def calculate_blocked_density_matrix(self, f_n, C_nM):
nbands = self.bd.nbands
mynbands = self.bd.mynbands
nao = self.nao
dtype = C_nM.dtype
self.nMdescriptor.checkassert(C_nM)
if self.gd.rank == 0:
Cf_nM = (C_nM * f_n[:, None]).conj()
else:
C_nM = self.nM_unique_descriptor.zeros(dtype=dtype)
Cf_nM = self.nM_unique_descriptor.zeros(dtype=dtype)
r = Redistributor(self.block_comm, self.nM_unique_descriptor,
self.mmdescriptor)
Cf_mm = self.mmdescriptor.zeros(dtype=dtype)
r.redistribute(Cf_nM, Cf_mm, nbands, nao)
del Cf_nM
C_mm = self.mmdescriptor.zeros(dtype=dtype)
r.redistribute(C_nM, C_mm, nbands, nao)
# no use to delete C_nM as it's in the input...
rho_mm = self.mmdescriptor.zeros(dtype=dtype)
pblas_simple_gemm(self.mmdescriptor,
self.mmdescriptor,
self.mmdescriptor,
Cf_mm, C_mm, rho_mm, transa='T')
return rho_mm
def calculate_density_matrix(self, f_n, C_nM, rho_mM=None):
nbands = self.bd.nbands
mynbands = self.bd.mynbands
nao = self.nao
if rho_mM is None:
rho_mM = self.mMdescriptor.zeros(dtype=C_nM.dtype)
Cf_nM = (C_nM * f_n[:, None]).conj()
pblas_simple_gemm(self.nMdescriptor, self.nMdescriptor,
self.mMdescriptor, Cf_nM, C_nM, rho_mM, transa='T')
return rho_mM
def oldcalculate_density_matrix(self, f_n, C_nM, rho_mM=None):
# This version is parallel over the band descriptor only.
# This is inefficient, but let's keep it for a while in case
# there's trouble with the more efficient version
nbands = self.bd.nbands
mynbands = self.bd.mynbands
nao = self.nao
if rho_mM is None:
rho_mM = self.mMdescriptor.zeros(dtype=C_nM.dtype)
Cf_nM = (C_nM * f_n[:, None]).conj()
pblas_simple_gemm(self.nMdescriptor, self.nMdescriptor,
self.mMdescriptor, Cf_nM, C_nM, rho_mM, transa='T')
return rho_mM
# simulate state-parallelization=2 and
# domain-decomposition.prod=32
B = 2
D = 32
mb = 32
grid = BlacsGrid(world, B, D)
nbands = 500
nG = 80**3
nGdesc = grid.new_descriptor(nbands, nG, nbands // B, nG // D)
nndesc = grid.new_descriptor(nbands, nbands, mb, mb)
psit_nG = gen.rand(*nGdesc.shape)
A_nn = gen.rand(*nndesc.shape)
assert nGdesc.check(psit_nG)
assert nndesc.check(A_nn)
parallelprint(world, (A_nn.shape, nndesc.shape, nndesc.lld))
pblas_simple_gemm(nGdesc,
nGdesc,
nndesc,
psit_nG,
psit_nG,
A_nn,
transa='N',
transb='T')
def main(M=160, N=120, K=140, seed=42, mprocs=2, nprocs=2, dtype=float):
gen = np.random.RandomState(seed)
grid = BlacsGrid(world, mprocs, nprocs)
if dtype == complex:
epsilon = 1.0j
else:
epsilon = 0.0
# Create descriptors for matrices on master:
globA = grid.new_descriptor(M, K, M, K)
globB = grid.new_descriptor(K, N, K, N)
globC = grid.new_descriptor(M, N, M, N)
globZ = grid.new_descriptor(K, K, K, K)
globX = grid.new_descriptor(K, 1, K, 1)
globY = grid.new_descriptor(M, 1, M, 1)
globD = grid.new_descriptor(M, K, M, K)
globS = grid.new_descriptor(M, M, M, M)
globU = grid.new_descriptor(M, M, M, M)
