def create_global_array(gatype):
if NEW_API:
g_a = ga.create_handle()
ga.set_data(g_a, [n,n], gatype)
ga.set_array_name(g_a, 'a')
if USE_RESTRICTED:
num_restricted = nproc/2 or 1
restricted_list = np.arange(num_restricted) + num_restricted/2
ga.set_restricted(g_a, restricted_list)
if BLOCK_CYCLIC:
if USE_SCALAPACK_DISTR:
if nproc % 2 == 0:
ga.error('Available procs must be divisible by 2',nproc)
ga.set_block_cyclic_proc_grid(g_a, block_size, proc_grid)
else:
ga.set_block_cyclic(g_a, block_size)
if MIRROR:
p_mirror = ga.pgroup_get_mirror()
ga.set_pgroup(g_a, p_mirror)
ga.allocate(g_a)
else:
if MIRROR:
p_mirror = ga.pgroup_get_mirror()
ga.create_config(gatype, (n,n), 'a', None, p_mirror)
else:
g_a = ga.create(gatype, (n,n), 'a')
if 0 == g_a:
ga.error('ga.create failed')
if MIRROR:
lproc = me - ga.cluster_procid(inode, 0)
lo,hi = ga.distribution(g_a, lproc)
else:
lo,hi = ga.distribution(g_a, me)
ga.sync()
return g_a
def parallel_task():
me = ga.pgroup_nodeid()
nproc = ga.pgroup_nnodes()
if not me:
print "This is process 0 on group %s" % ga.pgroup_get_default()
g_a = ga.create(ga.C_DBL, (3, 4, 5))
ga.randomize(g_a)
if me == 0:
print np.sum(ga.access(g_a))
def parallel_task():
me = ga.pgroup_nodeid()
nproc = ga.pgroup_nnodes()
if not me:
print "This is process 0 on group %s" % ga.pgroup_get_default()
g_a = ga.create(ga.C_DBL, (3,4,5))
ga.randomize(g_a)
if me == 0:
print np.sum(ga.access(g_a))
def matrix_multiply():
# Configure array dimensions. Force an unequal data distribution.
dims = [TOTALELEMS]*NDIM
chunk = [TOTALELEMS/nprocs-1]*NDIM
# Create a global array g_a and duplicate it to get g_b and g_c.
g_a = ga.create(ga.C_DBL, dims, "array A", chunk)
if not g_a: ga.error("create failed: A")
if not me: print "Created Array A"
g_b = ga.duplicate(g_a, "array B")
g_c = ga.duplicate(g_a, "array C")
if not g_b or not g_c: ga.eror("duplicate failed")
if not me: print "Created Arrays B and C"
# Initialize data in matrices a and b.
if not me: print "Initializing matrix A and B"
a = np.random.rand(*dims)*29
b = np.random.rand(*dims)*37
# Copy data to global arrays g_a and g_b.
if not me:
ga.put(g_a, a)
ga.put(g_b, b)
# Synchronize all processors to make sure everyone has data.
ga.sync()
# Determine which block of data is locally owned. Note that
# the same block is locally owned for all GAs.
lo,hi = ga.distribution(g_c)
# Get the blocks from g_a and g_b needed to compute this block in
# g_c and copy them into the local buffers a and b.
a = ga.get(g_a, (lo[0],0), (hi[0],dims[0]))
b = ga.get(g_b, (0,lo[1]), (dims[1],hi[1]))
# Do local matrix multiplication and store the result in local
# buffer c. Start by evaluating the transpose of b.
btrns = b.transpose()
# Multiply a and b to get c.
c = np.dot(a,b)
# Copy c back to g_c.
ga.put(g_c, c, lo, hi)
verify(g_a, g_b, g_c)
