def get_phir(XK, XV, surface, xq, Cells, par_reac, ind_reac):
"""
It computes the reaction potential.
To compute this potential we need more terms in the Taylor expansion, that
is the reason why we need fine parameters (par_reac class) and a different
array of indices (ind_reac) than ind0.
Arguments
----------
XK : array, input for the double layer potential.
XV : array, input for the single layer potential.
surface : class, surface where we are computing the reaction potential.
xq : array, it contains the position of the charges.
Cells : array, it contains the tree cells.
par_reac: class, fine parameters related to the surface.
ind_reac: array, it contains the indices related to the treecode
computation.
Returns
--------
phi_reac: array, reaction potential.
AI_int : int, counter of the amount of near singular integrals solved.
"""
N = len(XK)
AI_int = 0
# Setup vector
K = par_reac.K
tic = time.time()
w = getWeights(K)
X_V = numpy.zeros(N * K)
X_Kx = numpy.zeros(N * K)
X_Ky = numpy.zeros(N * K)
X_Kz = numpy.zeros(N * K)
X_Kc = numpy.zeros(N * K)
X_Vc = numpy.zeros(N * K)
for i in range(N * K):
X_V[i] = XV[i // K] * w[i % K] * surface.area[i // K]
X_Kx[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 0]
X_Ky[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 1]
X_Kz[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 2]
X_Kc[i] = XK[i // K]
X_Vc[i] = XV[i // K]
toc = time.time()
# P2M
tic = time.time()
C = 0
getMultipole(Cells, C, surface.xj, surface.yj, surface.zj, X_V, X_Kx, X_Ky,
X_Kz, ind_reac, par_reac.P, par_reac.NCRIT)
toc = time.time()
time_P2M = toc - tic
# M2M
tic = time.time()
for C in reversed(range(1, len(Cells))):
PC = Cells[C].parent
upwardSweep(Cells, C, PC, par_reac.P, ind_reac.II, ind_reac.JJ,
ind_reac.KK, ind_reac.index, ind_reac.combII,
ind_reac.combJJ, ind_reac.combKK, ind_reac.IImii,
ind_reac.JJmjj, ind_reac.KKmkk, ind_reac.index_small,
ind_reac.index_ptr)
toc = time.time()
time_M2M = toc - tic
# Evaluation
IorE = 0 # This evaluation is on charge points, no self-operator
# 0 means it doesn't matter if it is internal or external.
AI_int = 0
phi_reac = numpy.zeros(len(xq))
time_P2P = 0.
time_M2P = 0.
for i in range(len(xq)):
CJ = 0
Kval = 0.
Vval = 0.
source = []
Kval, Vval, source, time_M2P = M2P_nonvec(Cells, CJ, xq[i], Kval, Vval,
ind_reac.index_large,
par_reac, source, time_M2P)
Kval, Vval, AI_int, time_P2P = P2P_nonvec(Cells, surface, X_V, X_Kx,
X_Ky, X_Kz, X_Kc, X_Vc,
xq[i], Kval, Vval, IorE,
par_reac, w, source, AI_int,
time_P2P)
phi_reac[i] = (-Kval + Vval) / (4 * pi)
return phi_reac, AI_int
def get_phir_gpu(XK, XV, surface, field, par_reac, kernel):
"""
It computes the reaction potential on the GPU and it brings the data
to the cpu.
Arguments
----------
XK : array, input for the double layer potential.
XV : array, input for the single layer potential.
surface : class, surface where we are computing the reaction potential.
field : class, information about the different regions in the molecule.
par_reac: class, fine parameters related to the surface.
Returns
--------
phir_cpu: array, reaction potential brought from the GPU to the cpu.
AI_int : int, counter of the amount of near singular integrals solved.
