So far in this class we have only considered sequential programs. Execution of a sequential program proceeds one step at a time, with no choice about which step to take next. Sequential programs are limited in that they are not very good at dealing with multiple sources of simultaneous input and they can only execute on a single processor. Many modern applications are instead concurrent. Concurrent programs enable computations to overlap in duration, instead of being forced to happen sequentially.

Graphical user interfaces (GUIs), for example, rely on concurrency to keep the interface responsive while computation continues in the background.

  • A spreadsheet needs concurrency to re-compute all the cells while still keeping the menus and editing capabilities available for the user.

  • A web browser needs concurrency to read and render web pages incrementally as new data comes in over the network, to run JavaScript programs embedded in the web page, and to enable the user to navigate through the page and click on hyperlinks.

Without concurrency, a GUI would "lock up" until the current action is completed. Sometimes, because of concurrency bugs, that happens anyway—and it's frustrating for the user!

Servers are another example of applications that need concurrency. A web server needs to respond to many requests from clients, and clients would prefer not to wait. If an assignment is released in CMS, for example, you would prefer to be able to view that assignment at the same time as everyone else in the class, rather than having to "take a number" a wait for your number to be called—as at the Department of Motor Vehicles, or at an old-fashioned deli, etc.

One of the primary jobs of an operating system (OS) is to provide concurrency. The OS makes it possible for many applications to be executing concurrently: a music player, a web browser, a code editor, etc. How does it do that? There are two fundamental, complementary approaches:

  • Interleaving: rapidly switch back and forth between computations. For example, execute the music player for 100 milliseconds, then the browser, then the editor, then repeat. That makes it appear as though multiple computations are occurring simultaneously, but in reality, only one is ever occurring at the same time.

  • Parallelism: use hardware that is capable of performing two or more computations literally at the same time. Many processors these days are multicore, meaning that they have multiple central processing units (CPUs), each of which can be executing a program simultaneously.

Challenges of Concurrency

Regardless of the approaches being used, concurrent programming is challenging. Even if there are multiple cores available for simultaneous use, there are still many other resources that must be shared: memory, the screen, the network interface, etc. Managing that sharing, especially without introducing bugs, is quite difficult. For example, if two programs want to communicate by using the computer's memory, there needs to be some agreement on when each program is allowed to read and write from the memory. Otherwise, for example, both programs might attempt to write to the same location in memory, leading to corrupted data. Those kinds of race conditions, where a program races to complete its operations before another program, are notoriously difficult to avoid.

The most fundamental challenge is that concurrency makes the execution of a program become nondeterministic: the order in which operations occur cannot necessarily be known ahead of time. Race conditions are an example of nondeterminism. To program correctly in the face of nondeterminism, the programmer is forced to think about all possible orders in which operations might execute, and ensure that in all of them the program works correctly.

Purely functional programs make nondeterminism easier to reason about, because evaluation of an expression always returns the same value no matter what. For example, in the expression (2*4)+(3*5), the operations can be executed concurrently (e.g., with the left and right products evaluated simultaneously) without changing the answer. Imperative programming is more problematic. For example, the expressions !x and incr x; !x, if executed concurrently, could give different results depending on which executes first.


To make concurrent programming easier, computer scientists have invented many abstractions. One of the best known is threads. Abstractly, a thread is a single sequential computation. There can be many threads running at a time, either interleaved or in parallel depending on the hardware, and a scheduler handles choosing which threads are running at any given time. Scheduling can either be preemptive, meaning that the scheduler is permitted to stop a thread and restart it later without the thread getting a choice in the matter, or cooperative, meaning that the thread must choose to relinquish control back to the scheduler. The former can lead to race conditions, and the latter can lead to unresponsive applications.

Concretely, a thread is a set of values that are loaded into the registers of a processor. Those values tell the processor where to find the next instruction to execute, where its stack and heap are located in memory, etc. To implement preemption, a scheduler sets a timer in the hardware; when the timer goes off, the current thread is interrupted and the scheduler gets to run. CS 3410 and 4410 cover those concepts in detail.


In the functional programming paradigm, one of the best known abstractions for concurrency is promises. Other names for this idea include futures, deferreds, and delayeds. All those names refer to the idea of a computation that is not yet finished: it has promised to eventually produce a value in the future, but the completion of the computation has been deferred or delayed. There may be many such values being computed concurrently, and when the value is finally available, there may be computations ready to execute that depend on the value.

This idea has been widely adopted in many languages and libraries, including Java, JavaScript, and .NET. Indeed, modern JavaScript adds an async keyword that causes a function to return a promise, and an await keyword that waits for a promise to finish computing. There are two widely-used libraries in OCaml that implement promises: Async and Lwt. Async is developed by Jane Street. Lwt is part of the Ocsigen project, which is a web framework for OCaml.

We now take a deeper look at promises in Lwt. The name of the library was an acronym for "light-weight threads." But that was a misnomer, as the Github page admits (as of 10/22/18):

Much of the current manual refers to ... "lightweight threads" or just "threads." This will be fixed in the new manual. [Lwt implements] promises, and has nothing to do with system or preemptive threads.

So don't think of Lwt as having anything to do with threads: it really is a library for promises.

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