Anaconda Accelerate dot product is 2x slower than plain NumPy

Question

Why does Anaconda Accelerate compute dot products slower than plain NumPy on Python 3? I'm using accelerate version 2.3.1 with accelerate_cudalib 2.0 installed, Python 3.5.2 Windows 10 64-bit.

import numpy as np
from accelerate.cuda.blas import dot as gpu_dot
import time

def numpydot():
    start= time.time()
    for i in range(100):
        np.dot(np.arange(1000000, dtype=np.float64), np.arange(1000000, dtype=np.float64))
    elapsedtime = time.time()-start
    return elapsedtime

def acceleratedot():
    start= time.time()
    for i in range(100):
        gpu_dot(np.arange(1000000, dtype=np.float64), np.arange(1000000, dtype=np.float64))
    elapsedtime = time.time()-start
    return elapsedtime


numpydot()
0.6446375846862793
acceleratedot()
1.33168363571167

Answer 1

I figured out that shared arrays are created with Numba, a separate library. They have the documentation on their site.