"""
REAL = par_reac.REAL
Nq = len(field.xq)
N = len(XK)
AI_int = 0
# Setup vector
K = par_reac.K
tic = time.time()
w = getWeights(K)
X_V = numpy.zeros(N * K)
X_Kx = numpy.zeros(N * K)
X_Ky = numpy.zeros(N * K)
X_Kz = numpy.zeros(N * K)
X_Kc = numpy.zeros(N * K)
X_Vc = numpy.zeros(N * K)
for i in range(N * K):
X_V[i] = XV[i // K] * w[i % K] * surface.area[i // K]
X_Kx[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 0]
X_Ky[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 1]
X_Kz[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 2]
X_Kc[i] = XK[i // K]
X_Vc[i] = XV[i // K]
toc = time.time()
sort = surface.sortSource
phir = cuda.to_device(numpy.zeros(Nq, dtype=REAL))
m_gpu = cuda.to_device(X_V[sort].astype(REAL))
mx_gpu = cuda.to_device(X_Kx[sort].astype(REAL))
my_gpu = cuda.to_device(X_Ky[sort].astype(REAL))
mz_gpu = cuda.to_device(X_Kz[sort].astype(REAL))
mKc_gpu = cuda.to_device(X_Kc[sort].astype(REAL))
mVc_gpu = cuda.to_device(X_Vc[sort].astype(REAL))
AI_int_gpu = cuda.to_device(numpy.zeros(Nq, dtype=numpy.int32))
xkDev = cuda.to_device(surface.xk.astype(REAL))
wkDev = cuda.to_device(surface.wk.astype(REAL))
get_phir = kernel.get_function("get_phir")
GSZ = int(numpy.ceil(float(Nq) / par_reac.BSZ))
get_phir(phir,
field.xq_gpu,
field.yq_gpu,
field.zq_gpu,
m_gpu,
mx_gpu,
my_gpu,
mz_gpu,
mKc_gpu,
mVc_gpu,
surface.xjDev,
surface.yjDev,
surface.zjDev,
surface.AreaDev,
surface.kDev,
surface.vertexDev,
numpy.int32(len(surface.xj)),
numpy.int32(Nq),
numpy.int32(par_reac.K),
xkDev,
wkDev,
REAL(par_reac.threshold),
AI_int_gpu,
numpy.int32(len(surface.xk)),
surface.XskDev,
surface.WskDev,
block=(par_reac.BSZ, 1, 1),
grid=(GSZ, 1))
AI_aux = numpy.zeros(Nq, dtype=numpy.int32)
AI_aux = cuda.from_device(AI_int_gpu, Nq, dtype=numpy.int32)
AI_int = numpy.sum(AI_aux)
phir_cpu = numpy.zeros(Nq, dtype=REAL)
phir_cpu = cuda.from_device(phir, Nq, dtype=REAL)
return phir_cpu, AI_int
def project_Kt(XKt, LorY, surfSrc, surfTar, Kt_diag, self, param, ind0, timing,
kernel):
"""
It computes the adjoint double layer potential.
Arguments
----------
XKt : array, input for the adjoint double layer potential.
LorY : int, Laplace (1) or Yukawa (2).
surfSrc: class, source surface, the one that contains the gauss points.
surfTar: class, target surface, the one that contains the collocation points.
Kt_diag: array, diagonal elements of the adjoint double layer integral
operator.
self : int, position in the surface array of the source surface.
param : class, parameters related to the surface.
ind0 : array, it contains the indices related to the treecode computation.
timing : class, it contains timing information for different parts of the
code.
kernel : pycuda source module.
Returns
--------
Kt_lyr: array, adjoint double layer potential.
"""
if param.GPU == 1:
tic = cuda.Event()
toc = cuda.Event()
else:
tic = Event()
toc = Event()
REAL = param.REAL
Ns = len(surfSrc.triangle)
tic.record()
K = param.K
w = getWeights(K)
X_Kt = numpy.zeros(Ns * K)
X_Ktc = numpy.zeros(Ns * K)
NsK = numpy.arange(Ns * K)
X_Kt[:] = XKt[NsK // K] * w[NsK % K] * surfSrc.area[NsK // K]
X_Ktc[:] = XKt[NsK // K]
toc.record()
toc.synchronize()
timing.time_mass += tic.time_till(toc) * 1e-3
tic.record()
C = 0
X_aux = numpy.zeros(Ns * K)
getMultipole(surfSrc.tree, C, surfSrc.xj, surfSrc.yj, surfSrc.zj, X_Kt,
