Centre Blaise Pascal » Bench4GPU

#!/usr/bin/env python

#

# Ising2D model using PyOpenCL

#

# CC BY-NC-SA 2011 : <emmanuel.quemener@ens-lyon.fr>

#

# Thanks to Andreas Klockner for PyOpenCL:

# http://mathema.tician.de/software/pyopencl

#

# Interesting links:

# http://viennacl.sourceforge.net/viennacl-documentation.html

# http://enja.org/2011/02/22/adventures-in-pyopencl-part-1-getting-started-with-python/

import pyopencl as cl

import numpy

from numpy.random import randint as nprnd

from PIL import Image

import time

import sys

import getopt

import matplotlib.pyplot as plt

# 2097152 on HD5850 (with 1GB of RAM)

#  262144 on GT218

#STEP=262144

#STEP=1048576

#STEP=2097152

#STEP=4194304

#STEP=8388608

STEP=16777216

KERNEL_CODE="""

// Marsaglia RNG very simple implementation

#define znew (z=36969*(z&65535)+(z>>16))

#define wnew (w=18000*(w&65535)+(w>>16))

#define MWC ((znew<<16)+wnew )

#define MWCfp MWC * 2.328306435454494e-10f

__kernel void MainLoop(__global int *s,float J,float B,float T,uint size,

                       uint iterations,uint seed_w,uint seed_z)

{

   uint z=seed_z;

   uint w=seed_w;

   for (uint i=0;i<iterations;i++) {

      uint x=(uint)(MWC%size) ;

      uint y=(uint)(MWC%size) ;

      int p=s[x+size*y];

      int d=s[x+size*((y+1)%size)];

      int u=s[x+size*((y-1)%size)];

      int l=s[((x-1)%size)+size*y];

      int r=s[((x+1)%size)+size*y];

      float DeltaE=p*(2.0f*J*(u+d+l+r)+B);

      int factor=((DeltaE < 0.0f) || (MWCfp < exp(-DeltaE/T))) ? -1:1;

      s[x%size+size*(y%size)] = factor*p;

      // barrier(CLK_GLOBAL_MEM_FENCE);

   }

   barrier(CLK_GLOBAL_MEM_FENCE);

}

"""

def ImageOutput(sigma,prefix):

        Max=sigma.max()

        Min=sigma.min()

        # Normalize value as 8bits Integer

        SigmaInt=(255*(sigma-Min)/(Max-Min)).astype('uint8')

        image = Image.fromarray(SigmaInt)

        image.save("%s.jpg" % prefix)

def Metropolis(sigma,J,B,T,iterations,Device):

        # Initialisation des variables en les CASTant correctement

        # Je detecte un peripherique GPU dans la liste des peripheriques

    Id=1

    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

    if HasXPU==False:

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

        sys.exit()

    # Je cree le contexte et la queue pour son execution

    ctx = cl.Context([XPU])

    queue = cl.CommandQueue(ctx,

        properties=cl.command_queue_properties.PROFILING_ENABLE)

        # Je recupere les flag possibles pour les buffers

    mf = cl.mem_flags

    sigmaCL = cl.Buffer(ctx, mf.WRITE_ONLY|mf.COPY_HOST_PTR,hostbuf=sigma)

    MetropolisCL = cl.Program(ctx,KERNEL_CODE).build(

        options = "-cl-mad-enable -cl-fast-relaxed-math")

    i=0

    step=STEP

    duration=0.

    while (step*i < iterations):

        # Call OpenCL kernel

        # (1,) is global work size (only 1 work size)

        # (1,) is local work size

        # sigmaCL is lattice translated in CL format

        # step is number of iterations

        start_time=time.time()

        CLLaunch=MetropolisCL.MainLoop(queue,(1,),None,

                                                sigmaCL,

                                                numpy.float32(J),numpy.float32(B),

                                                numpy.float32(T),

                                                numpy.uint32(sigma.shape[0]),

                                                numpy.uint32(step),

                                                numpy.uint32(nprnd(2**32)),

                                                numpy.uint32(nprnd(2**32)))

        CLLaunch.wait()

        # Does not seem to work under AMD/ATI

        # elapsed = 1e-9*(CLLaunch.profile.end - CLLaunch.profile.start)

        elapsed = time.time()-start_time

        print "Iteration %i with T=%f and %i iterations in %f: " % (i,T,step,elapsed)

        if LAPIMAGE:

            cl.enqueue_copy(queue, sigma, sigmaCL).wait()

            ImageOutput(sigma,"Ising2D_GPU_Global_%i_%1.1f_%.3i_Lap" %

                (SIZE,T,i))

        i=i+1

        duration=duration+elapsed

        cl.enqueue_copy(queue, sigma, sigmaCL).wait()

        sigmaCL.release()

        return(duration)

def Magnetization(sigma,M):

    return(numpy.sum(sigma)/(sigma.shape[0]*sigma.shape[1]*1.0))

def Energy(sigma,J,B):

    # Copier et caster

    E=numpy.copy(sigma).astype(numpy.float32)

    # Appel par slice

    E[1:-1,1:-1]=E[1:-1,1:-1]*(2.0*J*(E[:-2,1:-1]+E[2:,1:-1]+

                                          E[1:-1,:-2]+E[1:-1,2:])+B)

    # Bien nettoyer la peripherie

    E[:,0]=0

    E[:,-1]=0

    E[0,:]=0

    E[-1,:]=0

    Energy=numpy.sum(E)

    return(Energy/(E.shape[0]*E.shape[1]*1.0))

def CriticalT(T,E):

    Epoly=numpy.poly1d(numpy.polyfit(T,E,T.size/3))

    dEpoly=numpy.diff(Epoly(T))

    dEpoly=numpy.insert(dEpoly,0,0)

    return(T[numpy.argmin(dEpoly)])

def DisplayCurves(T,E,M,J,B):

    plt.xlabel("Temperature")

    plt.ylabel("Energy")

    Experience,=plt.plot(T,E,label="Energy")

    plt.legend()

    plt.show()

if __name__=='__main__':

    # Set defaults values

    # Coupling factor

    J=1.

