# 6.3 Modeling computing

By composing the logic functions, we could build a wine computer to perform any Boolean function. And, we can perform any discrete arithmetic function using only Boolean functions. For a useful computer, though, we need programmability. We would like to be able to make the inputs to the machine describe the logical functions that it should perform, rather than having to build a new machine for each desired function. We could, in theory, construct such a machine using wine, but it would be awfully complicated. Instead, we consider programmable computing machines abstractly.

Recall in Chapter 1, we defined a computer as a machine that can:

1. Accept input.

2. Execute a mechanical procedure.

3. Produce output.

So, our model of a computer needs to model these three things.

Modeling input. In real computers, input comes in many forms: typing on a keyboard, moving a mouse, packets coming in from the network, an accelerometer in the device, etc.

For our model, we want to keep things as simple as possible, though. From a computational standpoint, it doesn’t really matter how the input is collected. We can represent any discrete input with a sequence of bits. Input devices like keyboards are clearly discrete: there are a finite number of keys, and each key could be assigned a unique number. Input from a pointing device like a mouse could be continuous, but we can always identify some minimum detected movement distance, and record the mouse movements as discrete numbers of move units and directions. Richer input devices like a camera or microphone can also produce discrete output by discretizing the input using a process similar to the image storage in Chapter 1. So, the information produced by any input device can be represented by a sequence of bits.

The virtual shopping spree was a first for the President who has a reputation for being “technologically challenged.” But White House sources insist that the First Shopper used his own laptop and even “knew how to use the mouse.”

For real input devices, the time an event occurs is often crucial. When playing a video game, it does not just matter that the mouse button was clicked, it matters a great deal when the click occurs. How can we model inputs where time matters using just our simple sequence of bits?

One way would be to divide time into discrete quanta and encode the input as zero or one events in each quanta. A more efficient way would be to add a timestamp to each input. The timestamps are just numbers (e.g., the number of milliseconds since the start time), so can be written down just as sequences of bits.

Thus, we can model a wide range of complex input devices with just a finite sequence of bits. The input must be finite, since our model computer needs all the input before it starts processing. This means our model is not a good model for computations where the input is infinite, such as a web server intended to keep running and processing new inputs (e.g., requests for a web page) forever. In practice, though, this isn’t usually a big problem since we can make the input finite by limiting the time the server is running in the model.

A finite sequence of bits can be modeled using a long, narrow, tape that is divided into squares, where each square contains one bit of the input.

Modeling output. Output from computers effects the physical world in lots of very complex ways: displaying images on a screen, printing text on a printer, sending an encoded web page over a network, sending an electrical signal to an anti-lock brake to increase the braking pressure, etc.

We don’t attempt to model the physical impact of computer outputs; that would be far too complicated, but it is also one step beyond modeling the computation itself. Instead, we consider just the information content of the output. The information in a picture is the same whether it is presented as a sequence of bits or an image projected on a screen, its just less pleasant to look at as a sequence of bits. So, we can model the output just like we modeled the input: a sequence of bits written on a tape divided into squares.

Modeling processing. Our processing model should be able to model every possible mechanical procedure since we want to model a universal computer, but should be as simple as possible.

One thing our model computer needs is a way to keep track of what it is doing. We can think of this like scratch paper: a human would not be able to do a long computation without keeping track of intermediate values on scratch paper, and a computer has the same need. In Babbage’s Analytical Engine, this was called the store, and divided into a thousand variables, each of which could store a fifty decimal digit number. In the Apollo Guidance Computer, the working memory was divided into banks, each bank holding 1024 words. Each word was 15 bits (plus one bit for error correction). In current 32-bit processors, such as the x86, memory is divided into pages, each containing 1024 32-bit words.

For our model machine, we don’t want to have arbitrary limits on the amount of working storage. So, we model the working storage with an infinitely long tape. Like the input and output tapes, it is divided into squares, and each square can contain one symbol. For our model computer, it is useful to think about having an infinitely long tape, but of course, no real computer has infinite amounts of working storage. We can, however, imagine continuing to add more memory to a real computer as needed until we have enough to solve a given problem, and adding more if we need to solve a larger problem.

Our model now involves separate tapes for input, output, and a working tape. We can simplify the model by using a single tape for all three. At the beginning of the execution, the tape contains the input (which must be finite). As processing is done, the input is read and the tape is used as the working tape. Whatever is on the tape and the end of the execution is the output.

We also need a way for our model machine to interface with the tape. We imagine a tape head that contacts a single square on the tape. On each processing step, the tape head can read the symbol in the current square, write a symbol in the current square, and move one square either left or right.

The final thing we need is a way to model actually doing the processing. In our model, this means controlling what the tape head does: at each step, it needs to decide what to write on the tape, and whether to move left or right, or to finish the execution. In early computing machines, processing meant performing one of the basic arithmetic operations (addition, subtraction, multiplication, or division). We don’t want to have to model anything as complex as multiplication in our model machine, however. The previous section showed how addition and other arithmetic operations can be built from simpler logical operations. To carry out a complex operation as a composition of simple operations, we need a way to keep track of enough state to know what to do next. The machine state is just a number that keeps track of what the machine is doing. Unlike the tape, it is limited to a finite number. There are two reasons why the machine state number must be finite: first, we need to be able to write down the program for the machine by explaining what it should do in each state, which would be difficult if there were infinitely many states.

We also need rules to control what the tape head does. We can think of each rule as a mapping from the current observed state of the machine to what to do next. The input for a rule is the symbol in the current tape square and the current state of the machine; the output of each rule is three things: the symbol to write on the current tape square, the direction for the tape head to move (left, right, or halt), and the new machine state. We can describe the program for the machine by listing the rules. For each machine state, we need a rule for each possible symbol on the tape.