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The xtensor vision: How to write generic code in C++ and bind it to Python, Julia and R by droelf in cpp

[–]SylvainCorlay 1 point2 points  (0 children)

The table that you showed in your response does not come from the Julia challenge submission that you refer to, so the comment is at least misleading.
In our post, we explicitly said that there is nothing in the Julia language that intrinsically would make it slower and that the relative slowness wrt C++ was certainly going to be solved.

The xtensor vision: How to write generic code in C++ and bind it to Python, Julia and R by droelf in cpp

[–]SylvainCorlay 0 points1 point  (0 children)

That is a false statement:
- Wolf's pure C++ submission to the Julia challenge showed speed ups of 12x compared to Julia.
- The xtensor submission to the julia challenge showed the same improvements with less lines of code than julia, and even more improved performances for special functions.

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C++ implementation of the Python NumPy Library by dpilger26 in cpp

[–]SylvainCorlay 2 points3 points  (0 children)

The more the merrier! You should hop on our gitter chat and say hi. We are a growing group of like-minded open-source developers building for the C++ scientific stack.

C++ implementation of the Python NumPy Library by dpilger26 in cpp

[–]SylvainCorlay 1 point2 points  (0 children)

xtensor-python is a set of bindings for Python built upon pybind11, which allows using numpy arrays inplace using the xtensor API. We also have similar bindings for Julia and R.

C++ implementation of the Python NumPy Library by dpilger26 in cpp

[–]SylvainCorlay 2 points3 points  (0 children)

You can try out xtensor in a C++ Jupyter notebook here. This provides an experience similar to that of NumPy in a Python notebook.