How Computers Work

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  1. Introduction
  2. Series Overview
    1. General Knowledge
  3. Historical Background
    1. Mechanical Computation
    2. Universal Computation
      1. Lambda Calculus
      2. Turing machine
      3. Boolean Algebra
    3. Electrical Computation
      1. Engineering
      2. Architecture
  4. Conclusion


Computing is an incredibly complex and detailed topic, and one about which I’m both passionate and reasonably well-informed. I’ve had some remarks about how they work received well previously, even getting requests for a blog series about it.

Well, actually, the commenter was asking if I had a YouTube channel, but I can’t get into videos to save my life, so, I’m writing instead. Maybe one day I’ll narrate something. Who knows.

Series Overview

Modern computing uses a stack metaphor rather extensively, and for good reason. My goal with this is to have a series of articles, each of which covers one part of the stack, and will have a general discussion about where it fits in the big picture, and a more detailed discussion that (ideally) will not require reading the other articles to grasp it.

General Knowledge

One of the hazards of specialization is that jargon and technical information become common knowledge to me, so I lose track of what is or isn’t actually common knowledge. I will strive to avoid using such terms carelessly, and to clearly define all the necessary terms when they are first encountered. Since I have yet to set up a commenting system on this site, feel free to write me by email or any other means with questions or followups of any kind.

Without further ado, let’s begin.

Historical Background

In the general sense, a computer is any device which is capable of manipulating information to suit a human’s purpose. The abacus could be included in this category as a very early, primitive computer, as it assisted humans in performing arithmetic.

Mechanical Computation

However, the modern sense of the word “computer” is a narrower category, whose first entry is Charles Babbage’s difference engine. This was a machine which was built and engineered specifically to translate mechanical state into mathematical results. Put simply, the user would set up the machine with the desired input state, crank it, and the machine’s construction and mechanical linkage would eventually result in a numeric answer.

The Babbage difference engine, although capable of performing remarkable feats of mathematics, was a non-programmable, single-purpose device whose execution behavior is defined directly by its construction. The difference engine could only perform the one algorithm it was designed to run.

Babbage’s next project was to design an Analytical Engine which would be able to perform more general computation controlled by punched cards.

The design for the Analytical Engine included an arithmetic and logic unit, a control flow processor capable of executing conditional statements and loops, and persistent memory. These are the necessary elements of computing that would later be formalized as “Turing completeness”, making the Analytical Engine the first true general-purpose computer, and the ancestor of all modern computers.

If Charles Babbage is the first computer engineer, for designing the first fully general-purpose computer, then the first computer scientist is Ada Lovelace, for designing the first algorithm specifically designed to be executed by a hardware computer rather than a human being.

Babbage’s Analytical Engine was never built, nor was Lovelace’s algorithm ever executed, but their work in mechanical computation and mathematics laid the foundation for the birth of electronic computation nearly a century later.

Universal Computation

The invention and deployment of telegraph and telephone networks in the late 19th century provided the groundwork for electronic systems control. I will expand on this topic more in a future post.

The formal theories behind general-purpose computation were described in the mid-1930s independently by Alonzo Church’s lambda calculus and Alan Turing’s concept of the Turing machine. The set of logical behaviors used to implement computation were described by George Boole in the 1850s.

Lambda Calculus

I’m going to briefly talk about theoretical mathematics. Don’t worry if you’re not a “math person” or my usage of the word “calculus” worries you; this isn’t the calculus of which you’re thinking (that’s Newton/Leibniz calculus and nothing like what I’m covering) and I won’t be talking about it very much. I encourage you to read this section and the future article, but it’s fine if you skim or skip.

The lambda calculus (or λ-calculus) is a formal mathematical system for the notation and analysis of mathematical algorithms. It describes a universal model of computation, and provides the abstract mathematics governing the operation of Turing machines.

The lambda calculus models information as three things: terms, abstractions, and applications. A term is a variable name or constant, such as or . An abstraction is a function definition which transforms a term into another term, or another abstraction. An application applies an abstraction to a term, and reduces to the abstraction’s output when solving the system.