# Deallocate arrays.
ga.destroy(g_a)
ga.destroy(g_b)
ga.destroy(g_c)
def test2D():
n = 1024
buf = np.zeros((n,n), dtype=np.float64)
chunk = np.asarray([1,3,4,9,16,24,30,48,64,91,128,171,256,353,440,512])
g_a = ga.create(ga.C_DBL, (n,n), 'a')
if 0 == g_a:
ga.error('ga.create failed')
buf[:] = 0.01
ga.zero(g_a)
if 0 == me:
print (' Performance of GA get, put & acc'
' for square sections of array[%d,%d]' % (n,n))
lo,hi = ga.distribution(g_a, me)
# local ops
TestPutGetAcc(g_a, n, chunk, buf, lo, hi, True)
# remote ops
TestPutGetAcc(g_a, n, chunk, buf, lo, hi, False)
def TRANSPOSE1D():
# Configure array dimensions. Force an unequal data distribution.
dims = [nprocs*TOTALELEMS + nprocs/2]
chunk = [TOTALELEMS] # minimum data on each process
# create a global array g_a and duplicate it to get g_b
g_a = ga.create(ga.C_INT, dims, "array A", chunk)
if not g_a: ga.error("create failed: A")
if not me: print "Created Array A"
g_b = ga.duplicate(g_a, "array B")
if not g_b: ga.error("duplicate failed")
if not me: print "Created Array B"
# initialize data in g_a
if not me:
print "Initializing matrix A"
ga.put(g_a, np.arange(dims[0], dtype=np.int32))
# Synchronize all processors to guarantee that everyone has data
# before proceeding to the next step.
ga.sync()
# Start initial phase of inversion by inverting the data held locally on
# each processor. Start by finding out which data each processor owns.
lo,hi = ga.distribution(g_a)
# Get locally held data and copy it into local buffer a
a = ga.get(g_a, lo, hi)
# Invert data locally
b = a[::-1]
# Invert data globally by copying locally inverted blocks into
# their inverted positions in the GA
ga.put(g_b, b, dims[0]-hi[0], dims[0]-lo[0])
# Synchronize all processors to make sure inversion is complete
ga.sync()
# Check to see if inversion is correct
if not me: verify(g_a, g_b)
# Deallocate arrays
ga.destroy(g_a)
ga.destroy(g_b)
def TRANSPOSE1D():
# Configure array dimensions. Force an unequal data distribution.
dims = [nprocs * TOTALELEMS + nprocs / 2]
chunk = [TOTALELEMS] # minimum data on each process
# create a global array g_a and duplicate it to get g_b
g_a = ga.create(ga.C_INT, dims, "array A", chunk)
if not g_a: ga.error("create failed: A")
if not me: print "Created Array A"
g_b = ga.duplicate(g_a, "array B")
if not g_b: ga.error("duplicate failed")
if not me: print "Created Array B"
# initialize data in g_a
if not me:
print "Initializing matrix A"
ga.put(g_a, np.arange(dims[0], dtype=np.int32))
# Synchronize all processors to guarantee that everyone has data
# before proceeding to the next step.
ga.sync()
