Centre Blaise Pascal » Bench4GPU

#!/usr/bin/env python3

#

# Pi-by-MonteCarlo using PyCUDA/PyOpenCL

#

# CC BY-NC-SA 2011 : Emmanuel QUEMENER <emmanuel.quemener@gmail.com> 

# Cecill v2 : Emmanuel QUEMENER <emmanuel.quemener@gmail.com>

#

# Thanks to Andreas Klockner for PyCUDA:

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

# Thanks to Andreas Klockner for PyOpenCL:

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

# 

# 2013-01-01 : problems with launch timeout

# http://stackoverflow.com/questions/497685/how-do-you-get-around-the-maximum-cuda-run-time

# Option "Interactive" "0" in /etc/X11/xorg.conf

# Common tools

import numpy

from numpy.random import randint as nprnd

import sys

import getopt

import time

import math

import itertools

from socket import gethostname

import mpi4py

from mpi4py import MPI

from PiXPU import *

if __name__=='__main__':

    # MPI Init

    comm = MPI.COMM_WORLD

    rank = comm.Get_rank()

    # Define number of Nodes on with computing is performed (exclude 0)

    RankSize=comm.Get_size()

    if rank == 0:

        # Set defaults values

        # Id of Device : 1 is for first find !

        Device=1

        # GPU style can be Cuda (Nvidia implementation) or OpenCL

        GpuStyle='OpenCL'

        # Iterations is integer

        Iterations=10000000

        # BlocksBlocks in first number of Blocks to explore

        BlocksBegin=1

        # BlocksEnd is last number of Blocks to explore

        BlocksEnd=16

        # BlocksStep is the step of Blocks to explore

        BlocksStep=1

        # ThreadsBlocks in first number of Blocks to explore

        ThreadsBegin=1

        # ThreadsEnd is last number of Blocks to explore

        ThreadsEnd=1

        # ThreadsStep is the step of Blocks to explore

        ThreadsStep=1

        # Redo is the times to redo the test to improve metrology

        Redo=1

        # OutMetrology is method for duration estimation : False is GPU inside

        OutMetrology=False

        Metrology='InMetro'

        # Curves is True to print the curves

        Curves=False

        # Fit is True to print the curves

        Fit=False

        # Marsaglia RNG

        RNG='MWC'

        # Value type : INT32, INT64, FP32, FP64

        ValueType='FP32'

        # Inside based on If

        IfThen=False

        HowToUse='%s -c (Print Curves) -k (Case On IfThen) -d <DeviceId> -g <CUDA/OpenCL> -i <Iterations> -b <BlocksBegin> -e <BlocksEnd> -s <BlocksStep> -f <ThreadsFirst> -l <ThreadsLast> -t <ThreadssTep> -r <RedoToImproveStats> -m <SHR3/CONG/MWC/KISS> -v <INT32/INT64/FP32/FP64>'

        try:

            opts, args = getopt.getopt(sys.argv[1:],"hckg:i:b:e:s:f:l:t:r:d:m:v:",["gpustyle=","iterations=","blocksBegin=","blocksEnd=","blocksStep=","threadsFirst=","threadsLast=","threadssTep=","redo=","device=","marsaglia=","valuetype="])

        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:")

                # 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

                    print

                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 == '-c':

                Curves=True

            elif opt == '-k':

                IfThen=True

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

                Devices.append(int(arg))

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

                GpuStyle = arg

            elif opt in ("-m", "--marsaglia"):

                RNG = arg

            elif opt in ("-v", "--valuetype"):

                ValueType = arg

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

                Iterations = numpy.uint64(arg)

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

                BlocksBegin = int(arg)

                BlocksEnd = BlocksBegin

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

                BlocksEnd = int(arg)

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

                BlocksStep = int(arg)

            elif opt in ("-f", "--threadsfirst"):

                ThreadsBegin = int(arg)

                ThreadsEnd = ThreadsBegin

            elif opt in ("-l", "--threadslast"):