globHEC = grid.new_descriptor(K, K, K, K)
# print globA.asarray()
# Populate matrices local to master:
A0 = gen.rand(*globA.shape) + epsilon * gen.rand(*globA.shape)
B0 = gen.rand(*globB.shape) + epsilon * gen.rand(*globB.shape)
D0 = gen.rand(*globD.shape) + epsilon * gen.rand(*globD.shape)
X0 = gen.rand(*globX.shape) + epsilon * gen.rand(*globX.shape)
# HEC = HEA * B
HEA0 = gen.rand(*globHEC.shape) + epsilon * gen.rand(*globHEC.shape)
if world.rank == 0:
HEA0 = HEA0 + HEA0.T.conjugate() # Make H0 hermitean
HEA0 = np.ascontiguousarray(HEA0)
# Local result matrices
Y0 = globY.empty(dtype=dtype)
C0 = globC.zeros(dtype=dtype)
Z0 = globZ.zeros(dtype=dtype)
S0 = globS.zeros(dtype=dtype) # zeros needed for rank-updates
U0 = globU.zeros(dtype=dtype) # zeros needed for rank-updates
HEC0 = globB.zeros(dtype=dtype)
# Local reference matrix product:
if rank == 0:
# C0[:] = np.dot(A0, B0)
gemm(1.0, B0, A0, 0.0, C0)
# gemm(1.0, A0, A0, 0.0, Z0, transa='t')
print(A0.shape, Z0.shape)
Z0[:] = np.dot(A0.T, A0)
# Y0[:] = np.dot(A0, X0)
gemv(1.0, A0, X0.ravel(), 0.0, Y0.ravel())
r2k(1.0, A0, D0, 0.0, S0)
rk(1.0, A0, 0.0, U0)
HEC0[:] = np.dot(HEA0, B0)
sM, sN = HEA0.shape
# We don't use upper diagonal
for i in range(sM):
for j in range(sN):
if i < j:
HEA0[i][j] = 99999.0
if world.rank == 0:
print(HEA0)
assert globA.check(A0) and globB.check(B0) and globC.check(C0)
assert globX.check(X0) and globY.check(Y0)
assert globD.check(D0) and globS.check(S0) and globU.check(U0)
# Create distributed destriptors with various block sizes:
distA = grid.new_descriptor(M, K, 2, 2)
distB = grid.new_descriptor(K, N, 2, 4)
distC = grid.new_descriptor(M, N, 3, 2)
distZ = grid.new_descriptor(K, K, 5, 7)
distX = grid.new_descriptor(K, 1, 4, 1)
distY = grid.new_descriptor(M, 1, 3, 1)
distD = grid.new_descriptor(M, K, 2, 3)
distS = grid.new_descriptor(M, M, 2, 2)
distU = grid.new_descriptor(M, M, 2, 2)
distHE = grid.new_descriptor(K, K, 2, 4)
# Distributed matrices:
A = distA.empty(dtype=dtype)
B = distB.empty(dtype=dtype)
C = distC.empty(dtype=dtype)
Z = distZ.empty(dtype=dtype)
X = distX.empty(dtype=dtype)
Y = distY.empty(dtype=dtype)
D = distD.empty(dtype=dtype)
S = distS.zeros(dtype=dtype) # zeros needed for rank-updates
U = distU.zeros(dtype=dtype) # zeros needed for rank-updates
HEC = distB.zeros(dtype=dtype)
HEA = distHE.zeros(dtype=dtype)
Redistributor(world, globA, distA).redistribute(A0, A)
Redistributor(world, globB, distB).redistribute(B0, B)
Redistributor(world, globX, distX).redistribute(X0, X)
Redistributor(world, globD, distD).redistribute(D0, D)
Redistributor(world, globHEC, distHE).redistribute(HEA0, HEA)
pblas_simple_gemm(distA, distB, distC, A, B, C)
pblas_simple_gemm(distA, distA, distZ, A, A, Z, transa='T')
pblas_simple_gemv(distA, distX, distY, A, X, Y)
pblas_simple_r2k(distA, distD, distS, A, D, S)
