If you want to crunch a lot of numbers in parallel, GPUs are your friend, and thus C/C++ (or any of the python libraries that already set these up for you).

If you are trying to run a lot of executions in parallel where you need the full instruction set of the CPU over a GPU (namely, the compare/jump operations, otherwise known as if statements, for loops, e.t.c), then you are most likely either extremely resource constrained (like writing code for a microprocessor or embedded system), at which point you will still use C/C++, or writing something like a video game, which again is C/C++/C# (or Swift for Apple).

For most every other use case, Python is simply applicable with its vast array of libraries. For example, at my work, we use FastUI with orjson for a backend that needs to handle some significant TPS. Its fast enough. Could we write the entire thing in another language and use less ECS containers/EC2 instances? Sure, and we will save on cloud costs but lose massively on developer costs.