Centre Blaise Pascal » Bench4GPU

#!/usr/bin/env python3

import numpy as np

import pyopencl as cl

from numpy import pi,cos,sin

# Naive Discrete Fourier Transform

def MyDFT(x,y):

    size=x.shape[0]

    X=np.zeros(size).astype(np.float32)

    Y=np.zeros(size).astype(np.float32)

    for i in range(size):

        for j in range(size):

            X[i]=X[i]+x[j]*cos(2.*pi*i*j/size)-y[j]*sin(2.*pi*i*j/size)

            Y[i]=Y[i]+x[j]*sin(2.*pi*i*j/size)+y[j]*cos(2.*pi*i*j/size)

    return(X,Y)

# Numpy Discrete Fourier Transform

def NumpyDFT(x,y):

    size=x.shape[0]

    X=np.zeros(size).astype(np.float32)

    Y=np.zeros(size).astype(np.float32)

    nj=np.multiply(2.0*np.pi/size,np.arange(size)).astype(np.float32)

    for i in range(size):

        X[i]=np.sum(np.subtract(np.multiply(np.cos(i*nj),x),np.multiply(np.sin(i*nj),y)))

        Y[i]=np.sum(np.add(np.multiply(np.sin(i*nj),x),np.multiply(np.cos(i*nj),y)))

    return(X,Y)

# Numba Discrete Fourier Transform

import numba

@numba.njit(parallel=True)

def NumbaDFT(x,y):

    size=x.shape[0]

    X=np.zeros(size).astype(np.float32)

    Y=np.zeros(size).astype(np.float32)

    nj=np.multiply(2.0*np.pi/size,np.arange(size)).astype(np.float32)

    for i in numba.prange(size):

        X[i]=np.sum(np.subtract(np.multiply(np.cos(i*nj),x),np.multiply(np.sin(i*nj),y)))

        Y[i]=np.sum(np.add(np.multiply(np.sin(i*nj),x),np.multiply(np.cos(i*nj),y)))

    return(X,Y)

import sys

import time

if __name__=='__main__':

    # Size of input vectors definition based on stdin

    import sys

    try:

        SIZE=int(sys.argv[1])

        print("Size of vectors set to %i" % SIZE)

    except:

        SIZE=256

        print("Size of vectors set to default size %i" % SIZE)

    a_np = np.ones(SIZE).astype(np.float32)

    b_np = np.ones(SIZE).astype(np.float32)

    C_np = np.zeros(SIZE).astype(np.float32)

    D_np = np.zeros(SIZE).astype(np.float32)

    C_np[0] = np.float32(SIZE)

    D_np[0] = np.float32(SIZE)

    # Native & Naive Implementation

    print("Performing naive implementation")

    TimeIn=time.time()

    c_np,d_np=MyDFT(a_np,b_np)

    NativeElapsed=time.time()-TimeIn

    NativeRate=int(SIZE/NativeElapsed)

    print("NativeRate: %i" % NativeRate)

    print("Precision: ",np.linalg.norm(c_np-C_np),np.linalg.norm(d_np-D_np))

    # Native & Numpy Implementation

    print("Performing Numpy implementation")

    TimeIn=time.time()

    e_np,f_np=NumpyDFT(a_np,b_np)

    NumpyElapsed=time.time()-TimeIn

    NumpyRate=int(SIZE/NumpyElapsed)

    print("NumpyRate: %i" % NumpyRate)

    print("Precision: ",np.linalg.norm(e_np-C_np),np.linalg.norm(f_np-D_np))

    # Native & Numba Implementation

    print("Performing Numba implementation")

    TimeIn=time.time()

    g_np,h_np=NumbaDFT(a_np,b_np)

    NumbaElapsed=time.time()-TimeIn

    NumbaRate=int(SIZE/NumbaElapsed)

    print("NumbaRate: %i" % NumbaRate)

    print("Precision: ",np.linalg.norm(g_np-C_np),np.linalg.norm(h_np-D_np))