pblas_simple_rk(distA, distU, A, U)
pblas_simple_hemm(distHE, distB, distB, HEA, B, HEC, uplo='L', side='L')
# Collect result back on master
C1 = globC.empty(dtype=dtype)
Y1 = globY.empty(dtype=dtype)
S1 = globS.zeros(dtype=dtype) # zeros needed for rank-updates
U1 = globU.zeros(dtype=dtype) # zeros needed for rank-updates
HEC1 = globB.zeros(dtype=dtype)
Redistributor(world, distC, globC).redistribute(C, C1)
Redistributor(world, distY, globY).redistribute(Y, Y1)
Redistributor(world, distS, globS).redistribute(S, S1)
Redistributor(world, distU, globU).redistribute(U, U1)
Redistributor(world, distB, globB).redistribute(HEC, HEC1)
if rank == 0:
gemm_err = abs(C1 - C0).max()
gemv_err = abs(Y1 - Y0).max()
r2k_err = abs(S1 - S0).max()
rk_err = abs(U1 - U0).max()
hemm_err = abs(HEC1 - HEC0).max()
print('gemm err', gemm_err)
print('gemv err', gemv_err)
print('r2k err', r2k_err)
print('rk_err', rk_err)
print('hemm_err', hemm_err)
else:
gemm_err = 0.0
gemv_err = 0.0
r2k_err = 0.0
rk_err = 0.0
hemm_err = 0.0
gemm_err = world.sum(gemm_err) # We don't like exceptions on only one cpu
gemv_err = world.sum(gemv_err)
r2k_err = world.sum(r2k_err)
rk_err = world.sum(rk_err)
hemm_err = world.sum(hemm_err)
equal(gemm_err, 0, tol)
equal(gemv_err, 0, tol)
equal(r2k_err, 0, tol)
equal(rk_err, 0, tol)
equal(hemm_err, 0, tol)
import numpy as np from gpaw.blacs import BlacsGrid, parallelprint from gpaw.mpi import world, rank, size from gpaw.utilities.scalapack import pblas_simple_gemm gen = np.random.RandomState(42) # simulate state-parallelization=2 and # domain-decomposition.prod=32 B = 2 D = 32 mb = 32 grid = BlacsGrid(world, B, D) nbands = 500 nG = 80 ** 3 nGdesc = grid.new_descriptor(nbands, nG, nbands / B, nG / D) nndesc = grid.new_descriptor(nbands, nbands, mb, mb) psit_nG = gen.rand(*nGdesc.shape) A_nn = gen.rand(*nndesc.shape) assert nGdesc.check(psit_nG) assert nndesc.check(A_nn) parallelprint(world, (A_nn.shape, nndesc.shape, nndesc.lld)) pblas_simple_gemm(nGdesc, nGdesc, nndesc, psit_nG, psit_nG, A_nn, transa="N", transb="T")
def calculate_blocked_density_matrix(self, f_n, C_nM):
nbands = self.bd.nbands
nao = self.nao
dtype = C_nM.dtype
self.nMdescriptor.checkassert(C_nM)
if self.gd.rank == 0:
Cf_nM = (C_nM * f_n[:, None])
else:
C_nM = self.nM_unique_descriptor.zeros(dtype=dtype)
Cf_nM = self.nM_unique_descriptor.zeros(dtype=dtype)
r = Redistributor(self.block_comm, self.nM_unique_descriptor,
self.mmdescriptor)
Cf_mm = self.mmdescriptor.zeros(dtype=dtype)
r.redistribute(Cf_nM, Cf_mm, nbands, nao)
del Cf_nM
C_mm = self.mmdescriptor.zeros(dtype=dtype)
r.redistribute(C_nM, C_mm, nbands, nao)
# no use to delete C_nM as it's in the input...
rho_mm = self.mmdescriptor.zeros(dtype=dtype)
if 1: # if self.libelpa is None:
pblas_simple_gemm(self.mmdescriptor,
self.mmdescriptor,
self.mmdescriptor,
Cf_mm,
C_mm,
rho_mm,
transa='C')
else:
# elpa_hermitian_multiply was not faster than the ordinary
# multiplication in the test. The way we have things distributed,
# we need to transpose things at the moment.
#
# Rather than enabling this, we should store the coefficients
# in an appropriate 2D block cyclic format (c_nm) and not the
# current C_nM format. This makes it possible to avoid
# redistributing the coefficients at all. But we don't have time
# to implement this at the moment.
mul = self.libelpa.hermitian_multiply
desc = self.mmdescriptor
from gpaw.utilities.scalapack import pblas_tran
def T(array):
tmp = array.copy()
pblas_tran(alpha=1.0,
a_MN=tmp,
beta=0.0,
c_NM=array,
desca=desc,
descc=desc)
T(C_mm)
T(Cf_mm)
mul(C_mm, Cf_mm, rho_mm, desc, desc, desc, uplo_a='X', uplo_c='X')
return rho_mm
def linear_propagator(self, sourceC_nM, targetC_nM, S_MM, H_MM, dt):
self.timer.start('Linear solve')
if self.blacs:
# XXX, Preallocate
target_blockC_nm = self.Cnm_block_descriptor.empty(dtype=complex)
temp_blockC_nm = self.Cnm_block_descriptor.empty(dtype=complex)
temp_block_mm = self.mm_block_descriptor.empty(dtype=complex)
if self.density.gd.comm.rank != 0:
# XXX Fake blacks nbands, nao, nbands, nao grid because some
# weird asserts
# (these are 0,x or x,0 arrays)
sourceC_nM = self.CnM_unique_descriptor.zeros(dtype=complex)
# 1. target = (S+0.5j*H*dt) * source
# Wave functions to target
self.CnM2nm.redistribute(sourceC_nM, temp_blockC_nm)
# XXX It can't be this f'n hard to symmetrize a matrix (tri2full)
# Remove upper diagonal
scalapack_zero(self.mm_block_descriptor, H_MM, 'U')
# Lower diagonal matrix:
temp_block_mm[:] = S_MM - (0.5j * dt) * H_MM
scalapack_set(self.mm_block_descriptor, temp_block_mm, 0, 0, 'U')
# Note it's strictly lower diagonal matrix
# Add transpose of H
pblas_tran(-0.5j * dt, H_MM, 1.0, temp_block_mm,
self.mm_block_descriptor, self.mm_block_descriptor)
# Add transpose of S
pblas_tran(1.0, S_MM, 1.0, temp_block_mm, self.mm_block_descriptor,
self.mm_block_descriptor)
pblas_simple_gemm(self.Cnm_block_descriptor,
self.mm_block_descriptor,
self.Cnm_block_descriptor, temp_blockC_nm,
temp_block_mm, target_blockC_nm)
# 2. target = (S-0.5j*H*dt)^-1 * target
# temp_block_mm[:] = S_MM + (0.5j*dt) * H_MM
# XXX It can't be this f'n hard to symmetrize a matrix (tri2full)
# Lower diagonal matrix:
temp_block_mm[:] = S_MM + (0.5j * dt) * H_MM
# Not it's stricly lower diagonal matrix:
scalapack_set(self.mm_block_descriptor, temp_block_mm, 0, 0, 'U')
# Add transpose of H:
pblas_tran(+0.5j * dt, H_MM, 1.0, temp_block_mm,
self.mm_block_descriptor, self.mm_block_descriptor)
# Add transpose of S
pblas_tran(1.0, S_MM, 1.0, temp_block_mm, self.mm_block_descriptor,
self.mm_block_descriptor)
scalapack_solve(self.mm_block_descriptor,
self.Cnm_block_descriptor, temp_block_mm,
target_blockC_nm)
if self.density.gd.comm.rank != 0: # XXX is this correct?
# XXX Fake blacks nbands, nao, nbands, nao grid because some
# weird asserts
# (these are 0,x or x,0 arrays)
target = self.CnM_unique_descriptor.zeros(dtype=complex)
else:
target = targetC_nM
self.Cnm2nM.redistribute(target_blockC_nm, target)
self.density.gd.comm.broadcast(targetC_nM, 0) # Is this required?
