I have a few basic questions about using Numpy with GPU (nvidia GTX 1080 Ti). I'm new to GPU, and would like to make sure I'm properly using the GPU to accelerate Numpy/Python. I searched on the internet for a while, but didn't find a simple tutorial that addressed my questions. I'd appreciate it if someone can give me some pointers:
1) Does Numpy/Python automatically detect the presence of GPU and utilize it to speed up matrix computation (e.g. numpy.multiply, numpy.linalg.inv, ... etc)? Or do I have code in a specific way to exploit the GPU for fast computation?
2) Can someone recommend a good tutorial/introductory material on using Numpy/Python with GPU (nvidia's)?
Thanks a lot!
NumPy doesn't natively support GPU s. However, there are tools and libraries to run NumPy on GPU s. Numba is a Python compiler that can compile Python code to run on multicore CPUs and CUDA-enabled GPU s. Numba also understands NumPy and generates optimized compiled code.
Still, even with that speedup ,Numpy is only running on the CPU. With consumer CPUs typically having 8 cores or less, the amount of parallel processing, and therefore the amount of speedup that can be achieved, is limited. That's where our new friend CuPy comes in!
NVIDIA's CUDA Python provides a driver and runtime API for existing toolkits and libraries to simplify GPU-based accelerated processing. Python is one of the most popular programming languages for science, engineering, data analytics, and deep learning applications.
Does Numpy/Python automatically detect the presence of GPU and utilize it to speed up matrix computation (e.g. numpy.multiply, numpy.linalg.inv, ... etc)?
No.
Or do I have code in a specific way to exploit the GPU for fast computation?
Yes. Search for Numba, CuPy, Theano, PyTorch or PyCUDA for different paradigms for accelerating Python with GPUs.
No, you can also use CuPy which has a similar interface with numpy. https://cupy.chainer.org/
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