Révision 55
| Pi/GPU/Pi-GPU.py (revision 55) | ||
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#!/usr/bin/env python |
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# |
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# Pi-by-MC using PyCUDA/PyOpenCL
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# Pi-by-MonteCarlo using PyCUDA/PyOpenCL
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# |
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# CC BY-NC-SA 2011 : <emmanuel.quemener@ens-lyon.fr> |
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# |
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import sys |
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import getopt |
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import time |
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import matplotlib.pyplot as plt |
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import math |
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from scipy.optimize import curve_fit |
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from socket import gethostname |
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# find prime factors of a number |
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def FitAndPrint(N,D,Curves): |
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from scipy.optimize import curve_fit |
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import matplotlib.pyplot as plt |
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try: |
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coeffs_Amdahl, matcov_Amdahl = curve_fit(Amdahl, N, D) |
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|
| ... | ... | |
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if __name__=='__main__': |
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# Set defaults values |
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# Alu can be CPU or GPU |
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# Alu can be CPU, GPU or ACCELERATOR |
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Alu='CPU' |
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# Id of GPU : 1 is for first find ! |
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Device=0 |
| ... | ... | |
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Metrology='InMetro' |
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# Curves is True to print the curves |
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Curves=False |
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# Fit is True to print the curves |
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Fit=False |
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# DoublePrecision on FP calculus |
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DoublePrecision=False |
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try: |
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opts, args = getopt.getopt(sys.argv[1:],"hocla:g:p:i:s:e:t:r:d:",["alu=","gpustyle=","parastyle=","iterations=","jobstart=","jobend=","jobstep=","redo=","device="]) |
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opts, args = getopt.getopt(sys.argv[1:],"hoclfa:g:p:i:s:e:t:r:d:",["alu=","gpustyle=","parastyle=","iterations=","jobstart=","jobend=","jobstep=","redo=","device="])
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except getopt.GetoptError: |
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print '%s -o (Out of Core Metrology) -c (Print Curves) -l (Double Precision) -a <CPU/GPU/ACCELERATOR> -d <DeviceId> -g <CUDA/OpenCL> -p <Threads/Hybrid/Blocks> -i <Iterations> -s <JobStart> -e <JobEnd> -t <JobStep> -r <RedoToImproveStats> ' % sys.argv[0] |
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print '%s -o (Out of Core Metrology) -c (Print Curves) -l (Double Precision) -f (Fit to Amdahl Law) -a <CPU/GPU/ACCELERATOR> -d <DeviceId> -g <CUDA/OpenCL> -p <Threads/Hybrid/Blocks> -i <Iterations> -s <JobStart> -e <JobEnd> -t <JobStep> -r <RedoToImproveStats> ' % sys.argv[0]
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sys.exit(2) |
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for opt, arg in opts: |
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if opt == '-h': |
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print '%s -o (Out of Core Metrology) -c (Print Curves) -l (Double Precision) -a <CPU/GPU/ACCELERATOR> -d <DeviceId> -g <CUDA/OpenCL> -p <Threads/Hybrid/Blocks> -i <Iterations> -s <JobStart> -e <JobEnd> -t <JobStep> -r <RedoToImproveStats>' % sys.argv[0] |
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print '%s -o (Out of Core Metrology) -c (Print Curves) -l (Double Precision) -f (Fit to Amdahl Law) -a <CPU/GPU/ACCELERATOR> -d <DeviceId> -g <CUDA/OpenCL> -p <Threads/Hybrid/Blocks> -i <Iterations> -s <JobStart> -e <JobEnd> -t <JobStep> -r <RedoToImproveStats>' % sys.argv[0]
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print "\nInformations about devices detected under OpenCL:" |
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# For PyOpenCL import |
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import pyopencl as cl |
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Id=1 |
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for platform in cl.get_platforms(): |
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for device in platform.get_devices(): |
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deviceType=cl.device_type.to_string(device.type) |
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print "Device #%i of type %s : %s" % (Id,deviceType,device.name) |
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Id=Id+1 |
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try: |
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import pyopencl as cl |
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Id=1 |
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for platform in cl.get_platforms(): |
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for device in platform.get_devices(): |
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deviceType=cl.device_type.to_string(device.type) |
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print "Device #%i of type %s : %s" % (Id,deviceType,device.name) |
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Id=Id+1 |
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sys.exit() |
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sys.exit() |
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except ImportError: |
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print "Your platform does not seem to support OpenCL" |
