But it's not equally exposed to the programmer. The branch predictor in an Intel CPU may be insanely complex but when writing code you usually don't need to care. On the other hand if you want to use a GPU you have to care about a huge amount of complexity right from the start.
Except you really do need to care about this complexity on CPUs. Things like cache locality & predictable access patterns are critical to achieving good CPU performance. This is why there's things like data-oriented design, SoA vs. AoS, and Z-order curves. It's also why linked-lists are so incredibly awful in practice, despite having superb algorithmic performance in theory.
A big reason programming for CPUs doesn't seem as complex is because the vast, vast majority of time nobody actually cares about CPU performance. We all just prefer to pretend a runtime or JIT or compiler managed to magically make a language that's god-awful horrendous on modern CPUs run fast. They didn't, we just all look the other way though.
The difference between CPUs & GPUs is when people reach for GPUs, such as for games or HPC, those are also the people that care a lot about performance. And guides like this are for them.
modeless|5 years ago
kllrnohj|5 years ago
A big reason programming for CPUs doesn't seem as complex is because the vast, vast majority of time nobody actually cares about CPU performance. We all just prefer to pretend a runtime or JIT or compiler managed to magically make a language that's god-awful horrendous on modern CPUs run fast. They didn't, we just all look the other way though.
The difference between CPUs & GPUs is when people reach for GPUs, such as for games or HPC, those are also the people that care a lot about performance. And guides like this are for them.