Centre Blaise Pascal » Bench4GPU

#!/usr/bin/env python3

import numpy as np

import pyopencl as cl

from numpy import pi,cos,sin

# piling 16 arithmetical functions

def MySillyFunction(x):

    return(np.power(np.sqrt(np.log(np.exp(np.arctanh(np.tanh(np.arcsinh(np.sinh(np.arccosh(np.cosh(np.arctan(np.tan(np.arcsin(np.sin(np.arccos(np.cos(x))))))))))))))),2))

# Native Operation under Numpy (for prototyping & tests

def NativeAddition(a_np,b_np):

    return(a_np+b_np)

# Native Operation with MySillyFunction under Numpy (for prototyping & tests

def NativeSillyAddition(a_np,b_np):

    return(MySillyFunction(a_np)+MySillyFunction(b_np))

# Naive Discrete Fourier Transform

def MyDFT(x,y):

    from numpy import pi,cos,sin

    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)

    Y=np.zeros(size)

    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)

# CUDA complete operation

def CUDAAddition(a_np,b_np):

    import pycuda.autoinit

    import pycuda.driver as drv

    import numpy

    from pycuda.compiler import SourceModule

    mod = SourceModule("""

    __global__ void sum(float *dest, float *a, float *b)

{

  // const int i = threadIdx.x;

  const int i = blockIdx.x;

  dest[i] = a[i] + b[i];

}

""")

    # sum = mod.get_function("sum")

    sum = mod.get_function("sum")

    res_np = numpy.zeros_like(a_np)

    sum(drv.Out(res_np), drv.In(a_np), drv.In(b_np),

        block=(1,1,1), grid=(a_np.size,1))

    return(res_np)

# CUDA Silly complete operation

def CUDASillyAddition(a_np,b_np):

    import pycuda.autoinit

    import pycuda.driver as drv

    import numpy

    from pycuda.compiler import SourceModule

    TimeIn=time.time()

    mod = SourceModule("""

__device__ float MySillyFunction(float x)

{

    return(pow(sqrt(log(exp(atanh(tanh(asinh(sinh(acosh(cosh(atan(tan(asin(sin(acos(cos(x))))))))))))))),2));

}

__global__ void sillysum(float *dest, float *a, float *b)

{

  const int i = blockIdx.x;

  dest[i] = MySillyFunction(a[i]) + MySillyFunction(b[i]);

}

""")

    Elapsed=time.time()-TimeIn

    print("Definition of kernel : %.3f" % Elapsed)

    TimeIn=time.time()

    # sum = mod.get_function("sum")

    sillysum = mod.get_function("sillysum")

    Elapsed=time.time()-TimeIn

    print("Synthesis of kernel : %.3f" % Elapsed)

    TimeIn=time.time()

    res_np = numpy.zeros_like(a_np)

    Elapsed=time.time()-TimeIn

    print("Allocation on Host for results : %.3f" % Elapsed)

    TimeIn=time.time()

    sillysum(drv.Out(res_np), drv.In(a_np), drv.In(b_np),

             block=(1,1,1), grid=(a_np.size,1))

    Elapsed=time.time()-TimeIn

    print("Execution of kernel : %.3f" % Elapsed)

    return(res_np)

# OpenCL complete operation

def OpenCLAddition(a_np,b_np):

    # Context creation

    ctx = cl.create_some_context()

    # Every process is stored in a queue

    queue = cl.CommandQueue(ctx)

    TimeIn=time.time()

    # Copy from Host to Device using pointers

    mf = cl.mem_flags

    a_g = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=a_np)

    b_g = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=b_np)

    Elapsed=time.time()-TimeIn

    print("Copy from Host 2 Device : %.3f" % Elapsed)

    TimeIn=time.time()

    # Definition of kernel under OpenCL

    prg = cl.Program(ctx, """

__kernel void sum(

    __global const float *a_g, __global const float *b_g, __global float *res_g)

{

  int gid = get_global_id(0);

  res_g[gid] = a_g[gid] + b_g[gid];

}

""").build()

    Elapsed=time.time()-TimeIn

    print("Building kernels : %.3f" % Elapsed)

    TimeIn=time.time()

    # Memory allocation on Device for result

    res_g = cl.Buffer(ctx, mf.WRITE_ONLY, a_np.nbytes)

    Elapsed=time.time()-TimeIn

    print("Allocation on Device for results : %.3f" % Elapsed)

    TimeIn=time.time()

    # Synthesis of function "sum" inside Kernel Sources

    knl = prg.sum  # Use this Kernel object for repeated calls

    Elapsed=time.time()-TimeIn

    print("Synthesis of kernel : %.3f" % Elapsed)

    TimeIn=time.time()

    # Call of kernel previously defined

    knl(queue, a_np.shape, None, a_g, b_g, res_g)

    Elapsed=time.time()-TimeIn

    print("Execution of kernel : %.3f" % Elapsed)

    TimeIn=time.time()

    # Creation of vector for result with same size as input vectors

    res_np = np.empty_like(a_np)

    Elapsed=time.time()-TimeIn

    print("Allocation on Host for results: %.3f" % Elapsed)

    TimeIn=time.time()

