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Since when does C++ optimally exploit the underlying hardware? It has no vector instructions, does not run on the GPU and is arguably too hard to make multithre

Since when does C++ optimally exploit the underlying hardware? It has no vector instructions, does not run on the GPU and is arguably too hard to make multithreaded. Which leaves you with about 0.5% performance of a current PCs.

jcelerier 3y ago
> does not run on the GPU both Cuda and the Metal shader language are C++, so is OpenCL since 2.0 (https://www.khronos.org/opencl/ https://www.khronos.org/opencl/), so is AMD ROCm's HIP (https://github.com/ROCm-Developer-Tools/HIP https://github.com/ROCm-Developer-Tools/HIP), so is SYCL (https://www.khronos.org/sycl/ https://www.khronos.org/sycl/)? C++ is pretty much the language that runs most on GPUs. > no vector instructions, There's a thousand different possibilities for SIMD in C++, from #pragma omp simd, to libs such as std::experimental::simd (https://en.cppreference.com/w/cpp/experimental/simd/simd https://en.cppreference.com/w/cpp/experimental/simd/simd), Eve (https://github.com/jfalcou/eve https://github.com/jfalcou/eve), Highway (https://github.com/google/highway https://github.com/google/highway), Vc (https://github.com/VcDevel/Vc https://github.com/VcDevel/Vc)...
waynecochran 3y ago
Vector types / instructions would be nice. The C++20 STL algorithms are very friendly to vectorization with the various parallel policies (e.g. std::execution::unsequenced_policy) that open up your code to be vectorized. Wonderful libs like Eigen handle a lot of my numeric needs for linear algebra. I think you are forgetting the CUDA is C/C++.
- Someone 3y ago
  > Vector types / instructions would be nice It’s technically not a C++ feature, but both gcc (https://gcc.gnu.org/onlinedocs/gcc/Vector-Extensions.html https://gcc.gnu.org/onlinedocs/gcc/Vector-Extensions.html) and Clang (https://releases.llvm.org/3.1/tools/clang/docs/LanguageExtensions.html#vectors https://releases.llvm.org/3.1/tools/clang/docs/LanguageExten...) have vector types, and clang even supports the gcc way of writing them, so it gets pretty close.
  - astrange 3y ago
    Those are traditionally dangerous since they tend to compile poorly; not as bad as autovectorization but not as good as just writing in assembly. And since vectorization is platform-dependent anyway (because it's so different across platforms), assembly really isn't nearly as bad as it sounds. Though it's certainly gotten better, the reason people push those is that they're written by compiler authors, who don't want to hear that their compiler doesn't work. Some of the reason for this is that C doesn't let you specify memory aliasing as precisely as you want to. Fortran is better about this.
pjmlp 3y ago
When compared against Python, more than enough. C++ is one of the supported CUDA languages, even standard C++17 does run just fine on the GPU. Metal uses C++14 alongside some extensions.

jcelerier 3y ago

> does not run on the GPU both Cuda and the Metal shader language are C++, so is OpenCL since 2.0 (https://www.khronos.org/opencl/ https://www.khronos.org/opencl/), so is AMD ROCm's HIP (https://github.com/ROCm-Developer-Tools/HIP https://github.com/ROCm-Developer-Tools/HIP), so is SYCL (https://www.khronos.org/sycl/ https://www.khronos.org/sycl/)? C++ is pretty much the language that runs most on GPUs. > no vector instructions, There's a thousand different possibilities for SIMD in C++, from #pragma omp simd, to libs such as std::experimental::simd (https://en.cppreference.com/w/cpp/experimental/simd/simd https://en.cppreference.com/w/cpp/experimental/simd/simd), Eve (https://github.com/jfalcou/eve https://github.com/jfalcou/eve), Highway (https://github.com/google/highway https://github.com/google/highway), Vc (https://github.com/VcDevel/Vc https://github.com/VcDevel/Vc)...

waynecochran 3y ago

Vector types / instructions would be nice. The C++20 STL algorithms are very friendly to vectorization with the various parallel policies (e.g. std::execution::unsequenced_policy) that open up your code to be vectorized. Wonderful libs like Eigen handle a lot of my numeric needs for linear algebra. I think you are forgetting the CUDA is C/C++.

Someone 3y ago
> Vector types / instructions would be nice It’s technically not a C++ feature, but both gcc (https://gcc.gnu.org/onlinedocs/gcc/Vector-Extensions.html https://gcc.gnu.org/onlinedocs/gcc/Vector-Extensions.html) and Clang (https://releases.llvm.org/3.1/tools/clang/docs/LanguageExtensions.html#vectors https://releases.llvm.org/3.1/tools/clang/docs/LanguageExten...) have vector types, and clang even supports the gcc way of writing them, so it gets pretty close.
- astrange 3y ago
  Those are traditionally dangerous since they tend to compile poorly; not as bad as autovectorization but not as good as just writing in assembly. And since vectorization is platform-dependent anyway (because it's so different across platforms), assembly really isn't nearly as bad as it sounds. Though it's certainly gotten better, the reason people push those is that they're written by compiler authors, who don't want to hear that their compiler doesn't work. Some of the reason for this is that C doesn't let you specify memory aliasing as precisely as you want to. Fortran is better about this.

pjmlp 3y ago

When compared against Python, more than enough. C++ is one of the supported CUDA languages, even standard C++17 does run just fine on the GPU. Metal uses C++14 alongside some extensions.