Benchmark of Error-Free Transforms for dot product

This benchmark compares the accuracy and efficiency of several dot product algorithms in floating point arithmetics.

def naive_dot(x, y):
    return choreo.segm.cython.eft_lib.naive_dot(x,y)

def naive_dot_ptr(x, y):
    return choreo.segm.cython.eft_lib.naive_dot_ptr(x,y)

def np_dot(x, y):
    return np.dot(x,y)

def m_sumprod(x, y):
    return m.sumprod(x, y)

def DotK_1(x, y):
    return choreo.segm.cython.eft_lib.DotK(x,y,1)

def DotK_2(x, y):
    return choreo.segm.cython.eft_lib.DotK(x,y,2)

def DotK_3(x, y):
    return choreo.segm.cython.eft_lib.DotK(x,y,3)

def DotK_4(x, y):
    return choreo.segm.cython.eft_lib.DotK(x,y,4)

def DotK_5(x, y):
    return choreo.segm.cython.eft_lib.DotK(x,y,5)

def prepare_x(n):
    x = np.random.random(n)
    y = np.random.random(n)
    return {'x': x, 'y': y}


pyquickbench.plot_benchmark(
    all_times   ,
    all_sizes   ,
    all_funs    ,
    show = True ,
    title = "Measured CPU time"   ,
)

pyquickbench.plot_benchmark(
    all_times   ,
    all_sizes   ,
    all_funs    ,
    show = True ,
    relative_to_val = {pyquickbench.fun_ax_name: "naive_dot"},
    title = "Relative measured CPU time compared to naive_dot"   ,
)