else:
# Note: The full equation is conjugated (therefore -+, not +-)
targetC_nM[:] = \
solve(S_MM - 0.5j * H_MM * dt,
np.dot(S_MM + 0.5j * H_MM * dt,
sourceC_nM.T.conjugate())).T.conjugate()
self.timer.stop('Linear solve')
def linear_propagator(self, sourceC_nM, targetC_nM, S_MM, H_MM, dt):
self.timer.start('Linear solve')
# XXX Debugging stuff. Remove
if self.propagator_debug:
if self.blacs:
globalH_MM = self.blacs_mm_to_global(H_MM)
globalS_MM = self.blacs_mm_to_global(S_MM)
if world.rank == 0:
tri2full(globalS_MM, 'L')
tri2full(globalH_MM, 'L')
U_MM = dot(inv(globalS_MM-0.5j*globalH_MM*dt), globalS_MM+0.5j*globalH_MM*dt)
debugC_nM = dot(sourceC_nM, U_MM.T.conjugate())
#print 'PASS PROPAGATOR'
#debugC_nM = sourceC_nM.copy()
else:
if world.rank == 0:
U_MM = dot(inv(S_MM-0.5j*H_MM*dt), S_MM+0.5j*H_MM*dt)
debugC_nM = dot(sourceC_nM, U_MM.T.conjugate())
#print 'PASS PROPAGATOR'
#debugC_nM = sourceC_nM.copy()
if self.blacs:
target_blockC_nm = self.Cnm_block_descriptor.empty(dtype=complex) # XXX, Preallocate
temp_blockC_nm = self.Cnm_block_descriptor.empty(dtype=complex) # XXX, Preallocate
temp_block_mm = self.mm_block_descriptor.empty(dtype=complex)
if self.density.gd.comm.rank != 0:
# XXX Fake blacks nbands, nao, nbands, nao grid because some weird asserts
# (these are 0,x or x,0 arrays)
sourceC_nM = self.CnM_unique_descriptor.zeros(dtype=complex)
# 1. target = (S+0.5j*H*dt) * source
# Wave functions to target
self.CnM2nm.redistribute(sourceC_nM, temp_blockC_nm)
# XXX It can't be this f'n hard to symmetrize a matrix (tri2full)
scalapack_zero(self.mm_block_descriptor, H_MM, 'U') # Remove upper diagonal
temp_block_mm[:] = S_MM - (0.5j*dt) * H_MM # Lower diagonal matrix
scalapack_set(self.mm_block_descriptor, temp_block_mm, 0, 0, 'U')
# Note it's stricly lower diagonal matrix
pblas_tran(-0.5j*dt, H_MM, 1.0, temp_block_mm, self.mm_block_descriptor, self.mm_block_descriptor) # Add transpose of H
pblas_tran(1.0, S_MM, 1.0, temp_block_mm, self.mm_block_descriptor, self.mm_block_descriptor) # Add transpose of S
pblas_simple_gemm(self.Cnm_block_descriptor,
self.mm_block_descriptor,
self.Cnm_block_descriptor,
temp_blockC_nm,
temp_block_mm,
target_blockC_nm)
# 2. target = (S-0.5j*H*dt)^-1 * target
#temp_block_mm[:] = S_MM + (0.5j*dt) * H_MM
# XXX It can't be this f'n hard to symmetrize a matrix (tri2full)
temp_block_mm[:] = S_MM + (0.5j*dt) * H_MM # Lower diagonal matrix
scalapack_set(self.mm_block_descriptor, temp_block_mm, 0, 0, 'U') # Not it's stricly lower diagonal matrix
pblas_tran(+0.5j*dt, H_MM, 1.0, temp_block_mm, self.mm_block_descriptor, self.mm_block_descriptor) # Add transpose of H
pblas_tran(1.0, S_MM, 1.0, temp_block_mm, self.mm_block_descriptor, self.mm_block_descriptor) # Add transpose of S
scalapack_solve(self.mm_block_descriptor,
self.Cnm_block_descriptor,
temp_block_mm,
target_blockC_nm)
if self.density.gd.comm.rank != 0: # XXX is this correct?
# XXX Fake blacks nbands, nao, nbands, nao grid because some weird asserts
# (these are 0,x or x,0 arrays)
target = self.CnM_unique_descriptor.zeros(dtype=complex)
else:
target = targetC_nM
self.Cnm2nM.redistribute(target_blockC_nm, target)
self.density.gd.comm.broadcast(targetC_nM, 0) # Is this required?