                ThreadsEnd = int(arg)

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

                ThreadsStep = int(arg)

            elif opt in ("-r", "--redo"):

                Redo = int(arg)

        print("Devices Identification : %s" % Devices)

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

        print("Iterations : %s" % Iterations)

        print("Number of Blocks on begin : %s" % BlocksBegin)

        print("Number of Blocks on end : %s" % BlocksEnd)

        print("Step on Blocks : %s" % BlocksStep)

        print("Number of Threads on begin : %s" % ThreadsBegin)

        print("Number of Threads on end : %s" % ThreadsEnd)

        print("Step on Threads : %s" % ThreadsStep)

        print("Number of redo : %s" % Redo)

        print("Metrology done out of XPU : %r" % OutMetrology)

        print("Type of Marsaglia RNG used : %s" % RNG)

        print("Type of variable : %s" % ValueType)

        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="*PU"

                        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")

        print(Devices,Alu)

        BlocksList=range(BlocksBegin,BlocksEnd+BlocksStep,BlocksStep)

        ThreadsList=range(ThreadsBegin,ThreadsEnd+ThreadsStep,ThreadsStep)

        ExploredJobs=numpy.array([]).astype(numpy.uint32)

        ExploredBlocks=numpy.array([]).astype(numpy.uint32)

        ExploredThreads=numpy.array([]).astype(numpy.uint32)

        avgD=numpy.array([]).astype(numpy.float32)

        medD=numpy.array([]).astype(numpy.float32)

        stdD=numpy.array([]).astype(numpy.float32)

        minD=numpy.array([]).astype(numpy.float32)

        maxD=numpy.array([]).astype(numpy.float32)

        avgR=numpy.array([]).astype(numpy.float32)

        medR=numpy.array([]).astype(numpy.float32)

        stdR=numpy.array([]).astype(numpy.float32)

        minR=numpy.array([]).astype(numpy.float32)

        maxR=numpy.array([]).astype(numpy.float32)

        IterationsMPI=numpy.uint64(Iterations/len(Devices))

        if Iterations%len(Devices)!=0:

            IterationsMPI+=1

        for Blocks,Threads in itertools.product(BlocksList,ThreadsList):

            ExploredJobs=numpy.append(ExploredJobs,Blocks*Threads)

            ExploredBlocks=numpy.append(ExploredBlocks,Blocks)

            ExploredThreads=numpy.append(ExploredThreads,Threads)

            DurationItem=numpy.array([]).astype(numpy.float32)

            Duration=numpy.array([]).astype(numpy.float32)

            Rate=numpy.array([]).astype(numpy.float32)

            for i in range(Redo):

                time_start=time.time()

                r=1

                # Distribution of Devices over nodes

                InputCL={}

                InputCL['Iterations']=IterationsMPI

                InputCL['Steps']=1

                InputCL['Blocks']=Blocks

                InputCL['Threads']=Threads

                InputCL['RNG']=RNG

                InputCL['ValueType']=ValueType

                InputCL['GpuStyle']=GpuStyle

                InputCL['IfThen']=IfThen

                for Device in Devices[1:]:

                    print("Send to device %i on rank %i" % (Device,r))

                    InputCL['Device']=Device

                    comm.send('CONTINUE',dest=r,tag=11)

                    comm.send(InputCL,dest=r,tag=11)

                    r+=1

                # Compute on rank 0

                print("Compute on rank 0")

                InputCL['Device']=Devices[0]

                if GpuStyle=='CUDA':

                    try:

                        OutputCL=MetropolisCuda(InputCL)

                    except:

                        print("Problem with (%i,%i) // computations on Cuda" % (Blocks,Threads))

                elif GpuStyle=='OpenCL':

                    try:

                        OutputCL=MetropolisOpenCL(InputCL)

                    except:

                        print("Problem with (%i,%i) // computations on OpenCL" %  (Blocks,Threads))

