In this lesson, we'll discuss the basics of concurrency.z
You should understand assignment and mutable data.
Most of this lesson is based off of these notes and SICP 3.4.
In Unit 3, we saw the power of computational objects with local state as tools for modeling. But this power came at a price.
By introducing assignment we are forced to admit time into our computational models. Before we introduced assignment, all our programs were timeless, in the sense that any expression that has a value always has the same value. In contrast, recall the example of modeling withdrawals from a bank account and returning the resulting balance, introduced at the beginning of section 3.1.1 in SICP:
> (withdraw 25)
75
> (withdraw 25)
50
Here successive evaluations of the same expression yield different values.
This behavior arises from the fact that the execution of assignment statements
(in this case, assignments to the variable balance) delineates moments in time
when values change. The result of evaluating an expression depends not only on
the expression itself, but also on whether the evaluation occurs before or
after these moments. Building models in terms of computational objects with
local state forces us to confront time as an essential concept in programming.
We can go further in structuring computational models to match our perception of the physical world. Objects in the world do not change one at a time in sequence. Rather we perceive them as acting concurrently—all at once. So it is often natural to model systems as collections of computational processes that execute concurrently. Just as we can make our programs modular by organizing models in terms of objects with separate local state, it is often appropriate to divide computational models into parts that evolve separately and concurrently.
In addition to making programs more modular, concurrent computation can provide a speed advantage over sequential computation. Sequential computers execute only one operation at a time, so the amount of time it takes to perform a task is proportional to the total number of operations performed. However, if it is possible to decompose a problem into pieces that are relatively independent and need to communicate only rarely, it may be possible to allocate pieces to separate computing processors, producing a speed advantage proportional to the number of processors available.
Unfortunately, the complexities introduced by assignment become even more problematic in the presence of concurrency. The fact of concurrent execution, either because the world operates in parallel or because our computers do, entails additional complexity in our understanding of time.