X_aux, X_aux, X_aux, ind0, param.P, param.NCRIT)
toc.record()
toc.synchronize()
timing.time_P2M += tic.time_till(toc) * 1e-3
tic.record()
for C in reversed(range(1, len(surfSrc.tree))):
PC = surfSrc.tree[C].parent
upwardSweep(surfSrc.tree, C, PC, param.P, ind0.II, ind0.JJ, ind0.KK,
ind0.index, ind0.combII, ind0.combJJ, ind0.combKK,
ind0.IImii, ind0.JJmjj, ind0.KKmkk, ind0.index_small,
ind0.index_ptr)
toc.record()
toc.synchronize()
timing.time_M2M += tic.time_till(toc) * 1e-3
tic.record()
X_Kt = X_Kt[surfSrc.sortSource]
X_Ktc = X_Ktc[surfSrc.sortSource]
toc.record()
toc.synchronize()
timing.time_sort += tic.time_till(toc) * 1e-3
param.Nround = len(surfTar.twig) * param.NCRIT
Ktx_aux = numpy.zeros(param.Nround)
Kty_aux = numpy.zeros(param.Nround)
Ktz_aux = numpy.zeros(param.Nround)
### CPU code
if param.GPU == 0:
if surfTar.offsetMlt[self, len(surfTar.twig)] > 0:
Ktx_aux, Kty_aux, Ktz_aux = M2PKt_sort(surfSrc, surfTar, Ktx_aux,
Kty_aux, Ktz_aux, self,
ind0.index_large, param,
LorY, timing)
Ktx_aux, Kty_aux, Ktz_aux = P2PKt_sort(surfSrc, surfTar, X_Kt, X_Ktc,
Ktx_aux, Kty_aux, Ktz_aux, self,
LorY, w, param, timing)
### GPU code
elif param.GPU == 1:
Ktx_gpu = cuda.to_device(Ktx_aux.astype(REAL))
Kty_gpu = cuda.to_device(Kty_aux.astype(REAL))
Ktz_gpu = cuda.to_device(Ktz_aux.astype(REAL))
if surfTar.offsetMlt[self, len(surfTar.twig)] > 0:
Ktx_gpu, Kty_gpu, Ktz_gpu = M2PKt_gpu(surfSrc, surfTar, Ktx_gpu,
Kty_gpu, Ktz_gpu, self, ind0,
param, LorY, timing, kernel)
Ktx_gpu, Kty_gpu, Ktz_gpu = P2PKt_gpu(surfSrc, surfTar, X_Kt, X_Ktc,
Ktx_gpu, Kty_gpu, Ktz_gpu, self,
LorY, w, param, timing, kernel)
tic.record()
Ktx_aux = cuda.from_device(Ktx_gpu, len(Ktx_aux), dtype=REAL)
Kty_aux = cuda.from_device(Kty_gpu, len(Kty_aux), dtype=REAL)
Ktz_aux = cuda.from_device(Ktz_gpu, len(Ktz_aux), dtype=REAL)
toc.record()
toc.synchronize()
timing.time_trans += tic.time_till(toc) * 1e-3
tic.record()
Kt_lyr = (Ktx_aux[surfTar.unsort] * surfTar.normal[:, 0] +
Kty_aux[surfTar.unsort] * surfTar.normal[:, 1] +
Ktz_aux[surfTar.unsort] * surfTar.normal[:, 2])
if abs(Kt_diag) > 1e-12: # if same surface
Kt_lyr += Kt_diag * XKt
toc.record()
toc.synchronize()
timing.time_sort += tic.time_till(toc) * 1e-3
return Kt_lyr
def project(XK, XV, LorY, surfSrc, surfTar, K_diag, V_diag, IorE, self, param,
ind0, timing, kernel):
"""
It computes the single and double layer potentials.
Arguments
----------
XK : array, input for the double layer potential.
XV : array, input for the single layer potential.
LorY : int, Laplace (1) or Yukawa (2).
surfSrc: class, source surface, the one that contains the gauss points.
surfTar: class, target surface, the one that contains the collocation
points.
K_diag : array, diagonal elements of the double layer integral operator.
V_diag : array, diagonal elements of the single layer integral operator.
IorE : int, internal (1) or external (2).
self : int, position in the surface array of the source surface.
param : class, parameters related to the surface.
ind0 : array, it contains the indices related to the treecode computation.
timing : class, it contains timing information for different parts of the
code.
kernel : pycuda source module.
Returns
--------
K_lyr : array, double layer potential.
V_lyr : array, single layer potential.
