What is the difference between PyCUDA and NumbaPro CUDA Python?
I am new to CUDA and am trying to figure out if PyCUDA (free) or NumbaPro CUDA Python (not free) would be better for me (assuming library cost is not an issue).
Both seem to require you to use their respective Python dialects. But it looks like PyCUDA requires you to write core functions in C
code, which would be more cumbersome than using NumbaPro, which seems to do all the hard work for you.
Is it really so? Will there be noticeable performance differences?
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Let's talk about each of these libraries:
PyCuda:
PyCUDA is a Python for CUDA programming environment that gives you access to the Nvidia CUDA parallel computing API from Python. PyCUDA is written in C ++ (basic level) and Python, C ++ code will run on an NVIDIA chip, and Python code to compile, execute and get results from C ++ code and automatic resource management. which make it one of the powerful CUDA libraries.
PyCUDA is slightly different from PyOpenCl and can be used to run code on a variety of platforms including Intel, AMD, NVIDIA and ATI chips. unlike PyCUDA, which can only run on NVIDIA chips:
Python + CUDA = PyCUDA
Python + OpenCL = PyOpenCL
Numba / NumbaPro:
NUMBA : NumbaPro or recently Numba (NumbaPro is deprecated and its code generation features have been ported to open source Numba.) Is an open source NumPy optimizing compiler for Python sponsored by Anaconda, Inc. It uses the wonderful LLVM compiler. framework for compiling Python syntax to machine code. Numba supports compiling Python to run on CPU or GPU hardware, and it is primarily written in Python. it is easy to install and implement.
As @Wang mentioned, Pycuda is faster than Numba.
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