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elif opt == '-o': |
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OutMetrology=True |
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Metrology='OutMetro' |
| ... | ... | |
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DoublePrecision=True |
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elif opt == '-c': |
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Curves=True |
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elif opt == '-f': |
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Fit=True |
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elif opt in ("-a", "--alu"):
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Alu = arg |
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elif opt in ("-d", "--device"):
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| ... | ... | |
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print "Double Precision in Kernels : %r" % DoublePrecision |
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if GpuStyle=='CUDA': |
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# For PyCUDA import |
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import pycuda.driver as cuda |
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import pycuda.gpuarray as gpuarray |
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import pycuda.autoinit |
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from pycuda.compiler import SourceModule |
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try: |
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# For PyCUDA import |
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import pycuda.driver as cuda |
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import pycuda.gpuarray as gpuarray |
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import pycuda.autoinit |
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from pycuda.compiler import SourceModule |
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except ImportError: |
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print "Platform does not seem to support CUDA" |
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if GpuStyle=='OpenCL': |
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# For PyOpenCL import |
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import pyopencl as cl |
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Id=1 |
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for platform in cl.get_platforms(): |
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for device in platform.get_devices(): |
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deviceType=cl.device_type.to_string(device.type) |
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print "Device #%i of type %s : %s" % (Id,deviceType,device.name) |
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if Id == Device: |
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# Set the Alu as detected Device Type |
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Alu=deviceType |
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Id=Id+1 |
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try: |
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# For PyOpenCL import |
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import pyopencl as cl |
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Id=1 |
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for platform in cl.get_platforms(): |
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for device in platform.get_devices(): |
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deviceType=cl.device_type.to_string(device.type) |
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print "Device #%i of type %s : %s" % (Id,deviceType,device.name) |
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if Id == Device: |
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# Set the Alu as detected Device Type |
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Alu=deviceType |
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Id=Id+1 |
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except ImportError: |
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print "Platform does not seem to support CUDA" |
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average=numpy.array([]).astype(numpy.float32) |
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median=numpy.array([]).astype(numpy.float32) |
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stddev=numpy.array([]).astype(numpy.float32) |
| ... | ... | |
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except: |
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print "Problem with %i // computations on Cuda" % Jobs |
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elif GpuStyle=='OpenCL': |
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# try: |
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# avg,med,std=MetropolisOpenCL(circle,Iterations,Redo,Jobs,ParaStyle,Alu,Device) |
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# except: |
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# print "Problem with %i // computations on OpenCL" % Jobs |
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avg,med,std=MetropolisOpenCL(circle,Iterations,Redo,Jobs,ParaStyle,Alu,Device,DoublePrecision) |
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try: |
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avg,med,std=MetropolisOpenCL(circle,Iterations,Redo,Jobs,ParaStyle,Alu,Device,DoublePrecision) |
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except: |
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print "Problem with %i // computations on OpenCL" % Jobs |
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if (avg,med,std) != (0,0,0): |
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print "jobs,avg,med,std",Jobs,avg,med,std |
| ... | ... | |
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numpy.savetxt("Pi%s_%s_%s_%s_%s_%i_%.8i_Device%i_%s_%s" % (Precision,Alu,GpuStyle,ParaStyle,JobStart,JobEnd,Iterations,Device,Metrology,gethostname()),numpy.transpose(ToSave))
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Jobs+=JobStep |
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FitAndPrint(ExploredJobs,median,Curves) |
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if Fit: |
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FitAndPrint(ExploredJobs,median,Curves) |
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Formats disponibles : Unified diff