    # Copy from Device to Host

    cl.enqueue_copy(queue, res_np, res_g)

    Elapsed=time.time()-TimeIn

    print("Copy from Device 2 Host : %.3f" % Elapsed)

    return(res_np)

# OpenCL complete operation

def OpenCLSillyAddition(a_np,b_np):

    # Context creation

    ctx = cl.create_some_context()

    # Every process is stored in a queue

    queue = cl.CommandQueue(ctx)

    TimeIn=time.time()

    # Copy from Host to Device using pointers

    mf = cl.mem_flags

    a_g = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=a_np)

    b_g = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=b_np)

    Elapsed=time.time()-TimeIn

    print("Copy from Host 2 Device : %.3f" % Elapsed)

    TimeIn=time.time()

    # Definition of kernel under OpenCL

    prg = cl.Program(ctx, """

float MySillyFunction(float x)

{

    return(pow(sqrt(log(exp(atanh(tanh(asinh(sinh(acosh(cosh(atan(tan(asin(sin(acos(cos(x))))))))))))))),2));

}

__kernel void sillysum(

    __global const float *a_g, __global const float *b_g, __global float *res_g)

{

  int gid = get_global_id(0);

  res_g[gid] = MySillyFunction(a_g[gid]) + MySillyFunction(b_g[gid]);

}

__kernel void sum(

    __global const float *a_g, __global const float *b_g, __global float *res_g)

{

  int gid = get_global_id(0);

  res_g[gid] = a_g[gid] + b_g[gid];

}

""").build()

    Elapsed=time.time()-TimeIn

    print("Building kernels : %.3f" % Elapsed)

    TimeIn=time.time()

    # Memory allocation on Device for result

    res_g = cl.Buffer(ctx, mf.WRITE_ONLY, a_np.nbytes)

    Elapsed=time.time()-TimeIn

    print("Allocation on Device for results : %.3f" % Elapsed)

    TimeIn=time.time()

    # Synthesis of function "sillysum" inside Kernel Sources

    knl = prg.sillysum  # Use this Kernel object for repeated calls

    Elapsed=time.time()-TimeIn

    print("Synthesis of kernel : %.3f" % Elapsed)

    TimeIn=time.time()

    # Call of kernel previously defined

    CallCL=knl(queue, a_np.shape, None, a_g, b_g, res_g)

    #

    CallCL.wait()

    Elapsed=time.time()-TimeIn

    print("Execution of kernel : %.3f" % Elapsed)

    TimeIn=time.time()

    # Creation of vector for result with same size as input vectors

    res_np = np.empty_like(a_np)

    Elapsed=time.time()-TimeIn

    print("Allocation on Host for results: %.3f" % Elapsed)

    TimeIn=time.time()

    # Copy from Device to Host

    cl.enqueue_copy(queue, res_np, res_g)

    Elapsed=time.time()-TimeIn

    print("Copy from Device 2 Host : %.3f" % Elapsed)

    return(res_np)

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=50000

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

    # a_np = np.random.rand(SIZE).astype(np.float32)

    # b_np = np.random.rand(SIZE).astype(np.float32)

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

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

    # 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)

    # 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(np.linalg.norm(c_np-e_np))

    print(np.linalg.norm(d_np-f_np))

    # Native & Numpy Implementation

    print("Performing Numba implementation")

    TimeIn=time.time()

    g_np,h_np=NumbaDFT(a_np,b_np)

    NumpyElapsed=time.time()-TimeIn

    NumpyRate=int(SIZE/NumpyElapsed)

    print("NumpyRate: %i" % NumpyRate)

    print(np.linalg.norm(c_np-g_np))

    print(np.linalg.norm(d_np-h_np))

   #  # OpenCL Implementation

   #  TimeIn=time.time()

   #  # res_cl=OpenCLAddition(a_np,b_np)

   #  res_cl=OpenCLSillyAddition(a_np,b_np)

   #  OpenCLElapsed=time.time()-TimeIn

   #  OpenCLRate=int(SIZE/OpenCLElapsed)

   #  print("OpenCLRate: %i" % OpenCLRate)

   #  # CUDA Implementation

   #  TimeIn=time.time()

   #  # res_cuda=CUDAAddition(a_np,b_np)

   #  res_cuda=CUDASillyAddition(a_np,b_np)

   #  CUDAElapsed=time.time()-TimeIn

   #  CUDARate=int(SIZE/CUDAElapsed)

   #  print("CUDARate: %i" % CUDARate)

   #  print("OpenCLvsNative ratio: %f" % (OpenCLRate/NativeRate))

   #  print("CUDAvsNative ratio: %f" % (CUDARate/NativeRate))

   # # Check on OpenCL with Numpy:

   #  print(res_cl - res_np)

   #  print(np.linalg.norm(res_cl - res_np))

   #  try:

   #      assert np.allclose(res_np, res_cl)

   #  except:

   #      print("Results between Native & OpenCL seem to be too different!")

   #  # Check on CUDA with Numpy:

   #  print(res_cuda - res_np)

   #  print(np.linalg.norm(res_cuda - res_np))

   #  try:

   #      assert np.allclose(res_np, res_cuda)

   #  except:

   #      print("Results between Native & CUDA seem to be too different!")