cuPy is a useful, and kind of large, library which does a lot of things. In your example, you use it to create buffers, fill them up with random values, and perform elementwise arithmetic on them. numpy does that, which is why cuPy does that. My library only wraps CUDA functionality, and mostly "does nothing" [1] - so you have to "do everything yourself", except that it's easy(ish) to do so. It definitely never does anything behind-the-scenes or behind-your-back.
This difference between the libraries makes your program more terse; however, you lose control over where your buffers are, from where they're accessible, when they get copied around and how etc. You can't even tell - from looking at the program source - whether the buffers will be "managed memory" accessed and copied page-by-page, or rather a copy will be made from system memory to device-global memory.
So, in my book, it is not as easy to access and control CUDA with cuPy. But - it is easier for a user who "needs numpy for GPUs", and does not care about the nitty-gritty, to write their program and get things done. Your program demostrates both of these points.
I should mention that I wrote my library with the hope that others will use it to build higher-level-abstraction libraries and apps. One could use it to create a cuCpp library that would be very numpy-like but for C++, a parallel of NumCpp [2].
Thanks for the tips regarding the README, I'll fix it up.
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[1] : cuda-api-wrappers does offer a couple of utility classes like a poor man's span for pre-C++17, and a span+unique_ptr combo - which is beyond wrapping CUDA's APIs, but still doesn't quite "do" thing.
> It definitely never does anything behind-the-scenes or behind-your-back.
Actually that's a bit of a lie, because of the economy of primary device context reference counts (it's quite annoying if you need to do it well and not leak resources) and the context stack. So, let's say it does as little as possible behind the scenes... :-(
This difference between the libraries makes your program more terse; however, you lose control over where your buffers are, from where they're accessible, when they get copied around and how etc. You can't even tell - from looking at the program source - whether the buffers will be "managed memory" accessed and copied page-by-page, or rather a copy will be made from system memory to device-global memory.
So, in my book, it is not as easy to access and control CUDA with cuPy. But - it is easier for a user who "needs numpy for GPUs", and does not care about the nitty-gritty, to write their program and get things done. Your program demostrates both of these points.
I should mention that I wrote my library with the hope that others will use it to build higher-level-abstraction libraries and apps. One could use it to create a cuCpp library that would be very numpy-like but for C++, a parallel of NumCpp [2].
Thanks for the tips regarding the README, I'll fix it up.
----
[1] : cuda-api-wrappers does offer a couple of utility classes like a poor man's span for pre-C++17, and a span+unique_ptr combo - which is beyond wrapping CUDA's APIs, but still doesn't quite "do" thing.
[2]: https://github.com/dpilger26/NumCpp