第5章アルゴリズムのハードウェアによる実現：コンピューターシステム設計

By the end of this section, you will be able to:

• Describe abstraction levels from the highest to the lowest

• Explain application programs abstractions in relation to HLLs and instruction set architectures

• Discuss processor abstractions and how microarchitecture supports them

• Identify the role of the operating system within abstraction

• Discuss examples of new disruptive computer systems

When you look around, you see that complex systems can be viewed as layers of abstractions. The removal of unimportant elements of a program or computer code that distract from its process is called abstraction. This way of looking at complex systems makes it easier to understand them. For example, cars are very complicated inventions. At the highest level of abstraction, we look at a car as a set of devices used to operate it such as a steering wheel, brake and accelerator pedals, and so on. If we go to a lower level, we see devices that power the car such as its engine, gears, and spark plugs. If we take these parts and look at how they are designed, then we are at an even lower level where we see metals, plastics, and other materials. The same approach can be applied to computers to understand how they work. We can see computers as several layers of abstractions, as shown in Figure 5.5. For the remaining part of this section, we start from the highest level and then work through each abstraction layer to illustrate how it is used as a building block by the layer before it.

Computers are just a tool used to solve a problem. You may use computers to play games or listen to music, and in these cases, the problems that the computer is trying to solve are associated with programs that you use for entertainment purposes.

The top line in Figure 5.5 starts with the problem; we must have a very precise definition of the problem we are trying to solve with a computer. So, the first step in solving a problem with a computer is to know exactly, and with no vagueness, what we are trying to solve. You cannot make a computer solve a problem unless there is a defined and repeatable set of instructions to solve the problem. You may wonder then why it is necessary to use a computer in the first place if you can solve the problem yourself. Well, computers do not get bored, are precise, and can deal with very large problems. This is why, the next step after problem definition is to lay out the steps for solving the problem. This solution layout is called an algorithm. The algorithm is written in free format; that is, it can be steps written as a bulleted list, it can be a flowchart, or it can be a series of mathematical equations. Regardless of the format you choose for writing the algorithm, the algorithm needs to have a key set of characteristics.

The first characteristic is that an algorithm must be unambiguous. Each step of the algorithm must be very well defined and precise. The second characteristic is that the algorithm must be deterministic to be reproducible and repeatable so that the same set of inputs produce the same output. The third characteristic is that the algorithm, when implemented on a computer, must consume a reasonable amount of time and storage based on the problem needs. For example, an algorithm can be finite and precise, but if it requires 100 years to generate a result, it is clearly useless. For instance, an algorithm that counts the number of even numbers is not finite because we have an infinite number of even numbers.

Assuming we have an algorithm, we then need to prepare it for execution on the computer. First, we must prepare the input before it is ready to be consumed by the computer, so we give the algorithm to a programmer whose job it is to read the algorithm, understand it, and then write a program in a known computer language such as C/C++ or Python. The program tells the computer what to do, but in a formal way rather than a freeform way as an algorithm. At that point, we move to another level of abstraction.