This is useful because it provides a theoretical model that maps exactly to how universal computing devices work, and allow for formal mathematical proofs of a program’s correctness. Every possible computation can be modeled on a whiteboard using lambda calculus, which means that programs can be solved on a whiteboard and shown to succeed or fail (…mostly.)

Turing machine

A Turing machine is a theoretical model for a machine which follows a certain set of rules for its behavior. A machine is said to be Turing complete if, by following these rules, any action and computation may be reached.

A Turing machine has:

  • An infinite memory from which it can read and to which it can write
  • A set of possible internal states
  • A list of instructions

It works by:

  • Selecting a single cell from memory and reading it
  • Executing the instruction appropriate for the combination of read memory and internal state
    • Writing a new value to that memory cell
    • Selecting a new internal state
    • Selecting a different memory cell

Turing machines are discrete, finite, and distinguishable. This means that it can not operate on, nor select, fractions of a cell; it has a limited amount of symbols on the memory it can process and a limited amount of internal state, and that each symbol and state are clearly distinct from all others. The completeness of a Turing machine comes from its infinite supply of memory and time of operation.

The importance of this concept, as well as that of the lambda calculus, is that together they define a formal model of general-purpose computation and the necessary properties of machines that can implement it.

Boolean Algebra

Boolean algebra is a set of mathematical rules that mix logic and arithmetic. It defines three fundamental operations, AND (), OR (), and NOT (), which can be combined to implement any logical transformation of data. The logical operators act on binary symbols, true and false. These can be represented as 1 and 0, respectively.

There are other Boolean operators that can be defined as combinations of these fundamentals: XOR (), XNOR (), NAND (), and NOR ().

The NAND and NOR operators are especially interesting, in that they are each functionally complete – that is, all other Boolean operations can be created out of combinations of only NAND or only NOR operators. Coincidentally, NAND and NOR logic gates are incredibly easy to make with modern transistors, so modern computers are usually built entirely out of NAND logic or NOR logic.

Electrical Computation

There are several problems with mechanical general-purpose computers, including size, speed (or rather the lack), and power consumption.


The advent of the telegraph and electrical signaling resulted in the discovery that switching relays could be combined to implement basic Boolean logic. If you’ll remember grade school electrical science, you’ll remember that there are two basic kinds of circuit layout: serial and parallel. Switches in series must all be closed to permit current to flow – an AND gate. Switches in parallel must all be open to forbid current to flow – an OR gate. A NOT gate can be created simply by reversing how a switch responds to an input signal. With these three Boolean primitives, an electronic Turing computer can be assembled according to equations in the lambda calculus.

At first, these [computers] were built using mechanical switches and plugs operated by human secretaries. The development of vacuum tubes improved their operation, although the computers were still large, slow, and power-hungry.

It wasn’t until William Shockley invented the first transistor in 1947 that computers were able to begin making significant advances in size, complexity, and self-manipulation. Since transistors are still in use today, I will delve more into their operation in a future article.


There are two overarching kinds of computer: those which keep their programming separate from their data, and those that do not. The former class is referred to as the Harvard architecture, and the latter as von Neumann machines. All computers, from Babbage’s Analytical Engine onwards, were Harvard-type machines until after the invention of transistors allowed computers to use the same signals and pathways for instruction programming as for data processing. Transistors are unlike any other computing element in that they both govern, and are controlled by, simple electrical voltages. Like a complex rail network with pressure-operated switches, so that tracks could be rearranged by the trains traveling across them, transistors can be constructed such that the data flowing through them alters the layout of the whole circuit.

Most modern computers use the von Neumann architecture, though many smaller devices such as embedded microcontrolelrs use the Harvard architecture, as it is often cheaper.


These topics provide the theoretical and engineering background on which computers are built, but I’m probably not going to touch on them very much, if at all, in future articles. They’re good to know, and provide some useful context, but if you skimmed or skipped them you’re not going to be overly penalized later. Since one of my goals of this series is to have each layer be more or less self-contained, it shouldn’t matter very much which topics you read and which you skip.