else:
# Note: The full equation is conjugated (therefore -+, not +-)
targetC_nM[:] = solve(S_MM-0.5j*H_MM*dt, np.dot(S_MM+0.5j*H_MM*dt, sourceC_nM.T.conjugate())).T.conjugate()
# XXX Debugging stuff. Remove
if self.propagator_debug:
if world.rank == 0:
verify(targetC_nM, debugC_nM,
'Linear solver propagator vs. reference')
self.timer.stop('Linear solve')
def main(M=160, N=120, K=140, seed=42, mprocs=2, nprocs=2, dtype=float):
gen = np.random.RandomState(seed)
grid = BlacsGrid(world, mprocs, nprocs)
if (dtype==complex):
epsilon = 1.0j
else:
epsilon = 0.0
# Create descriptors for matrices on master:
globA = grid.new_descriptor(M, K, M, K)
globB = grid.new_descriptor(K, N, K, N)
globC = grid.new_descriptor(M, N, M, N)
globZ = grid.new_descriptor(K, K, K, K)
globX = grid.new_descriptor(K, 1, K, 1)
globY = grid.new_descriptor(M, 1, M, 1)
globD = grid.new_descriptor(M, K, M, K)
globS = grid.new_descriptor(M, M, M, M)
globU = grid.new_descriptor(M, M, M, M)
# print globA.asarray()
# Populate matrices local to master:
A0 = gen.rand(*globA.shape) + epsilon * gen.rand(*globA.shape)
B0 = gen.rand(*globB.shape) + epsilon * gen.rand(*globB.shape)
D0 = gen.rand(*globD.shape) + epsilon * gen.rand(*globD.shape)
X0 = gen.rand(*globX.shape) + epsilon * gen.rand(*globX.shape)
# Local result matrices
Y0 = globY.empty(dtype=dtype)
C0 = globC.zeros(dtype=dtype)
Z0 = globZ.zeros(dtype=dtype)
S0 = globS.zeros(dtype=dtype) # zeros needed for rank-updates
U0 = globU.zeros(dtype=dtype) # zeros needed for rank-updates
# Local reference matrix product:
if rank == 0:
# C0[:] = np.dot(A0, B0)
gemm(1.0, B0, A0, 0.0, C0)
#gemm(1.0, A0, A0, 0.0, Z0, transa='t')
print A0.shape, Z0.shape
Z0[:] = np.dot(A0.T, A0)
# Y0[:] = np.dot(A0, X0)
gemv(1.0, A0, X0.ravel(), 0.0, Y0.ravel())
r2k(1.0, A0, D0, 0.0, S0)
rk(1.0, A0, 0.0, U0)
assert globA.check(A0) and globB.check(B0) and globC.check(C0)
assert globX.check(X0) and globY.check(Y0)
assert globD.check(D0) and globS.check(S0) and globU.check(U0)
# Create distributed destriptors with various block sizes:
distA = grid.new_descriptor(M, K, 2, 2)
distB = grid.new_descriptor(K, N, 2, 4)
distC = grid.new_descriptor(M, N, 3, 2)
distZ = grid.new_descriptor(K, K, 5, 7)
distX = grid.new_descriptor(K, 1, 4, 1)
distY = grid.new_descriptor(M, 1, 3, 1)
distD = grid.new_descriptor(M, K, 2, 3)
distS = grid.new_descriptor(M, M, 2, 2)
distU = grid.new_descriptor(M, M, 2, 2)
# Distributed matrices:
A = distA.empty(dtype=dtype)
B = distB.empty(dtype=dtype)
C = distC.empty(dtype=dtype)
Z = distZ.empty(dtype=dtype)
X = distX.empty(dtype=dtype)
Y = distY.empty(dtype=dtype)
D = distD.empty(dtype=dtype)
S = distS.zeros(dtype=dtype) # zeros needed for rank-updates
U = distU.zeros(dtype=dtype) # zeros needed for rank-updates
Redistributor(world, globA, distA).redistribute(A0, A)
Redistributor(world, globB, distB).redistribute(B0, B)
Redistributor(world, globX, distX).redistribute(X0, X)
Redistributor(world, globD, distD).redistribute(D0, D)
pblas_simple_gemm(distA, distB, distC, A, B, C)
pblas_simple_gemm(distA, distA, distZ, A, A, Z, transa='T')
pblas_simple_gemv(distA, distX, distY, A, X, Y)
pblas_simple_r2k(distA, distD, distS, A, D, S)
pblas_simple_rk(distA, distU, A, U)
