Bh107 supports user kernels, which makes it possible to implement a specialized handwritten kernel. The idea is that if you encounter a problem that you cannot implement using array programming and Bh107 cannot accelerate, you can write a kernel in C99 that calls other libraries or do the calculation itself.

OpenMP Example

In order to write and run your own kernel use bh107.user_kernel.execute():

import bh107 as bh

def fftn(ary):
    # Making sure that `ary` is complex, contiguous, and uses no offset
    ary = bh.user_kernel.make_behaving(ary, dtype=bh.complex128)
    res = bh.empty_like(a)

    # Indicates the direction of the transform you are interested in;
    # technically, it is the sign of the exponent in the transform.

    kernel = """
    #include <stdint.h>
    #include <stdlib.h>
    #include <complex.h>
    #include <fftw3.h>

    #if defined(_OPENMP)
        #include <omp.h>
        static inline int omp_get_max_threads() { return 1; }
        static inline int omp_get_thread_num()  { return 0; }
        static inline int omp_get_num_threads() { return 1; }

    void execute(double complex *in, double complex *out) {
        const int ndim = %(ndim)d;
        const int shape[] = {%(shape)s};
        const int sign = %(sign)s;


        fftw_plan p = fftw_plan_dft(ndim, shape, in, out, sign, FFTW_ESTIMATE);
        if(p == NULL) {
            printf("fftw plan fail!\\n");
    """ % {'ndim': a.ndim, 'shape': str(a.shape)[1:-1], 'sign': sign[0]}

    # Adding some extra link options to the compiler command
    cmd = bh.user_kernel.get_default_compiler_command() + " -lfftw3 -lfftw3_threads"
    bh.user_kernel.execute(kernel, [ary, res], compiler_command=cmd)
    return res

Two useful help functions when writing user kernels is bh107.user_kernel.make_behaving(), which makes that an array is of a specific data type, is contiguous, and uses no offset and bh107.user_kernel.dtype_to_c99(), which converts a Bh107/NumPy array data type into a C99 data type.

OpenCL Example

In order to use the OpenCL backend, use the tag and param of bh107.user_kernel.execute():

import bh107 as bh

kernel = """
#pragma OPENCL EXTENSION cl_khr_fp64 : enable

kernel void execute(global double *a, global double *b) {
    int i0 = get_global_id(0);
    int i1 = get_global_id(1);
    int gid = i0 * 5 + i1;
    b[gid] = a[gid] + gid;
a = bh.ones(10*5, bh.double).reshape(10,5)
res = bh.empty_like(a)
# Notice, the OpenCL backend requires global_work_size and local_work_size
bh.user_kernel.execute(kernel, [a, res],
                       param={"global_work_size": [10, 5], "local_work_size": [1, 1]})


Remember to use the OpenCL backend by setting BH_STACK=opencl.