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 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 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)
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 me == 0:
if ok:
print "OKAY"
else:
print "FAILED"
"""Use ga.access() to sum locally per SMP node."""
import mpi4py.MPI
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
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:
print "verifying using ga.gemm...",
ok = verify_using_ga(g_a, g_b, g_c)
if me == 0:
if ok:
print "OKAY"
else:
print "FAILED"
"""Use ga.access() to sum locally per SMP node."""
import mpi4py.MPI
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!