# Collect result back on master
C1 = globC.empty(dtype=dtype)
Y1 = globY.empty(dtype=dtype)
S1 = globS.zeros(dtype=dtype) # zeros needed for rank-updates
U1 = globU.zeros(dtype=dtype) # zeros needed for rank-updates
Redistributor(world, distC, globC).redistribute(C, C1)
Redistributor(world, distY, globY).redistribute(Y, Y1)
Redistributor(world, distS, globS).redistribute(S, S1)
Redistributor(world, distU, globU).redistribute(U, U1)
if rank == 0:
gemm_err = abs(C1 - C0).max()
gemv_err = abs(Y1 - Y0).max()
r2k_err = abs(S1 - S0).max()
rk_err = abs(U1 - U0).max()
print 'gemm err', gemm_err
print 'gemv err', gemv_err
print 'r2k err' , r2k_err
print 'rk_err' , rk_err
else:
gemm_err = 0.0
gemv_err = 0.0
r2k_err = 0.0
rk_err = 0.0
gemm_err = world.sum(gemm_err) # We don't like exceptions on only one cpu
gemv_err = world.sum(gemv_err)
r2k_err = world.sum(r2k_err)
rk_err = world.sum(rk_err)
equal(gemm_err, 0, tol)
equal(gemv_err, 0, tol)
equal(r2k_err, 0, tol)
equal(rk_err,0, tol)
from gpaw.blacs import BlacsGrid, parallelprint
from gpaw.mpi import world, rank, size
from gpaw.utilities.scalapack import pblas_simple_gemm
gen = np.random.RandomState(42)
# simulate state-parallelization=2 and
# domain-decomposition.prod=32
B = 2
D = 32
mb = 32
grid = BlacsGrid(world, B, D)
nbands = 500
nG = 80**3
nGdesc = grid.new_descriptor(nbands, nG, nbands/B, nG/D)
nndesc = grid.new_descriptor(nbands, nbands, mb, mb)
psit_nG = gen.rand(*nGdesc.shape)
A_nn = gen.rand(*nndesc.shape)
assert nGdesc.check(psit_nG)
assert nndesc.check(A_nn)
parallelprint(world, (A_nn.shape, nndesc.shape, nndesc.lld))
pblas_simple_gemm(nGdesc, nGdesc, nndesc, psit_nG, psit_nG, A_nn,
transa='N', transb='T')
def main(M=160, N=120, K=140, seed=42, mprocs=2, nprocs=2, dtype=float):
gen = np.random.RandomState(seed)
grid = BlacsGrid(world, mprocs, nprocs)
if dtype == complex:
epsilon = 1.0j
else:
epsilon = 0.0
# Create descriptors for matrices on master:
globA = grid.new_descriptor(M, K, M, K)
globB = grid.new_descriptor(K, N, K, N)
globC = grid.new_descriptor(M, N, M, N)
globZ = grid.new_descriptor(K, K, K, K)
globX = grid.new_descriptor(K, 1, K, 1)
globY = grid.new_descriptor(M, 1, M, 1)
globD = grid.new_descriptor(M, K, M, K)
globS = grid.new_descriptor(M, M, M, M)
globU = grid.new_descriptor(M, M, M, M)
globHEC = grid.new_descriptor(K, K, K, K)
# print globA.asarray()
# Populate matrices local to master:
A0 = gen.rand(*globA.shape) + epsilon * gen.rand(*globA.shape)
B0 = gen.rand(*globB.shape) + epsilon * gen.rand(*globB.shape)
D0 = gen.rand(*globD.shape) + epsilon * gen.rand(*globD.shape)
X0 = gen.rand(*globX.shape) + epsilon * gen.rand(*globX.shape)
# HEC = HEA * B
HEA0 = gen.rand(*globHEC.shape) + epsilon * gen.rand(*globHEC.shape)
if world.rank == 0:
HEA0 = HEA0 + HEA0.T.conjugate() # Make H0 hermitean
# Local result matrices
Y0 = globY.empty(dtype=dtype)
C0 = globC.zeros(dtype=dtype)
Z0 = globZ.zeros(dtype=dtype)
S0 = globS.zeros(dtype=dtype) # zeros needed for rank-updates
U0 = globU.zeros(dtype=dtype) # zeros needed for rank-updates
HEC0 = globB.zeros(dtype=dtype)
# Local reference matrix product:
if rank == 0:
# C0[:] = np.dot(A0, B0)
gemm(1.0, B0, A0, 0.0, C0)
