What happens when including numba or pytorch, etc in the scripts? GPU acceleration in python is one really nice way of getting decent speed, but I would imagine it's difficult to shuffle over when doing this type of compiling. If the end compiled program allows for use of all available computational resources (some logic with python to determine what accelerations to allocate, what is available, etc) and then can compile to C++ speeds for CPU and use GPU where appropriate, this will be astoundingly good.
Right we support (currently a subset) of NumPy (just `from numpy import ...`) and SymPy (`from sympy import ...`) and some parts of the Python standard library. We want to support PyTorch, CuPy and other such libraries in a similar way, at least the subset that can be ahead of time compiled, which is quite large.
Yes, offloading to GPU we want to support naturally via NumPy syntax. We will look at this very soon, most likely via annotating that a given array lives on a GPU or host, and then array copy will copy it from host to device, etc.
Comments
What happens when including numba or pytorch, etc in the scripts? GPU acceleration in python is one really nice way of getting decent speed, but I would imagine it's difficult to shuffle over when doing this type of compiling. If the end compiled program allows for use of all available computational resources (some logic with python to determine what accelerations to allocate, what is available, etc) and then can compile to C++ speeds for CPU and use GPU where appropriate, this will be astoundingly good.
Right we support (currently a subset) of NumPy (just `from numpy import ...`) and SymPy (`from sympy import ...`) and some parts of the Python standard library. We want to support PyTorch, CuPy and other such libraries in a similar way, at least the subset that can be ahead of time compiled, which is quite large.
Yes, offloading to GPU we want to support naturally via NumPy syntax. We will look at this very soon, most likely via annotating that a given array lives on a GPU or host, and then array copy will copy it from host to device, etc.