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Now you should be able to run make, and then follow the above example with vector_add_cuda to add two Numpy arrays together. Since it is CUDA related, maybe start at your writeable CUDA samples directory (as you were directed to make from the read-only copy), and make your project there, copying existing sample code /makefile examples. You will get better support on a site dedicated to C support. Here we track down all the dependencies we need and configure the script. This is not really an Ubuntu problem, but a C/makefile syntax issue. format ( CUDA_PATH )) exit ( 0 ) setup ( name = 'myModule', version = '1.0', \Įxt_modules =, include_dirs =, libraries =, library_dirs = )])īasically, we need to include the CUDA headers and link dynamically with the libcudart.so library.
#Cuda c makefile example update#
Please update the CUDA_PATh variable and rerun". Static PyObject * vector_add ( PyObject * self, PyObject * args ) not found. Now what does that mean for us? Let’s try writing a function to parse two Numpy float arrays and add them together (same as the addition function in Numpy): An excellent explanation can be found here! For 2-dimensional arrays, this can be more complicated, depending on whether the array is stored in row-major (the default) or column-major order. For a one-dimensional array, the stride will just be a one entry tuple whose size is the size of the datatype (8 in the case of doubles, 4 for 32bit floats, 4 for integers, etc). While we don’t need to get into the weeds, the stride is a tuple which specifies how many bytes you need to step along each axis to get to the next element, say from arr to arr or arr to arr. If you will issue a command make clean then it removes all the object files and core files available in the current directory. Now you can build your program hello using the make. This program consists of three files main.cpp, factorial.cpp and hello.cpp.
#Cuda c makefile example how to#
Every Numpy object is internally a PyArrayObject, with a data pointer, a shape tuple (think arr.shape in Numpy), and a stride tuple that defines how to access the nth entry along each axis. This is an example of the Makefile for compiling the hello program. We’re going to dive right away into how to parse Numpy arrays in C and use CUDA to speed up our computations. A full Github repository containing all this code can be found here. This is Part 2 of a series on the Python C API and CUDA/Numpy integration.