# gemm(1.0, A0, A0, 0.0, Z0, transa='t')
print(A0.shape, Z0.shape)
Z0[:] = np.dot(A0.T, A0)
# Y0[:] = np.dot(A0, X0)
gemv(1.0, A0, X0.ravel(), 0.0, Y0.ravel())
r2k(1.0, A0, D0, 0.0, S0)
rk(1.0, A0, 0.0, U0)
HEC0[:] = np.dot(HEA0, B0)
sM, sN = HEA0.shape
# We don't use upper diagonal
for i in range(sM):
for j in range(sN):
if i < j:
HEA0[i][j] = 99999.0
if world.rank == 0:
print(HEA0)
assert globA.check(A0) and globB.check(B0) and globC.check(C0)
assert globX.check(X0) and globY.check(Y0)
assert globD.check(D0) and globS.check(S0) and globU.check(U0)
# Create distributed destriptors with various block sizes:
distA = grid.new_descriptor(M, K, 2, 2)
distB = grid.new_descriptor(K, N, 2, 4)
distC = grid.new_descriptor(M, N, 3, 2)
distZ = grid.new_descriptor(K, K, 5, 7)
distX = grid.new_descriptor(K, 1, 4, 1)
distY = grid.new_descriptor(M, 1, 3, 1)
distD = grid.new_descriptor(M, K, 2, 3)
distS = grid.new_descriptor(M, M, 2, 2)
distU = grid.new_descriptor(M, M, 2, 2)
distHE = grid.new_descriptor(K, K, 2, 4)
# Distributed matrices:
A = distA.empty(dtype=dtype)
B = distB.empty(dtype=dtype)
C = distC.empty(dtype=dtype)
Z = distZ.empty(dtype=dtype)
X = distX.empty(dtype=dtype)
Y = distY.empty(dtype=dtype)
D = distD.empty(dtype=dtype)
S = distS.zeros(dtype=dtype) # zeros needed for rank-updates
U = distU.zeros(dtype=dtype) # zeros needed for rank-updates
HEC = distB.zeros(dtype=dtype)
HEA = distHE.zeros(dtype=dtype)
Redistributor(world, globA, distA).redistribute(A0, A)
Redistributor(world, globB, distB).redistribute(B0, B)
Redistributor(world, globX, distX).redistribute(X0, X)
Redistributor(world, globD, distD).redistribute(D0, D)
Redistributor(world, globHEC, distHE).redistribute(HEA0, HEA)
pblas_simple_gemm(distA, distB, distC, A, B, C)
pblas_simple_gemm(distA, distA, distZ, A, A, Z, transa="T")
pblas_simple_gemv(distA, distX, distY, A, X, Y)
pblas_simple_r2k(distA, distD, distS, A, D, S)
pblas_simple_rk(distA, distU, A, U)
pblas_simple_hemm(distHE, distB, distB, HEA, B, HEC, uplo="L", side="L")
# Collect result back on master
C1 = globC.empty(dtype=dtype)
Y1 = globY.empty(dtype=dtype)
S1 = globS.zeros(dtype=dtype) # zeros needed for rank-updates
U1 = globU.zeros(dtype=dtype) # zeros needed for rank-updates
HEC1 = globB.zeros(dtype=dtype)
Redistributor(world, distC, globC).redistribute(C, C1)
Redistributor(world, distY, globY).redistribute(Y, Y1)
Redistributor(world, distS, globS).redistribute(S, S1)
Redistributor(world, distU, globU).redistribute(U, U1)
Redistributor(world, distB, globB).redistribute(HEC, HEC1)
if rank == 0:
gemm_err = abs(C1 - C0).max()
gemv_err = abs(Y1 - Y0).max()
r2k_err = abs(S1 - S0).max()
rk_err = abs(U1 - U0).max()
hemm_err = abs(HEC1 - HEC0).max()
print("gemm err", gemm_err)
print("gemv err", gemv_err)
print("r2k err", r2k_err)
print("rk_err", rk_err)
print("hemm_err", hemm_err)
else:
gemm_err = 0.0
gemv_err = 0.0
r2k_err = 0.0
rk_err = 0.0
hemm_err = 0.0
gemm_err = world.sum(gemm_err) # We don't like exceptions on only one cpu
gemv_err = world.sum(gemv_err)
r2k_err = world.sum(r2k_err)
rk_err = world.sum(rk_err)
hemm_err = world.sum(hemm_err)
equal(gemm_err, 0, tol)
equal(gemv_err, 0, tol)
equal(r2k_err, 0, tol)
equal(rk_err, 0, tol)
equal(hemm_err, 0, tol)
