In principle, a processor can have just one core and run multiple threads. In this case, it simply halts one thread and

The CPU can analyze such a graph of nodes and determine which instructions it can perform in parallel and where it needs to wait for the results from multiple dependent calculations before carrying on.

Apple, in contrast, is on the complete opposite end of the spectrum. Apple makes single-user devices. Lots of threads is not an advantage. Their devices are used for gaming, video editing, development, etc. They want desktops with beautiful responsive graphics and animations.

Desktop software is generally not made to utilize lots of cores. For example, computer games will likely benefit from eight cores, but something like 128 cores would be a total waste. Instead, you would want fewer but more powerful cores.

Many instructions will finish early but we cannot make their results official. We cannot commit them; otherwise, we supply the result in the wrong order. To the rest of the world, it has to look as if the instructions were carried out in the same sequence as they were issued.

Multiplication tends to be a slow process. So say it takes multiple clock cycles to perform. The second instruction will simply have to wait because its calculation depends on knowing the result that gets put into the r1 register.

That is the reason why you see ARM CPU-makers such as Ampere making CPUs such as the Altra Max which has a crazy 128 cores. This chip is specifically made for the cloud. You don’t need crazy single-core performance because in the cloud it is all about having as many threads as possible per watt to handle as many concurrent users as possible.

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It analyses the instructions by looking at the inputs to each instruction. Do the inputs depend on output from one or more other instructions? By input and output, we mean registers containing results from previous calculations.

The problem with this is that the developer has to write code to take advantage of this. Some tasks such as sever software are easy to write like this. You can imagine processing each connecting user separately. These tasks are so independent from each other that having lots of cores is an excellent choice for servers especially cloud-based services.

However, the third instruction at line 03 doesn’t depend on calculations from previous instructions. Hence an Out-of-Order processor can begin calculating this instruction in parallel.

To understand how this works, you need to understand some things about memory. Asking for data in one particular memory location is slow. But there is no difference in the delay getting 1 byte compared to getting say 128 bytes. Data is sent across what we call a databus. You can think of it as a road or pipe between memory and different parts of the CPU where data gets pushed through. In reality, it is of course just some copper tracks conducting electricity. If the databus is wide enough you can just get multiple bytes at the same time.

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Thus CPUs get a whole chunk of instructions at a time to execute. But they are written to be executed one after the other. Modern microprocessors do what we call Out-of-Order (OoO) execution.

So here is the interesting thing, Out-of-Order execution is a way to execute more instructions in parallel but without exposing that capability as multiple threads. Developers don’t have to code their software specifically to take advantage of it. Seen from the developer’s perspective it just looks like each core runs faster.

For example, the add r4, r1, 5 instruction depends on input from r1 which is produced by mul r1, r2, r3 . We can chain together these relationships into long elaborate graphs that the CPU can work through. The nodes are the instructions and the edges are the registers connecting them.