Centre Blaise Pascal » Bench4GPU

#!/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,Calls):

    a=a_np.copy()

    b=b_np.copy()

    for i in range(Calls):

        a=MySillyFunction(a)

        b=MySillyFunction(b)

    return(a+b)

# 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,Calls,Threads):

    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, int Calls)

{

  const int i = blockDim.x*blockIdx.x+threadIdx.x;

  float ai=a[i];

  float bi=b[i];

  for (int c=0;c<Calls;c++)

  {

    ai=MySillyFunction(ai);

    bi=MySillyFunction(bi);

  }

  dest[i] = ai + bi;

}

""")

    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)

    Size=a_np.size

    if (Size % Threads != 0):

        print("Impossible : %i not multiple of %i..." % (Threads,Size) )

        TimeIn=time.time()

        sillysum(drv.Out(res_np), drv.In(a_np), drv.In(b_np), np.uint32(Calls),

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

        Elapsed=time.time()-TimeIn

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

    else:

        Blocks=int(Size/Threads)

        TimeIn=time.time()

        sillysum(drv.Out(res_np), drv.In(a_np), drv.In(b_np), np.uint32(Calls),

                 block=(Threads,1,1), grid=(Blocks,1))

        Elapsed=time.time()-TimeIn

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

    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)

    # Liberation of memory

    a_g.release()

    b_g.release()

    res_g.release()

    return(res_np)

# OpenCL complete operation

def OpenCLSillyAddition(a_np,b_np,Calls):

    Id=0

    HasXPU=False

    for platform in cl.get_platforms():

        for device in platform.get_devices():

            if Id==Device:

                XPU=device

                print("CPU/GPU selected: ",device.name.lstrip())

                HasXPU=True

            Id+=1

            # print(Id)

    if HasXPU==False:

        print("No XPU #%i found in all of %i devices, sorry..." % (Device,Id-1))

        sys.exit()

    try:

        ctx = cl.Context(devices=[XPU])

        queue = cl.CommandQueue(ctx,properties=cl.command_queue_properties.PROFILING_ENABLE)

    except:

        print("Crash during context creation")

    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 Calls)

{

  int gid = get_global_id(0);

  float ai=a_g[gid];

  float bi=b_g[gid];

  for (int c=0;c<Calls;c++)

  {

    ai=MySillyFunction(ai);

    bi=MySillyFunction(bi);

  }

  res_g[gid] = ai + bi;

}

__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, np.uint32(Calls))

    #

    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)

    # Liberation of memory

    a_g.release()

    b_g.release()

    res_g.release()

    return(res_np)

import sys

import time

if __name__=='__main__':

    GpuStyle='OpenCL'

    SIZE=1024

    Device=0

    Calls=1

    Threads=1

    Serial=True

    import getopt

    HowToUse='%s -n -g <CUDA/OpenCL> -s <SizeOfVector> -d <DeviceId> -c <SillyCalls> -t <Threads>'

    try:

        opts, args = getopt.getopt(sys.argv[1:],"hng:s:d:c:t:",["gpustyle=","size=","device=","calls=","threads="])

    except getopt.GetoptError:

        print(HowToUse % sys.argv[0])

        sys.exit(2)

    # List of Devices

    Devices=[]

    Alu={}

    for opt, arg in opts:

        if opt == '-h':

            print(HowToUse % sys.argv[0])

            print("\nInformations about devices detected under OpenCL API:")

            # For PyOpenCL import

            try:

                import pyopencl as cl

                Id=0

                for platform in cl.get_platforms():

                    for device in platform.get_devices():

                        #deviceType=cl.device_type.to_string(device.type)

                        deviceType="xPU"

                        print("Device #%i from %s of type %s : %s" % (Id,platform.vendor.lstrip(),deviceType,device.name.lstrip()))

                        Id=Id+1

            except:

                print("Your platform does not seem to support OpenCL")

            print("\nInformations about devices detected under CUDA API:")

            # For PyCUDA import

            try:

                import pycuda.driver as cuda

                cuda.init()

                for Id in range(cuda.Device.count()):

                    device=cuda.Device(Id)

                    print("Device #%i of type GPU : %s" % (Id,device.name()))

                print

            except:

                print("Your platform does not seem to support CUDA")

            sys.exit()

        elif opt in ("-d", "--device"):

            Device=int(arg)

        elif opt in ("-t", "--threads"):

            Threads=int(arg)

        elif opt in ("-c", "--calls"):

            Calls=int(arg)

        elif opt in ("-g", "--gpustyle"):

            GpuStyle = arg

        elif opt in ("-s", "--size"):

            SIZE = int(arg)

        elif opt in ("-n"):

            Serial = False

    print("Device Selection : %i" % Device)

    print("GpuStyle used : %s" % GpuStyle)

    print("Size of complex vector : %i" % SIZE)

    print("Number of silly calls : %i" % Calls)

    print("Number of Threads : %i" % Threads)

    print("Serial compute : %i" % Serial)

    if GpuStyle=='CUDA':

        try:

            # For PyCUDA import

            import pycuda.driver as cuda

            cuda.init()

            for Id in range(cuda.Device.count()):

                device=cuda.Device(Id)

                print("Device #%i of type GPU : %s" % (Id,device.name()))

                if Id in Devices:

                    Alu[Id]='GPU'

        except ImportError:

            print("Platform does not seem to support CUDA")

    if GpuStyle=='OpenCL':

        try:

            # For PyOpenCL import

            import pyopencl as cl

            Id=0

            for platform in cl.get_platforms():

                for device in platform.get_devices():

                    #deviceType=cl.device_type.to_string(device.type)

                    deviceType="xPU"

                    print("Device #%i from %s of type %s : %s" % (Id,platform.vendor.lstrip().rstrip(),deviceType,device.name.lstrip().rstrip()))

                    if Id in Devices:

                    # Set the Alu as detected Device Type

                        Alu[Id]=deviceType

                    Id=Id+1

        except ImportError:

            print("Platform does not seem to support OpenCL")

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

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

    # Native Implementation

    if Serial:

        TimeIn=time.time()

        res_np=NativeSillyAddition(a_np,b_np,Calls)

        NativeElapsed=time.time()-TimeIn

        NativeRate=int(SIZE/NativeElapsed)

        print("NativeRate: %i" % NativeRate)

    # OpenCL Implementation

    if GpuStyle=='OpenCL' or GpuStyle=='all':

        TimeIn=time.time()

        # res_cl=OpenCLAddition(a_np,b_np)

        res_cl=OpenCLSillyAddition(a_np,b_np,Calls)

        OpenCLElapsed=time.time()-TimeIn

        OpenCLRate=int(SIZE/OpenCLElapsed)

        print("OpenCLRate: %i" % OpenCLRate)

        # Check on OpenCL with Numpy:

        if Serial:

            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!")

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

    # CUDA Implementation

    if GpuStyle=='CUDA' or GpuStyle=='all':

        TimeIn=time.time()

        res_cuda=CUDASillyAddition(a_np,b_np,Calls,Threads)

        CUDAElapsed=time.time()-TimeIn

        CUDARate=int(SIZE/CUDAElapsed)

        print("CUDARate: %i" % CUDARate)

        # Check on CUDA with Numpy:

        if Serial:

            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!")

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