5 ms·

> but a lot of primitive stuff in languages like python is also just C calls and may be optimised to the point of being faster That assertion makes zero sense

by barumi 6y ago

> but a lot of primitive stuff in languages like python is also just C calls and may be optimised to the point of being faster 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++.

quietbritishjim 6y ago
The overwhelming majority of C extensions release the Python GIL during calls to them. If you're in the situation described in the parent comment then you can parallelize just fine using threads.
- deleted 6y ago
  [deleted]
- barumi 6y ago
  > The overwhelming majority of C extensions release the Python GIL during calls to them. 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.
  - quietbritishjim 6y ago
    The comment that started this discussion is about the specific case that most of your execution time is in libraries rather than your application code. In that Python is usually no slower to execute than C/C++. You disagreed with that by claiming that such programs cannot be multi threaded because of that GIL, and I just corrected that because it's untrue (edit: it's untrue in general, but especially untrue in this situation we're talking about). 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?
jnxx 6y ago
> That assertion makes zero sense if we take into account that parallelization is one of the most basic performance techniques there is 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++.
- pumpkinpal 6y ago
  Its funny that you mentioned games as an example of parallel computation because I'd argue they're some of the hardest programs to parallelize effectively since they don't generally involve that much bulk-processing of read-only data. 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.
pumpkinpal 6y ago
Python and C++ are joined at the hip now primarily due to scientific computing and AI/ML adjacent fields. I feel the growth of Python has helped C++ grow too.

by barumi 6y ago

quietbritishjim 6y ago

The overwhelming majority of C extensions release the Python GIL during calls to them. If you're in the situation described in the parent comment then you can parallelize just fine using threads.

deleted 6y ago
[deleted]
barumi 6y ago
> The overwhelming majority of C extensions release the Python GIL during calls to them. 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.
- quietbritishjim 6y ago
  The comment that started this discussion is about the specific case that most of your execution time is in libraries rather than your application code. In that Python is usually no slower to execute than C/C++. You disagreed with that by claiming that such programs cannot be multi threaded because of that GIL, and I just corrected that because it's untrue (edit: it's untrue in general, but especially untrue in this situation we're talking about). 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?

jnxx 6y ago

> That assertion makes zero sense if we take into account that parallelization is one of the most basic performance techniques there is 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++.

pumpkinpal 6y ago
Its funny that you mentioned games as an example of parallel computation because I'd argue they're some of the hardest programs to parallelize effectively since they don't generally involve that much bulk-processing of read-only data. 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.

pumpkinpal 6y ago

Python and C++ are joined at the hip now primarily due to scientific computing and AI/ML adjacent fields. I feel the growth of Python has helped C++ grow too.