# Start initial phase of inversion by inverting the data held locally on
# each processor. Start by finding out which data each processor owns.
lo, hi = ga.distribution(g_a)
# Get locally held data and copy it into local buffer a
a = ga.get(g_a, lo, hi)
# Invert data locally
b = a[::-1]
# Invert data globally by copying locally inverted blocks into
# their inverted positions in the GA
ga.put(g_b, b, dims[0] - hi[0], dims[0] - lo[0])
# Synchronize all processors to make sure inversion is complete
ga.sync()
# Check to see if inversion is correct
if not me: verify(g_a, g_b)
# Deallocate arrays
ga.destroy(g_a)
ga.destroy(g_b)
def test2D():
n = 1024
buf = np.zeros((n, n), dtype=np.float64)
chunk = np.asarray(
[1, 3, 4, 9, 16, 24, 30, 48, 64, 91, 128, 171, 256, 353, 440, 512])
g_a = ga.create(ga.C_DBL, (n, n), 'a')
if 0 == g_a:
ga.error('ga.create failed')
buf[:] = 0.01
ga.zero(g_a)
if 0 == me:
print(
' Performance of GA get, put & acc'
' for square sections of array[%d,%d]' % (n, n))
lo, hi = ga.distribution(g_a, me)
# local ops
TestPutGetAcc(g_a, n, chunk, buf, lo, hi, True)
# remote ops
TestPutGetAcc(g_a, n, chunk, buf, lo, hi, False)
def test1D():
n = 1024*1024
buf = np.zeros(n/4, dtype=np.float64)
chunk = np.asarray([1,9,16,81,256,576,900,2304,4096,8281,
16384,29241,65536,124609,193600,262144])
g_a = ga.create(ga.C_DBL, (n,), 'a')
if 0 == g_a:
ga.error('ga.create failed')
buf[:] = 0.01
ga.zero(g_a)
if 0 == me:
print ''
print ''
print ''
print (' Performance of GA get, put & acc'
' for 1-dimensional sections of array[%d]' % n)
lo,hi = ga.distribution(g_a, me)
# local ops
TestPutGetAcc1(g_a, n, chunk, buf, lo, hi, True)
# remote ops
TestPutGetAcc1(g_a, n, chunk, buf, lo, hi, False)
def test1D():
n = 1024 * 1024
buf = np.zeros(n / 4, dtype=np.float64)
chunk = np.asarray([
1, 9, 16, 81, 256, 576, 900, 2304, 4096, 8281, 16384, 29241, 65536,
124609, 193600, 262144
])
g_a = ga.create(ga.C_DBL, (n, ), 'a')
if 0 == g_a:
ga.error('ga.create failed')
buf[:] = 0.01
ga.zero(g_a)
if 0 == me:
print ''
print ''
print ''
print(
' Performance of GA get, put & acc'
' for 1-dimensional sections of array[%d]' % n)
lo, hi = ga.distribution(g_a, me)
# local ops
TestPutGetAcc1(g_a, n, chunk, buf, lo, hi, True)
# remote ops
TestPutGetAcc1(g_a, n, chunk, buf, lo, hi, False)
# the (i,j) element has the value j*NSIZE + i. This corresponds to
# numbering each of the elements consecutively in column-major order.
#
# Find local processor ID and number of processors
me = ga.nodeid()
nprocs = ga.nnodes()
if me == 0:
print "Initialized GA library on %d processes" % nprocs
# Create a GA
dims = (NSIZE,NSIZE)
chunk = (-1,-1)
ld = NSIZE
g_a = ga.create(ga.C_INT, dims, "test_a", chunk)
if me == 0 and g_a:
print "\nSuccessfully created Global Array"
# Initialize data in GA. Find data owned by neighboring processor
nghbr = (me+1)%nprocs
lo,hi = ga.distribution(g_a, nghbr)
# Create data in local buffer, assign unique value for each data element
patch_shape = hi-lo
a_buf = np.fromfunction(lambda i,j: j*NSIZE + i,
patch_shape, dtype=ga.dtype(ga.C_INT))
a_buf += lo[1,np.newaxis]
a_buf += lo[np.newaxis,0]*dims[0]
s = 0.0
for i in xrange(myrank + 1, n + 1, nprocs):
x = h * (i - 0.5)
s += 4.0 / (1.0 + x**2)
return s * h
def prn_pi(pi, PI):
message = "pi is approximately %.16f, error is %.16f"
print(message % (pi, abs(pi - PI)))
nprocs = ga.nnodes()
myrank = ga.nodeid()
g_pi = ga.create(ga.C_DBL, [1])
one_time = False
if len(sys.argv) == 2:
n = int(sys.argv[1])
one_time = True
while True:
if not one_time:
if myrank == 0:
n = get_n()
n = ga.brdcst(n)
else:
n = ga.brdcst(0)
if n == 0:
break
u = mda.Universe(PSF, longXTC1)
