I would not support that stance. There are areas where parallelization is helpful, like numerical weather models, graphics, and scientific number crunching, but for by far the most cases, parallelization is anything but trivial and requires a pretty deep knowledge about things like the C++ memory models, what read-write barriers, locks, and fences are, and so on. Less than 1% of C++ programmers can really handle that. And apart from that this is an area where other languages like Rust, Clojure, Scala have a strong point, because whether it is done correctly is completely implicit in a C++ program while in Rust, many errors will have the result that your program does not compile. Debugging the same mistakes in C++ will make you pull your hair.
In fact, I'd urge anyone who wants to learn principles of safe concurrent computation in C++, to learn Rust, Clojure and Scala first, they are wonderful languages with strong, highly coherent concepts, and it will make you a much better C++ programmer even if you never use them again.
Of course it is not only possible but common practice to implement parallel computation in games and such in C++, but this is not the standard application of C++.
Parellism in C++ is most often used for scientific applications and other forms of mass number crunching. It's really easy to just throw a "#pragma omp parallel for" on a loop and call it a day but of course that would also apply to C and Fortran and is somewhat limited. Parallelism libraries like Intel TBB which I'm most familiar with are very easy to use and performant. I think there's a large problem in the reluctance of educators to use libraries to teach parallelism and people always dive straight into locks, threads and atomics which are really not the way to approach parallel computing if you're looking to do parallel computing and not looking to implement parallel primitives yourself (i.e DIY tasking-system or lock-free queue)
Focusing on TBB, it facilitates efficient parallelism by providing high-level canned algorithms such as parallel_invoke, parallel_reduce, parallel_for and parallel_do which anybody who claims to know C++ should be able to use easily. It also provides a task-graph which is great for more complex processing pipelines (things like join/split, fan-in/out and queueing). If you need more low level control you operate at the task level and TBB provides customization points for that. There's other libraries out there which provide similar functionality and even the STL in C++17 provides basic parallel algorithms such as transform (equivalent of map in other langs), reduce and many others.
There are two aspects. I agree to the aspect that today, C++ is used often for such tasks.
Now, what are the reasons why C++ is used dominantly in this domain? I think more or less the only reason is performance. The good performance is what makes the authors of such libraries put up with the disadvantages of C++.
However, I think it is also true that Rust allows for a more concise and safe formulation of the computation (also Scala, for example, which serves somewhat different purposes). For scientific applications, correctness matters, and knowing that the code which compiles does not has hidden memory errors and data races is extremely valuable, because it can save a ton of time.
Also, if there are still differences between Rust and C++ performance, they are minor. In many cases, Rust is faster.
Now, if authors of scientific computing libraries were to come to the conclusion that there exist an alternative which produces at least as fast code, provides better and safer support for parallelization, and is with some learning easier to work with, why should these library authors continue to use C++?
Of course, nobody is going to ditch a large C++ project like Eigen over night and rewrite it in Rust. There is too much inertia for that. Also, GCC support is still lacking. However, one can expect that the number of new projects which use Rust is going to increase and the project which are successful there will blaze a new trail. For something like Python extension modules, users of these libraries do not need to know anything about Rust.
Also, some nitpick. C++ is used in important scientific libraries. However, many essential libraries such as Numpy are written either completely in C, or use C interfaces, because C++ does not has a stable ABI and Python uses the C ABI. This would make a switch to Rust pretty easy. In fact, I think the impulses in this domain will come first from researchers and analysts which start to write small Rust extension for Python which use the C ABI and integrate with Numpy, for example.
In that corner case you still have slow Python glue code calling fast C++ code.
It makes no sense to claim that Python is performant based on the idea that it may be used to glue together calls to performant C++ code, while ignoring the fact that not only Python forces performance restrictions on it's code but also that C++ code is quite capable of calling performant C++ code itself.
To be honest I'm not sure what your new comment is about. If you're saying that Python is not necessarily faster than C then I'm sure no one going to dispute that; in the situation we're discussing, the performance of your code is totally dependant on the efficiency of the libraries that are doing all the work, not your glue code. If you're saying that Python is slower than C when it's doing a non-trivial amount of the execution, then sure, but no one was claiming that either. Or maybe you just want to simplistically categorise languages as "performat" and "non-performant" without thinking about the specific contexts they can be used in, but that wouldn't make any sense. Did I misunderstand?
That assertion makes zero sense if we take into account that parallelization is one of the most basic performance techniques there is, and Python's GIL simply eliminates that option except for multi-process applications which then require serializing stuff back and forth.
Python is pretty much unparalleled as a glue language, and excels at putting together small exploratory scripts for data analysis and number crunching applications, but it makes no sense to present Python as a performant alternative to C++.