    # External Magnetic Field is null

    B=0.

    # Size of Lattice

    Size=256

    # Default Temperatures (start, end, step)

    Tmin=0.1

    Tmax=5

    Tstep=0.1

    # Default Number of Iterations

    Iterations=Size*Size*Size

    # Default Device is first

    Device=1

    # Curves is True to print the curves

    Curves=False

    OCL_vendor={}

    OCL_type={}

    OCL_description={}

    try:

        import pyopencl as cl

        print "\nHere are available OpenCL devices:"

        Id=1

        for platform in cl.get_platforms():

            for device in platform.get_devices():

                OCL_vendor[Id]=platform.vendor.lstrip().rstrip()

                #OCL_type[Id]=cl.device_type.to_string(device.type)

                OCL_type[Id]="xPU"

                OCL_description[Id]=device.name.lstrip().rstrip()

                print "* Device #%i from %s of type %s : %s" % (Id,OCL_vendor[Id],OCL_type[Id],OCL_description[Id])

                Id=Id+1

        OCL_MaxDevice=Id-1

        print

    except ImportError:

        print "Your platform does not seem to support OpenCL"

        sys.exit(0)

    try:

        opts, args = getopt.getopt(sys.argv[1:],"hcj:b:z:i:s:e:p:d:",["coupling=","magneticfield=","size=","iterations=","tempstart=","tempend=","tempstep=","units",'device='])

    except getopt.GetoptError:

        print '%s -d <Device Id> -j <Coupling Factor> -b <Magnetic Field> -z <Size of Square Lattice> -i <Iterations> -s <Minimum Temperature> -e <Maximum Temperature> -p <steP Temperature> -c (Print Curves)' % sys.argv[0]

        sys.exit(2)

    for opt, arg in opts:

        if opt == '-h':

            print '%s -d <Device Id> -j <Coupling Factor> -b <Magnetic Field> -z <Size of Square Lattice> -i <Iterations> -s <Minimum Temperature> -e <Maximum Temperature> -p <steP Temperature> -c (Print Curves)' % sys.argv[0]

            sys.exit()

        elif opt == '-c':

            Curves=True

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

            Device = int(arg)

            if Device>OCL_MaxDevice:

                "Device #%s seems not to be available !"

                sys.exit()

        elif opt in ("-j", "--coupling"):

            J = float(arg)

        elif opt in ("-b", "--magneticfield"):

            B = float(arg)

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

            Tmin = float(arg)

        elif opt in ("-e", "--tempmax"):

            Tmax = arg

        elif opt in ("-p", "--tempstep"):

            Tstep = numpy.uint32(arg)

        elif opt in ("-i", "--iterations"):

            Iterations = int(arg)

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

            Size = int(arg)

    print "Here are parameters of simulation:"

    print "* Device selected #%s: %s of type %s from %s" % (Device,OCL_description[Device],OCL_type[Device],OCL_vendor[Device])

    print "* Coupling Factor J : %s" % J

    print "* Magnetic Field B :  %s" % B

    print "* Size of lattice : %sx%s" % (Size,Size)

    print "* Iterations : %s" % Iterations

    print "* Temperatures from %s to %s by %s" % (Tmin,Tmax,Tstep)

    LAPIMAGE=False

    if Iterations<STEP:

        STEP=Iterations

    sigmaIn=numpy.where(numpy.random.randn(Size,Size)>0,1,-1).astype        (numpy.int32)

    ImageOutput(sigmaIn,"Ising2D_GPU_Global_%i_Initial" % (Size))

    Trange=numpy.arange(Tmin,Tmax+Tstep,Tstep)

    E=[]

    M=[]

    for T in Trange:

             sigma=numpy.copy(sigmaIn)

            duration=Metropolis(sigma,J,B,T,Iterations,Device)

            E=numpy.append(E,Energy(sigma,J,B))

            M=numpy.append(M,Magnetization(sigma,B))

            ImageOutput(sigma,"Ising2D_GPU_Global_%i_%1.1f_Final" % (Size,T))

            print "GPU Time : %f" % (duration)

            print "Total Energy at Temperature %f : %f" % (T,E[-1])

            print "Total Magnetization at Temperature %f : %f" % (T,M[-1])

    # Save output

    numpy.savez("Ising2D_GPU_Global_%i_%.8i" % (Size,Iterations),(Trange,E,M))

    # Estimate Critical temperature

    print "The critical temperature on %ix%i lattice with J=%f, B=%f is %f " % (Size,Size,J,B,CriticalT(Trange,E))

    if Curves:

        DisplayCurves(Trange,E,M,J,B)