print(len(u.trajectory))
start1 = time.time()
u = mda.Universe(PSF, longXTC1)
mobile = u.select_atoms(
"(resid 1:29 or resid 60:121 or resid 160:214) and name CA")
index = mobile.indices
topology, trajectory = mobile.universe.filename, mobile.universe.trajectory.filename
uref = mda.Universe(PSF, longXTC0)
ref0 = uref.select_atoms(
"(resid 1:29 or resid 60:121 or resid 160:214) and name CA")
xref0 = ref0.positions - ref0.center_of_mass()
bsize = int(np.ceil(mobile.universe.trajectory.n_frames))
g_a = ga.create(ga.C_DBL, [bsize * size, 2], "RMSD")
buf = np.zeros([bsize * size, 2], dtype=float)
# Create each segment for each process
start2 = time.time()
frames_seg = np.zeros([size, 2], dtype=int)
for iblock in range(size):
frames_seg[iblock, :] = iblock * bsize, (iblock + 1) * bsize
d = dict([key, frames_seg[key]] for key in range(size))
start, stop = d[rank][0], d[rank][1]
# Block-RMSD in Parallel
start3 = time.time()
import mpi4py.MPI # initialize Message Passing Interface
from ga4py import ga # initialize Global Arrays
me = ga.nodeid()
def print_distribution(g_a):
for i in range(ga.nnodes()):
lo, hi = ga.distribution(g_a, i)
print "%s lo=%s hi=%s" % (i, lo, hi)
# create some arrays
g_a = ga.create(ga.C_DBL, (10, 20, 30))
g_b = ga.create(ga.C_INT, (2, 3, 4, 5, 6))
if not me:
print_distribution(g_a)
print_distribution(g_b)
def main():
# TODO there's got to be a loopless, more pythonic way to do this
ii = 0
for i in range(num1*num1):
ii += 1
if ii > num1:
ii = 0
h0[i] = ii
# compute times assuming 500 mflops and 5 second target time
# ntimes = max(3.0, 5.0/(4.0-9*num**3))
ntimes = 5
for ii in range(howmany):
num_m = nums_m[ii]
num_n = nums_n[ii]
num_k = nums_k[ii]
a = 0.5/(num_m*num_n)
if num_m > nummax or num_n > nummax or num_k > nummax:
ga.error('Insufficient memory: check nummax')
if BLOCK_CYCLIC:
block_size = [128,128]
g_c = ga.create_handle()
ga.set_data(g_c, (num_m,num_n), ga.C_DBL)
ga.set_array_name(g_c, 'g_c')
ga.set_block_cyclic(g_c, block_size)
if not ga.allocate(g_c):
ga.error('create failed')
block_size = [128,128]
g_b = ga.create_handle()
ga.set_data(g_b, (num_k,num_n), ga.C_DBL)
ga.set_array_name(g_b, 'g_b')
ga.set_block_cyclic(g_b, block_size)
if not ga.allocate(g_b):
ga.error('create failed')
block_size = [128,128]
g_a = ga.create_handle()
ga.set_data(g_a, (num_m,num_k), ga.C_DBL)
ga.set_array_name(g_a, 'g_a')
ga.set_block_cyclic(g_a, block_size)
if not ga.allocate(g_a):
ga.error('create failed')
else:
g_a = ga.create(ga.C_DBL, (num_m,num_k), 'g_a')
g_b = ga.create(ga.C_DBL, (num_k,num_n), 'g_b')
g_c = ga.create(ga.C_DBL, (num_m,num_n), 'g_c')
for handle in [g_a,g_b,g_c]:
if 0 == handle:
ga.error('create failed')
# initialize matrices A and B
if 0 == me:
load_ga(g_a, h0, num_m, num_k)
load_ga(g_b, h0, num_k, num_n)
ga.zero(g_c)
ga.sync()
if 0 == me:
print '\nMatrix Multiplication C = A[%d,%d] x B[%d,%d]\n' % (
num_m, num_k, num_k, num_n)
print ' %4s %12s %12s %7s %7s'%(
"Run#", "Time (seconds)", "mflops/proc",
"A trans", "B trans")
avg_t[:] = 0
avg_mf[:] = 0
for itime in range(ntimes):
for i in range(ntrans):
ga.sync()
ta = transa[i]
tb = transb[i]
t1 = time.time()
ga.gemm(ta,tb,num_m,num_n,num_k,1,g_a,g_b,0,g_c)
t1 = time.time() - t1
if 0 == me:
mf = 2*num_m*num_n*num_k/t1*10**-6/nproc
avg_t[i] += t1
avg_mf[i] += mf
print ' %4d %12.4f %12.1f %7s %7s'%(
itime+1, t1, mf, ta, tb)
if VERIFY and itime == 0:
verify_ga_gemm(ta, tb, num_m, num_n, num_k,
1.0, g_a, g_b, 0.0, g_c)
if 0 == me:
print ''
for i in range(ntrans):
print 'Average: %12.4f seconds %12.1f mflops/proc %s %s'%(
avg_t[i]/ntimes, avg_mf[i]/ntimes,
transa[i], transb[i])
if VERIFY:
print 'All ga.gemms are verified...O.K.'
