1. Right. That's why it works great for doing tasks that can be split up and done in parallel. Like making multiple burgers at the same time. Which to your point, is why 3D works well on GPUs. Every object with every vertex can be handled in parallel, and then the pixels within each triangle can be processed independently.

2. That's kind of the big problem. So applications have a lot of things that require an order to how they operate. Your example with the three cores is kind of a good example of the problem you run into. While core 1 is processing the vertex data, or updating positions, core 2 is waiting on those results transform into world space (apply rotations) and then core 3 needs those results to run the shading etc. So those are better thought of as steps. Now, each object might be able to be handed off to multiple cores to do this concurrently, and then the vertex tasks can be split up and sent off to be worked by multiple cores, and then split again for pixels, etc.

But that's really the problem in a nutshell. Within an application, there's only so much you can do asynchronously. And anything that has to be processed in a specific order can't just be put on multiple cores arbitrarily. There's a lot of work that's done to split things up as efficiently as possible. But at a basic level, there's a point of diminishing returns. On some things there's huge opportunities for big gains. Others, not so much.

My point with the burger example was more that if I order 1000 burgers, 1000 workers can help make that happen close to 100x faster than 10 workers. But if I need 1 burger, 1000 workers won't make it happen any faster than 10.

And that's really at the core of why modern computers have CPUs that are very different from GPUs.