Centre Blaise Pascal » Bench4GPU

#!/usr/bin/env python3

import numpy as np

import pyopencl as cl

# Native Operation under Numpy (for prototyping & tests

def NativeAddition(a_np,b_np):

    return(a_np+b_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)

    # 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)

    # 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()

    # Memory allocation on Device for result

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

    # Synthesis of function "sum" inside Kernel Sources

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

    # Call of kernel previously defined 

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

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

    res_np = np.empty_like(a_np)

    # Copy from Device to Host

    cl.enqueue_copy(queue, res_np, res_g)

    return(res_np)

#if __name__=='__main__':

a_np = np.random.rand(50000).astype(np.float32)

b_np = np.random.rand(50000).astype(np.float32)

res_np=NativeAddition(a_np,b_np)

res_cl=OpenCLAddition(a_np,b_np)

# Check on CPU with Numpy:

print(res_cl - res_np)

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

assert np.allclose(res_np, res_cl)

#!/usr/bin/env python3

import numpy as np

import pyopencl as cl

a_np = np.random.rand(50000).astype(np.float32)

b_np = np.random.rand(50000).astype(np.float32)

ctx = cl.create_some_context()

queue = cl.CommandQueue(ctx)

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)

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()

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

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

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

res_np = np.empty_like(a_np)

cl.enqueue_copy(queue, res_np, res_g)

# Check on CPU with Numpy:

print(res_np - (a_np + b_np))

print(np.linalg.norm(res_np - (a_np + b_np)))

assert np.allclose(res_np, a_np + b_np)

#!/usr/bin/env python3

import numpy as np

import pyopencl as cl

# Native Operation under Numpy (for prototyping & tests

def NativeAddition(a_np,b_np):

    return(a_np+b_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)

    # 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)

    # 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()

    # Memory allocation on Device for result

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

    # Synthesis of function "sum" inside Kernel Sources

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

    # Call of kernel previously defined 

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

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

    res_np = np.empty_like(a_np)

    # Copy from Device to Host

    cl.enqueue_copy(queue, res_np, res_g)

    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)

    TimeIn=time.time()

    res_np=NativeAddition(a_np,b_np)

    NativeElapsed=time.time()-TimeIn

    NativeRate=int(SIZE/NativeElapsed)

    print("NativeRate: %i" % NativeRate)

    TimeIn=time.time()

    res_cl=OpenCLAddition(a_np,b_np)

    OpenCLElapsed=time.time()-TimeIn

    OpenCLRate=int(SIZE/OpenCLElapsed)

    print("OpenCLRate: %i" % OpenCLRate)

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

    # Check on CPU with Numpy:

    print(res_cl - res_np)

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

    assert np.allclose(res_np, res_cl)

#!/usr/bin/env python3

import numpy as np

import pyopencl as cl

# 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))

# 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)

    TimeIn=time.time()

    res_np=NativeSillyAddition(a_np,b_np)

    NativeElapsed=time.time()-TimeIn

    NativeRate=int(SIZE/NativeElapsed)

    print("NativeRate: %i" % NativeRate)

    TimeIn=time.time()

    res_cl=OpenCLSillyAddition(a_np,b_np)

    OpenCLElapsed=time.time()-TimeIn

    OpenCLRate=int(SIZE/OpenCLElapsed)

    print("OpenCLRate: %i" % OpenCLRate)

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

    # Check on CPU with Numpy:

    print(res_cl - res_np)

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

    assert np.allclose(res_np, res_cl,rtol=1e-4)

#!/usr/bin/env python3

import numpy as np

import pyopencl as cl

# 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))

# 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;

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

}

""")

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

    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)

    TimeIn=time.time()

    # res_np=NativeSillyAddition(a_np,b_np)

    res_np=NativeAddition(a_np,b_np)

    NativeElapsed=time.time()-TimeIn

    NativeRate=int(SIZE/NativeElapsed)

    print("NativeRate: %i" % NativeRate)

    TimeIn=time.time()

    # res_cl=OpenCLSillyAddition(a_np,b_np)

    res_cl=OpenCLAddition(a_np,b_np)

    OpenCLElapsed=time.time()-TimeIn

    OpenCLRate=int(SIZE/OpenCLElapsed)

    print("OpenCLRate: %i" % OpenCLRate)

    TimeIn=time.time()

    # res_cl=OpenCLSillyAddition(a_np,b_np)

    res_cuda=CUDAAddition(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 CPU with Numpy:

    print(res_cl - res_np)

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

    assert np.allclose(res_np, res_cl)

    # Check on CPU with Numpy:

    print(res_cuda - res_np)

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

    assert np.allclose(res_np, res_cuda)