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Pushing section 5
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‎5-AdvancedScaling/basics/basic_features.py‎

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#!/usr/bin/env python
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from mpi4py import MPI
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import numpy as np
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comm = MPI.COMM_WORLD
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rank = comm.Get_rank()
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mpisize = comm.Get_size()
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def output(var,nomen,rank,method=None,operation=None):
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if method==None and operation==None:
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print "Rank %d sees %s as %s" %(rank,nomen,var)
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else:
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print "Rank %d sees %s as %s after %s using %s" %(rank,nomen,var,method,operation)
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if rank == 0:
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local_dict={'a':1, 'b':2, 'c':3, 'd':4, 'e':5, 'f':6, 'g':'gee whiz', 'h':('hi', 'there') }
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local_list_max=[rank+i for i in range(mpisize/2)]
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local_list_sum=[rank+i for i in range(mpisize/2)]
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local_string="""This is a string."""
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local_tuple=(rank,)*mpisize
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local_np_array=np.array(range(10))
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else:
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local_dict=None
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local_list_max=[rank*i for i in range(mpisize/2)]
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local_list_sum=[rank*i for i in range(mpisize/2)]
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local_string="""This should be fun!"""
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local_tuple=(rank,)*mpisize
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local_np_array=np.array(range(10))
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comm.Barrier()
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if rank == 0:
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print "#"*78
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output(local_dict,'local_dict',rank)
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output(local_list_max,'local_list_max',rank)
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output(local_list_sum,'local_list_sum',rank)
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output(local_string,'local_string',rank)
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output(local_tuple,'local_tuple',rank)
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output(local_np_array,'local_np_array',rank)
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comm.Barrier()
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if rank == 6:
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print "#"*78
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output(local_dict,'local_dict',rank)
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output(local_list_max,'local_list_max',rank)
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output(local_list_sum,'local_list_sum',rank)
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output(local_string,'local_string',rank)
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output(local_tuple,'local_tuple',rank)
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output(local_np_array,'local_np_array',rank)
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comm.Barrier()
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if rank == 0:
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print "#"*78
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print ""
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print "Running collective operations"
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print ""
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comm.Barrier()
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local_dict = comm.scatter(local_dict)
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local_list_max = comm.allreduce(local_list_max,op=MPI.MAX)
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local_list_sum = comm.allreduce(local_list_sum,op=MPI.SUM)
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local_string = comm.bcast(local_string)
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local_tuple = comm.alltoall(local_tuple)
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local_np_array = comm.allreduce(local_np_array,op=MPI.SUM)
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comm.Barrier()
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if rank == 0:
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print "#"*78
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output(local_dict,'local_dict',rank)
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output(local_list_max,'local_list_max',rank,'allreduce','max')
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output(local_list_sum,'local_list_sum',rank,'allreduce','sum')
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output(local_string,'local_string',rank,'bcast')
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output(local_tuple,'local_tuple',rank,'alltoall')
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output(local_np_array,'local_np_array',rank,'allreduce')
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comm.Barrier()
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if rank == 6:
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print "#"*78
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output(local_dict,'local_dict',rank)
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output(local_list_max,'local_list_max',rank,'allreduce','max')
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output(local_list_sum,'local_list_sum',rank,'allreduce','sum')
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output(local_string,'local_string',rank,'bcast')
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output(local_tuple,'local_tuple',rank,'alltoall')
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output(local_np_array,'local_np_array',rank,'allreduce')

