helps you understand the data.
Scheduling: Making the use of resources fair to all concerned is another way in which algorithms make their presence known in a big way. For example, timing lights at intersections are no longer simple devices that count down the seconds between light changes. Modern devices consider all sorts of issues, such as the time of day, weather conditions, and flow of traffic.
Graph analysis: Deciding on the shortest path between two points finds all sorts of uses. For example, in a routing problem, your GPS couldn’t function without this particular algorithm because it could never direct you along city streets using the shortest route from point A to point B. And even then, your GPS might direct you to drive into a lake (https://theweek.com/articles/464674/8-drivers-who-blindly-followed-gps-into-disaster).
Cryptography: Keeping data safe is an ongoing battle with hackers constantly attacking data sources. Algorithms make it possible to analyze data, put it into some other form, and then return it to its original form later.
Pseudorandom number generation: Imagine playing games that never varied. You start at the same place; perform the same steps, in the same manner, every time you play. Without the capability to generate seemingly random numbers, many computer tasks become impossible.
Finding algorithms everywhere
The previous section mentions the toast algorithm for a specific reason. For some reason, making toast is probably the most popular algorithm ever created. Many grade-school children write their equivalent of the toast algorithm long before they can even solve the most basic math. It’s not hard to imagine how many variations of the toast algorithm exist and what the precise output is of each of them. The results likely vary by individual and the level of creativity employed. There are also websites dedicated to telling children about algorithms, such as the one at https://www.idtech.com/blog/algorithms-for-kids. In short, algorithms appear in great variety and often in unexpected places.
Every task you perform on a computer involves algorithms. Some algorithms appear as part of the computer hardware. The very act of booting a computer involves the use of an algorithm. You also find algorithms in operating systems, applications, and every other piece of software. Even users rely on algorithms. Scripts help direct users to perform tasks in a specific way, but those same steps could appear as written instructions or as part of an organizational policy statement.
Daily routines often devolve into algorithms. Think about how you spend your day. If you’re like most people, you perform essentially the same tasks every day in the same order, making your day an algorithm that solves the problem of how to live successfully while expending the least amount of energy possible. After all, that’s what a routine does; it makes us efficient.
Throughout this book, you see the same three elements for every algorithm:
1 Describe the problem.
2 Create a series of steps to solve the problem (well defined).
3 Perform the steps to obtain a desired result (finite and effective).
Using Computers to Solve Problems
The term computer sounds quite technical and possibly a bit overwhelming to some people, but people today are neck deep (possibly even deeper) in computers. You wear at least one computer, your smartphone, most of the time. If you have any sort of special device, such as a pacemaker, it also includes a computer. A car can contain as many as 150 computers in the form of embedded microprocessors that regulate fuel consumption, engine combustion, transmission, steering, and stability (see https://spectrum.ieee.org/software-eating-car for details), provide Advanced Driver-Assist Systems (ADAS), and more lines of code than a jet fighter. A computer exists to solve problems quickly and with less effort than solving them manually. Consequently, it shouldn’t surprise you that this book uses still more computers to help you understand algorithms better.
Computers vary in a number of ways. The computer in a watch is quite small; the one on a desktop quite large. Supercomputers are immense and contain many smaller computers all tasked to work together to solve complex issues, such as predicting tomorrow’s weather. The most complex algorithms rely on special computer functionality to obtain solutions to the issues people design them to solve. Yes, you could use lesser resources to perform the task, but the trade-off is waiting a lot longer for an answer, or getting an answer that lacks sufficient accuracy to provide a useful solution. In some cases, you wait so long that the answer is no longer important. With the need for both speed and accuracy in mind, the following sections discuss some special computer features that can affect algorithms.
Getting the most out of modern CPUs and GPUs
General-purpose processors, CPUs, started out as a means to solve problems using algorithms. However, their general-purpose nature also means that a CPU can perform a great many other tasks, such as moving data around or interacting with external devices. A general-purpose processor does many things well, which means that it can perform the steps required to complete an algorithm, but not necessarily fast. Owners of early general-purpose processors could add math coprocessors (special math-specific chips) to their systems to gain a speed advantage (see https://www.computerhope.com/jargon/m/mathcopr.htm for details). Today, general-purpose processors have the math coprocessor embedded into them, so when you get an Intel i9 processor, you actually get multiple processors in a single package.
A GPU is a special-purpose processor with capabilities that lend themselves to faster algorithm execution. For most people, GPUs are supposed to take data, manipulate it in a special way, and then display a pretty picture onscreen. However, any computer hardware can serve more than one purpose. It turns out that GPUs are particularly adept at performing data transformations, which is a key task for solving algorithms in many cases. It shouldn’t surprise you to discover that people who create algorithms spend a lot of time thinking outside the box, which means that they often see methods of solving issues in nontraditional approaches.
The point is that CPUs and GPUs form the most commonly used chips for performing algorithm-related tasks. The first performs general-purpose tasks quite well, and the second specializes in providing support for math-intensive tasks, especially those that involve data transformations. Using multiple cores makes parallel processing (performing more than one algorithmic step at a time) possible. Adding multiple chips increases the number of cores available. Having more cores adds speed, but a number of factors keeps the speed gain to a minimum. Using two i9 chips won’t produce double the speed of just one i9 chip.
Working with special-purpose