def verify_using_np(g_a, g_b, g_c):
a = ga.get(g_a)
b = ga.get(g_b)
c = ga.get(g_c)
v = np.dot(a,b)
val = int(np.abs(np.sum(c-v))>0.0001)
val = ga.gop_add(val)
return val == 0
if __name__ == '__main__':
if nproc > MULTIPLIER**3:
if 0 == me:
print "You must use less than %s processors" % (MULTIPLIER**3+1)
else:
g_a = ga.create(ga.C_DBL, [N,N])
g_b = ga.create(ga.C_DBL, [N,N])
g_c = ga.create(ga.C_DBL, [N,N])
# put some fake data into input arrays A and B
if me == 0:
ga.put(g_a, np.random.random(N*N))
ga.put(g_b, np.random.random(N*N))
ga.sync()
if me == 0:
print "srumma...",
srumma(g_a, g_b, g_c, CHUNK_SIZE, MULTIPLIER)
if me == 0:
print "done"
if me == 0:
print "verifying using ga.gemm...",
ok = verify_using_ga(g_a, g_b, g_c)
"""Use ga.access() to sum locally per SMP node."""
import mpi4py.MPI
from ga4py import ga
import numpy as np
# Okay, we create the global array
g_a = ga.create(ga.C_DBL, (3,4,5,6))
if world_id == 0:
ga.put(g_a, np.arange(3*4*5*6))
ga.sync()
# You're on your own!
import mpi4py.MPI # initialize Message Passing Interface
from ga4py import ga # initialize Global Arrays
me = ga.nodeid()
def print_distribution(g_a):
for i in range(ga.nnodes()):
lo, hi = ga.distribution(g_a, i)
print "%s lo=%s hi=%s" % (i, lo, hi)
# create some arrays
g_a = ga.create(ga.C_DBL, (10, 20, 30), chunk=(-1, 20, -1))
g_b = ga.create(ga.C_DBL, (10, 20, 30), chunk=(10, -1, -1))
if not me:
print_distribution(g_a)
print_distribution(g_b)
ga.fill(g_a, 6)
ga.copy(g_a, g_b)
if not me:
buffer = ga.access(g_b)
print buffer.shape
print buffer
def verify_using_np(g_a, g_b, g_c):
a = ga.get(g_a)
b = ga.get(g_b)
c = ga.get(g_c)
v = np.dot(a,b)
val = int(np.abs(np.sum(c-v))>0.0001)
val = ga.gop_add(val)
return val == 0
if __name__ == '__main__':
if nproc > MULTIPLIER**3:
if 0 == me:
print "You must use less than %s processors" % (MULTIPLIER**3+1)
else:
g_a = ga.create(ga.C_DBL, [N,N])
g_b = ga.create(ga.C_DBL, [N,N])
g_c = ga.create(ga.C_DBL, [N,N])
g_counter = ga.create(ga.C_INT, [1])
ga.zero(g_counter)
# put some fake data into input arrays A and B
if me == 0:
ga.put(g_a, np.random.random(N*N))
ga.put(g_b, np.random.random(N*N))
ga.sync()
if me == 0:
print "srumma...",
srumma(g_a, g_b, g_c, CHUNK_SIZE, MULTIPLIER, g_counter)
if me == 0:
print "done"
if me == 0:
def verify_using_np(g_a, g_b, g_c):
a = ga.get(g_a)
b = ga.get(g_b)
c = ga.get(g_c)
v = np.dot(a, b)
val = int(np.abs(np.sum(c - v)) > 0.0001)
val = ga.gop_add(val)
return val == 0
if __name__ == '__main__':
if nproc > MULTIPLIER**3:
if 0 == me:
print "You must use less than %s processors" % (MULTIPLIER**3 + 1)
else:
g_a = ga.create(ga.C_DBL, [N, N])
g_b = ga.create(ga.C_DBL, [N, N])
g_c = ga.create(ga.C_DBL, [N, N])
g_counter = ga.create(ga.C_INT, [1])
ga.zero(g_counter)
# put some fake data into input arrays A and B
if me == 0:
ga.put(g_a, np.random.random(N * N))
ga.put(g_b, np.random.random(N * N))
ga.sync()
if me == 0:
print "srumma...",
srumma(g_a, g_b, g_c, CHUNK_SIZE, MULTIPLIER, g_counter)
if me == 0:
print "done"
if me == 0:
def verify_using_np(g_a, g_b, g_c):
a = ga.get(g_a)
b = ga.get(g_b)
c = ga.get(g_c)
v = np.dot(a, b)
val = int(np.abs(np.sum(c - v)) > 0.0001)
val = ga.gop_add(val)
return val == 0
if __name__ == '__main__':
if nproc > MULTIPLIER**3:
if 0 == me:
print "You must use less than %s processors" % (MULTIPLIER**3 + 1)
else:
g_a = ga.create(ga.C_DBL, [N, N])
g_b = ga.create(ga.C_DBL, [N, N])
g_c = ga.create(ga.C_DBL, [N, N])
# put some fake data into input arrays A and B
if me == 0:
ga.put(g_a, np.random.random(N * N))
ga.put(g_b, np.random.random(N * N))
ga.sync()
if me == 0:
print "srumma...",
srumma(g_a, g_b, g_c, CHUNK_SIZE, MULTIPLIER)
if me == 0:
print "done"
if me == 0:
print "verifying using ga.gemm...",
ok = verify_using_ga(g_a, g_b, g_c)
if DEBUG:
print_sync(value)
return value < EPSILON
def convergence_test_L2(g_a, g_b):
# compute L2 norm of change
# subtract g_b from g_a, results stored in g_b
ga.add(g_a, g_b, g_b, beta=-1)
# compute elementwise dot product (i.e. treats N-d arrays as vectors)
value = ga.dot(g_b, g_b)
if DEBUG:
print_sync(value)
return value < EPSILON
# create GA, distribute entire rows
g_a = ga.create(ga.C_FLOAT, (dim,dim), chunk=(0,dim))
# create a duplicate GA for the convergence test
g_b = ga.duplicate(g_a)
# process 0 initializes global array
# Note: alternatively, each process could initialize its local data using
# ga.access() and ga.distribution()
a = np.zeros((dim,dim), dtype=np.float32)
if rank == 0:
a[0,:] = 100 #top row
a[:,0] = 75 #left column
a[:,a.shape[0] - 1] = 50 #right column
ga.put(g_a, a)
ga.sync()
# which piece of array do I own?
import mpi4py.MPI # initialize Message Passing Interface
from ga4py import ga # initialize Global Arrays
me = ga.nodeid()
def print_distribution(g_a):
for i in range(ga.nnodes()):
lo,hi = ga.distribution(g_a, i)
print "%s lo=%s hi=%s" % (i,lo,hi)
# create some arrays
g_a = ga.create(ga.C_DBL, (10,20,30), chunk=(-1,20,-1))
g_b = ga.create(ga.C_DBL, (10,20,30), chunk=(10,-1,-1))
if not me:
print_distribution(g_a)
print_distribution(g_b)
ga.fill(g_a, 6)
ga.copy(g_a,g_b)
if not me:
buffer = ga.access(g_b)
print buffer.shape
print buffer
"""Use ga.access() to sum locally per SMP node."""