‎5-AdvancedScaling/pi/builtins_mpi_pi.py‎

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#!/usr/bin/env python2.7
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# File: builtins_mpi_pi.py
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# Author: William Scullin
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# Date: 2015-11-28
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#
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# Sample program that does a parallel monte carlo calculation of pi using MPI
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# and standard library functions. NumPy is only invoked to create mutable
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# buffers for mpi4py to read and write to/from.
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from mpi4py import MPI
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import numpy as np
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import random
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import util
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def calcpi(samples):
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"""calculate Pi in parallel using standard library functions"""
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comm = MPI.COMM_WORLD
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rank = comm.Get_rank()
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mpisize = comm.Get_size()
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# we need this to have a muteable buffer for mpi4py
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# to write into so we don't need to serialize data
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pi = np.zeros(1)
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nsamples = int(samples/mpisize)
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inside = 0
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random.seed(rank)
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for i in range(nsamples):
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x = random.random()
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y = random.random()
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if (x*x)+(y*y) < 1:
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inside += 1
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mypi = (4.0*inside)/nsamples
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# capital Reduce works with C/NumPy compatible data
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# -types and avoids the need to serialize data at
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# transmission and receipt. Lowercase reduce would
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# work on Python objects.
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#
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# The value of mypi will be summed into pi on rank 0
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comm.Reduce(np.array(mypi), pi, op=MPI.SUM, root=0)
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return pi/mpisize
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if __name__ == '__main__':
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samples = util.get_sample_count()
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comm = MPI.COMM_WORLD
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rank = comm.Get_rank()
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start_time = MPI.Wtime()
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pi = calcpi(samples)
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end_time = MPI.Wtime()
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if rank == 0:
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util.output(samples, pi, start_time, end_time)

‎5-AdvancedScaling/pi/builtins_pi.py‎

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#!/usr/bin/env python2.7
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# File: builtins_pi.py
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# Author: William Scullin
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# Date: 2015-11-28
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#
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# Sample program that does a serial monte carlo calculation of pi using only
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# functions from the standard library.
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import random
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import time
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import util
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def calcpi(nsamples):
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"""serially calculate Pi using only standard library functions"""
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inside = 0
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random.seed(0)
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for i in range(int(samples)):
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x = random.random()
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y = random.random()
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if (x*x)+(y*y) < 1:
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inside += 1
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return (4.0 * inside)/samples
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if __name__ == '__main__':
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samples = util.get_sample_count()
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start_time = time.time()
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pi = calcpi(samples)
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end_time = time.time()
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util.output(samples, pi, start_time, end_time)

‎5-AdvancedScaling/pi/builtins_pyobj_mpi_pi.py‎

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#!/usr/bin/env python2.7
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# File: builtins_mpi_pi.py
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# Author: William Scullin
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# Date: 2015-11-28
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#
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# Sample program that does a parallel monte carlo calculation of pi using MPI
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# and standard library functions. NumPy is only invoked to create mutable
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# buffers for mpi4py to read and write to/from.
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from mpi4py import MPI
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import numpy as np
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import random
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import util
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def calcpi(samples):
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"""calculate Pi in parallel using standard library functions"""
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comm = MPI.COMM_WORLD
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rank = comm.Get_rank()
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mpisize = comm.Get_size()
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nsamples = int(samples/mpisize)
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inside = 0
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random.seed(rank)
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for i in range(nsamples):
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x = random.random()
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y = random.random()
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if (x*x)+(y*y) < 1:
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inside += 1
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pi = (4.0*inside)/nsamples
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# capital Reduce works with C/NumPy compatible data
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# -types and avoids the need to serialize data at
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# transmission and receipt. Lowercase reduce would
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# work on Python objects.
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#
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# The value of mypi will be summed into pi on rank 0
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pi=comm.reduce(pi, op=MPI.SUM, root=0)
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if rank==0:
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pi=pi/mpisize
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return pi
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if __name__ == '__main__':
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samples = util.get_sample_count()
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comm = MPI.COMM_WORLD
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rank = comm.Get_rank()
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start_time = MPI.Wtime()
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pi = calcpi(samples)
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end_time = MPI.Wtime()
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if rank == 0:
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util.output(samples, pi, start_time, end_time)