                Inside=OutputCL['Inside']

                NewIterations=OutputCL['NewIterations']

                for slave in range(1,len(Devices)):

                    print("Get OutputCL from %i" % slave)

                    OutputCL=comm.recv(source=slave,tag=11)

                    print(OutputCL)

                    NewIterations+=OutputCL['NewIterations']

                    Inside+=OutputCL['Inside']

                print("Pi estimation %.8f" % (4./NewIterations*Inside))

                Duration=numpy.append(Duration,time.time()-time_start)

                Rate=numpy.append(Rate,NewIterations/Duration[-1])

            avgD=numpy.append(avgD,numpy.average(Duration))

            medD=numpy.append(medD,numpy.median(Duration))

            stdD=numpy.append(stdD,numpy.std(Duration))

            minD=numpy.append(minD,numpy.min(Duration))

            maxD=numpy.append(maxD,numpy.max(Duration))

            avgR=numpy.append(avgR,numpy.average(Rate))

            medR=numpy.append(medR,numpy.median(Rate))

            stdR=numpy.append(stdR,numpy.std(Rate))

            minR=numpy.append(minR,numpy.min(Rate))

            maxR=numpy.append(maxR,numpy.max(Rate))

            print("%.2f %.2f %.2f %.2f %.2f %i %i %i %i %i" % (avgD[-1],medD[-1],stdD[-1],minD[-1],maxD[-1],avgR[-1],medR[-1],stdR[-1],minR[-1],maxR[-1]))

            numpy.savez("PiMPI_%s_%s_%s_%s_%s_%s_%s_%s_%.8i_Device%i_%s_%s" % (ValueType,RNG,Alu[Devices[0]],GpuStyle,BlocksBegin,BlocksEnd,ThreadsBegin,ThreadsEnd,Iterations,Devices[0],Metrology,gethostname()),(ExploredBlocks,ExploredThreads,avgD,medD,stdD,minD,maxD,avgR,medR,stdR,minR,maxR))

            ToSave=[ ExploredBlocks,ExploredThreads,avgD,medD,stdD,minD,maxD,avgR,medR,stdR,minR,maxR ]

            numpy.savetxt("PiMPI_%s_%s_%s_%s_%s_%s_%s_%i_%.8i_Device%i_%s_%s" % (ValueType,RNG,Alu[Devices[0]],GpuStyle,BlocksBegin,BlocksEnd,ThreadsBegin,ThreadsEnd,Iterations,Devices[0],Metrology,gethostname()),numpy.transpose(ToSave),fmt='%i %i %e %e %e %e %e %i %i %i %i %i')

            if Fit:

                FitAndPrint(ExploredJobs,median,Curves)

        # Send MPI exit tag

        for slave in range(1,RankSize):

            comm.send('BREAK',dest=slave,tag=11)

    else:

        while True:

            Signal=comm.recv(source=0,tag=11)

            if Signal=='CONTINUE':

                # Receive information from Master

                InputCL=comm.recv(source=0,tag=11)

                print("Parameters retreive for rank %s of %s on %s from master:" % (rank,RankSize,gethostname()))

                print("Input CL:" % InputCL)

                # Execute on slave

                if InputCL['GpuStyle']=='CUDA':

                    try:

                        OutputCL=MetropolisCuda(InputCL)

                    except:

                        print("Problem with (%i,%i) // computations on Cuda" % (InputCL['Blocks'],InputCL['Threads']))

                elif InputCL['GpuStyle']=='OpenCL':

                    try:

                        OutputCL=MetropolisOpenCL(InputCL)

                    except:

                        print("Problem with (%i,%i) // computations on OpenCL" %  (InputCL['Blocks'],InputCL['Threads']))

                print("Output CL:" % OutputCL)

                # Send information to Master

                comm.send(OutputCL,dest=0,tag=11)

                print("Data sent to master")

            else:

                print('Exit signal from Master')

                break