import mpi4py.MPI
from ga4py import ga
import numpy as np
# Okay, we create the global array
g_a = ga.create(ga.C_DBL, (3, 4, 5, 6))
if world_id == 0:
ga.put(g_a, np.arange(3 * 4 * 5 * 6))
ga.sync()
# You're on your own!
import mpi4py.MPI # initialize Message Passing Interface
from ga4py import ga # initialize Global Arrays
import numpy as np
me = ga.nodeid()
nproc = ga.nnodes()
def print_distribution(g_a):
for i in range(ga.nnodes()):
lo,hi = ga.distribution(g_a, i)
print "P=%s lo=%s hi=%s" % (i,lo,hi)
# create some irregular arrays
block = [3,2]
map = [0,2,6,0,5]
if nproc < np.prod(block):
raise ValueError, "ERROR: fewer procs than requested blocks"
g_a = ga.create_irreg(ga.C_DBL, [8,10], block, map, "Array A")
if not g_a:
ga.error("Could not create global array A",g_a)
g_b = ga.create(ga.C_INT, (2,3,4,5,6))
if not me:
print_distribution(g_a)
print_distribution(g_b)
def comp_pi(n, myrank=0, nprocs=1):
h = 1.0 / n;
s = 0.0;
for i in xrange(myrank + 1, n + 1, nprocs):
x = h * (i - 0.5);
s += 4.0 / (1.0 + x**2);
return s * h
def prn_pi(pi, PI):
message = "pi is approximately %.16f, error is %.16f"
print (message % (pi, abs(pi - PI)))
nprocs = ga.nnodes()
myrank = ga.nodeid()
g_pi = ga.create(ga.C_DBL, [1])
one_time = False
if len(sys.argv) == 2:
n = int(sys.argv[1])
one_time = True
while True:
if not one_time:
if myrank == 0:
n = get_n()
n = ga.brdcst(n)
else:
n = ga.brdcst(0)
if n == 0:
break
def parallel_task():
me = ga.pgroup_nodeid()
nproc = ga.pgroup_nnodes()
### print a message from the master of the group
g_a = ga.create(ga.C_DBL, (3,4,5))
ga.randomize(g_a)
import mpi4py.MPI # initialize Message Passing Interface
from ga4py import ga # initialize Global Arrays
me = ga.nodeid()
def print_distribution(g_a):
for i in range(ga.nnodes()):
lo,hi = ga.distribution(g_a, i)
print "%s lo=%s hi=%s" % (i,lo,hi)
# create some arrays
g_a = ga.create(ga.C_DBL, (10,20,30))
g_b = ga.create(ga.C_INT, (2,3,4,5,6))
if not me:
print_distribution(g_a)
print_distribution(g_b)
import mpi4py.MPI # initialize Message Passing Interface
from ga4py import ga # initialize Global Arrays
import numpy as np
me = ga.nodeid()
nproc = ga.nnodes()
def print_distribution(g_a):
for i in range(ga.nnodes()):
lo, hi = ga.distribution(g_a, i)
print "P=%s lo=%s hi=%s" % (i, lo, hi)
# create some irregular arrays
block = [3, 2]
map = [0, 2, 6, 0, 5]
if nproc < np.prod(block):
raise ValueError, "ERROR: fewer procs than requested blocks"
g_a = ga.create_irreg(ga.C_DBL, [8, 10], block, map, "Array A")
if not g_a:
ga.error("Could not create global array A", g_a)
g_b = ga.create(ga.C_INT, (2, 3, 4, 5, 6))
if not me:
print_distribution(g_a)
print_distribution(g_b)