‎5-AdvancedScaling/pi/numba_mpi_pi.py‎

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#!/usr/bin/env python2.7
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# File: numba_mpi_pi.py
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# Author: William Scullin
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# Date: 2015-11-28
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#
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# Sample program that does a parallel monte carlo calculation of pi using MPI
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# and standard library functions, but invokes the Numba JIT to speed
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# calculation. NumPy is only invoked to create mutable buffers for mpi4py to
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# read and write into.
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from mpi4py import MPI
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import numpy as np
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import random
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from numba import jit
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import util
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@jit
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def calcpi(nsamples):
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"""calculate Pi using Numba jit"""
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inside = 0
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for i in range(nsamples):
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x = random.random()
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y = random.random()
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if (x*x)+(y*y) < 1:
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inside += 1
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return (4.0 * inside)/nsamples
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@jit
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def calcpi_mpi(samples):
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"""carry out Pi calculations in parallel using MPI"""
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# Needed to provide mpi4py a writable buffer
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pi = np.zeros(1)
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comm = MPI.COMM_WORLD
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rank = comm.Get_rank()
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mpisize = comm.Get_size()
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nsamples = int(samples/mpisize)
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random.seed(rank)
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mypi = np.array(calcpi(nsamples))
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comm.Reduce(mypi, pi, op=MPI.SUM, root=0)
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return pi/mpisize
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if __name__ == '__main__':
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samples = util.get_sample_count()
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comm = MPI.COMM_WORLD
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rank = comm.Get_rank()
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start_time = MPI.Wtime()
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pi = calcpi_mpi(samples)
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end_time = MPI.Wtime()
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if rank == 0:
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util.output(samples, pi, start_time, end_time)

‎5-AdvancedScaling/pi/numba_pi.py‎

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#!/usr/bin/env python2.7
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# File: numba_pi.py
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# Author: William Scullin
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# Date: 2015-11-28
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#
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# Sample program that does a serial monte carlo calculation of pi using
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# standard library functions, but invokes the Numba JIT to speed calculation.
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import random
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import time
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from numba import jit
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import util
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@jit
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def calcpi(nsamples):
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"""carry out Pi calculations in serial using Numba jit"""
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random.seed(0)
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inside = 0
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for i in range(nsamples):
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x = random.random()
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y = random.random()
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if (x*x)+(y*y) < 1:
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inside += 1
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return (4.0 * inside)/nsamples
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if __name__ == '__main__':
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samples = util.get_sample_count()
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start_time = time.time()
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pi = calcpi(samples)
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end_time = time.time()
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util.output(samples, pi, start_time, end_time)

‎5-AdvancedScaling/pi/numpy_mpi_pi.py‎

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#!/usr/bin/env python2.7
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# File: numpy_mpi_pi.py
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# Author: William Scullin
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# Date: 2015-11-28
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#
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# Sample program that does a parallel monte carlo calculation of pi using MPI
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# and NumPy functions for speed. Note that performance my strongly depend on
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# your NumPy build and MPI implementation.
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from mpi4py import MPI
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import numpy as np
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import util
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def calcpi(samples):
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"""calculate Pi using numpy and mpi"""
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comm = MPI.COMM_WORLD
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rank = comm.Get_rank()
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mpisize = comm.Get_size()
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nsamples = int(samples/mpisize)
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pi = np.zeros(1)
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np.random.seed(rank)
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xy = np.random.random((nsamples, 2))
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mypi = np.array(4.0*np.sum(np.sum(xy**2, 1) < 1)/nsamples)
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comm.Reduce(mypi, pi, op=MPI.SUM, root=0)
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return pi/mpisize
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if __name__ == '__main__':
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samples = util.get_sample_count()
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comm = MPI.COMM_WORLD
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rank = comm.Get_rank()
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start_time = MPI.Wtime()
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pi = calcpi(samples)
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end_time = MPI.Wtime()
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if rank == 0:
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util.output(samples, pi, start_time, end_time)

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