years, as have applications based on the Message Passing Interface (MPI) specification [29].

To add acclaim to remedy,5 good programming practice even in default-share multithreaded languages actually enshrines that threads ought to be isolated. Herb Sutter, a world-class expert in concurrency, writes in an article eloquently entitled "Use threads correctly = isolation + asynchronous messages" [54]:

  • Threads are a low-level tool for expressing asynchronous work. "Uplevel" them by applying discipline: strive to make their data private, and have them communicate and synchronize using asynchronous messages. Each thread that needs to get information from other threads or from people should have a message queue, whether a simple FIFO queue or a priority queue, and organize its work around an event-driven message pump mainline; replacing spaghetti with event-driven logic is a great way to improve the clarity and determinism of your code.

If there is one thing that decades of computing have taught us, it must be that discipline-oriented programming does not scale. It is reassuring, then, to reckon that the quote above pretty much summarizes quite accurately the following few sections, save for the discipline part.

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The D Programming Language

This chapter is from the book

The D Programming Language

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