def verify_using_ga(g_a, g_b, g_c):
g_v = ga.duplicate(g_c)
ga.gemm(False, False, N, N, N, 1, g_a, g_b, 0, g_v)
c = ga.access(g_c)
v = ga.access(g_v)
if c is not None:
val = int(np.abs(np.sum(c - v)) > 0.0001)
else:
val = 0
val = ga.gop_add(val)
ga.destroy(g_v)
return val == 0
def verify_using_ga(g_a, g_b, g_c):
g_v = ga.duplicate(g_c)
ga.gemm(False,False,N,N,N,1,g_a,g_b,0,g_v)
c = ga.access(g_c)
v = ga.access(g_v)
if c is not None:
val = int(np.abs(np.sum(c-v))>0.0001)
else:
val = 0
val = ga.gop_add(val)
ga.destroy(g_v)
return val == 0
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))
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 __call__(self, g_a):
a = ga.access(g_a)
if a is not None:
self.ufunc(a, a)
"""Use ga.access() to sum locally per SMP node."""
import mpi4py.MPI
from ga4py import ga
import numpy as np
world_id = ga.nodeid()
world_nproc = ga.nnodes()
node_id = ga.cluster_nodeid()
node_nproc = ga.cluster_nprocs(node_id)
node_me = ga.cluster_procid(node_id,ga.nodeid())
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()
if node_me == 0:
sum = 0
for i in range(node_nproc):
smp_neighbor_world_id = ga.cluster_procid(node_id,i)
buffer = ga.access(g_a, proc=smp_neighbor_world_id)
sum += np.sum(buffer)
print sum
# note that rhi and chi follow python range conventions i.e. [lo,hi)
(rlo,clo),(rhi,chi) = ga.distribution(g_a)
iteration = 0
start = ga.wtime()
while True:
iteration += 1
if iteration % HOW_MANY_STEPS_BEFORE_CONVERGENCE_TEST == 0:
# check for convergence will occur, so make a copy of the GA
ga.sync()
ga.copy(g_a, g_b)
# the iteration
if rlo == 0 and rhi == dim:
# I own the top and bottom rows
ga.sync()
my_array = ga.access(g_a)
my_array[1:-1,1:-1] = (
my_array[0:-2, 1:-1] +
my_array[2:, 1:-1] +
my_array[1:-1,0:-2] +
my_array[1:-1, 2:]) / 4
ga.release(g_a)
elif rlo == 0:
# I own the top rows, so get top row of next domain
next_domain_row = ga.get(g_a, (rhi,0), (rhi+1,dim))
ga.sync()
my_array = ga.access(g_a)
combined = np.vstack((my_array,next_domain_row))
my_array[1:,1:-1] = (
combined[0:-2, 1:-1] +
combined[2:, 1:-1] +
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
world_id = ga.nodeid()
world_nproc = ga.nnodes()
node_id = ga.cluster_nodeid()
node_nproc = ga.cluster_nprocs(node_id)
node_me = ga.cluster_procid(node_id, ga.nodeid())
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()
if node_me == 0:
sum = 0
for i in range(node_nproc):
smp_neighbor_world_id = ga.cluster_procid(node_id, i)
buffer = ga.access(g_a, proc=smp_neighbor_world_id)
sum += np.sum(buffer)
print sum
def __call__(self, g_a):
a = ga.access(g_a)
if a is not None:
self.ufunc(a,a)