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

from threading import Thread

from PiXPU import *

class threadWithReturn(Thread):

    def __init__(self, *args, **kwargs):

        super(threadWithReturn, self).__init__(*args, **kwargs)

        self._return = None

    def run(self):

        if self._target is not None:

            self._return = self._target(*self._args, **self._kwargs)

    def join(self, *args, **kwargs):

        super(threadWithReturn, self).join(*args, **kwargs)

        return self._return

if __name__=='__main__':

    # 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'

    HowToUse='%s -c (Print Curves) -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:],"hcg: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 ImportError:

                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 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)

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

            BlocksEnd = int(arg)

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

            BlocksStep = int(arg)

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

            ThreadsBegin = int(arg)

        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)

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

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

        ExploredBlocks=numpy.append(ExploredBlocks,Blocks)

        ExploredThreads=numpy.append(ExploredThreads,Threads)

        IterationsMP=Iterations/len(Devices)

        if Iterations%len(Devices)!=0:

            IterationsMP+=1

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

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

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

        for i in range(Redo):

            MyThreads=[]

            time_start=time.time()

            for Device in Devices:

                InputCL={}

                InputCL['Iterations']=IterationsMP

                InputCL['Steps']=1

                InputCL['Blocks']=Blocks

                InputCL['Threads']=Threads

                InputCL['Device']=Device

                InputCL['RNG']=RNG

                InputCL['ValueType']=ValueType

                if GpuStyle=='CUDA':

                    try:

                        MyThread=threadWithReturn(target=MetropolisCuda, args=(InputCL,))

                    except:

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

                elif GpuStyle=='OpenCL':

                    try:

                        MyThread=threadWithReturn(target=MetropolisOpenCL, args=(InputCL,))

                    except:

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

                print("Start on #%i device..." % Device)

                MyThread.start()

                MyThreads.append(MyThread)

            NewIterations=0

            Inside=0

            for MyThread in MyThreads:

                OutputCL=MyThread.join()

                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("PiThreads_%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("PiThreads_%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)