"""
if param.GPU == 1:
tic = cuda.Event()
toc = cuda.Event()
else:
tic = Event()
toc = Event()
REAL = param.REAL
Ns = len(surfSrc.triangle)
L = numpy.sqrt(2 * surfSrc.area) # Representative length
tic.record()
K = param.K
w = getWeights(K)
X_V = numpy.zeros(Ns * K)
X_Kx = numpy.zeros(Ns * K)
X_Ky = numpy.zeros(Ns * K)
X_Kz = numpy.zeros(Ns * K)
X_Kc = numpy.zeros(Ns * K)
X_Vc = numpy.zeros(Ns * K)
NsK = numpy.arange(Ns * K)
X_V[:] = XV[NsK // K] * w[NsK % K] * surfSrc.area[NsK // K]
X_Kx[:] = XK[NsK // K] * w[NsK % K] * surfSrc.area[
NsK // K] * surfSrc.normal[NsK // K, 0]
X_Ky[:] = XK[NsK // K] * w[NsK % K] * surfSrc.area[
NsK // K] * surfSrc.normal[NsK // K, 1]
X_Kz[:] = XK[NsK // K] * w[NsK % K] * surfSrc.area[
NsK // K] * surfSrc.normal[NsK // K, 2]
X_Kc[:] = XK[NsK // K]
X_Vc[:] = XV[NsK // K]
toc.record()
toc.synchronize()
timing.time_mass += tic.time_till(toc) * 1e-3
tic.record()
C = 0
getMultipole(surfSrc.tree, C, surfSrc.xj, surfSrc.yj, surfSrc.zj, X_V,
X_Kx, X_Ky, X_Kz, ind0, param.P, param.NCRIT)
toc.record()
toc.synchronize()
timing.time_P2M += tic.time_till(toc) * 1e-3
tic.record()
for C in reversed(range(1, len(surfSrc.tree))):
PC = surfSrc.tree[C].parent
upwardSweep(surfSrc.tree, C, PC, param.P, ind0.II, ind0.JJ, ind0.KK,
ind0.index, ind0.combII, ind0.combJJ, ind0.combKK,
ind0.IImii, ind0.JJmjj, ind0.KKmkk, ind0.index_small,
ind0.index_ptr)
toc.record()
toc.synchronize()
timing.time_M2M += tic.time_till(toc) * 1e-3
tic.record()
X_V = X_V[surfSrc.sortSource]
X_Kx = X_Kx[surfSrc.sortSource]
X_Ky = X_Ky[surfSrc.sortSource]
X_Kz = X_Kz[surfSrc.sortSource]
X_Kc = X_Kc[surfSrc.sortSource]
X_Vc = X_Vc[surfSrc.sortSource]
toc.record()
toc.synchronize()
timing.time_sort += tic.time_till(toc) * 1e-3
param.Nround = len(surfTar.twig) * param.NCRIT
K_aux = numpy.zeros(param.Nround)
V_aux = numpy.zeros(param.Nround)
### CPU code
if param.GPU == 0:
if surfTar.offsetMlt[self, len(surfTar.twig)] > 0:
K_aux, V_aux = M2P_sort(surfSrc, surfTar, K_aux, V_aux, self,
ind0.index_large, param, LorY, timing)
K_aux, V_aux = P2P_sort(surfSrc, surfTar, X_V, X_Kx, X_Ky, X_Kz, X_Kc,
X_Vc, K_aux, V_aux, self, LorY, K_diag, V_diag,
IorE, L, w, param, timing)
### GPU code
elif param.GPU == 1:
K_gpu = cuda.to_device(K_aux.astype(REAL))
V_gpu = cuda.to_device(V_aux.astype(REAL))
if surfTar.offsetMlt[self, len(surfTar.twig)] > 0:
K_gpu, V_gpu = M2P_gpu(surfSrc, surfTar, K_gpu, V_gpu, self, ind0,
param, LorY, timing, kernel)
K_gpu, V_gpu = P2P_gpu(surfSrc, surfTar, X_V, X_Kx, X_Ky, X_Kz, X_Kc,
X_Vc, K_gpu, V_gpu, self, LorY, K_diag, IorE, L,
w, param, timing, kernel)
tic.record()
K_aux = cuda.from_device(K_gpu, len(K_aux), dtype=REAL)
V_aux = cuda.from_device(V_gpu, len(V_aux), dtype=REAL)
toc.record()
toc.synchronize()
timing.time_trans += tic.time_till(toc) * 1e-3
tic.record()
K_lyr = K_aux[surfTar.unsort]
V_lyr = V_aux[surfTar.unsort]
toc.record()
toc.synchronize()
timing.time_sort += tic.time_till(toc) * 1e-3
return K_lyr, V_lyr
def get_phir(XK, XV, surface, xq, Cells, par_reac, ind_reac):
"""
It computes the reaction potential.
To compute this potential we need more terms in the Taylor expansion, that
is the reason why we need fine parameters (par_reac class) and a different
array of indices (ind_reac) than ind0.
Arguments
----------
XK : array, input for the double layer potential.
XV : array, input for the single layer potential.
surface : class, surface where we are computing the reaction potential.
xq : array, it contains the position of the charges.
Cells : array, it contains the tree cells.
par_reac: class, fine parameters related to the surface.
ind_reac: array, it contains the indices related to the treecode
computation.
Returns
--------
phi_reac: array, reaction potential.
AI_int : int, counter of the amount of near singular integrals solved.
"""
N = len(XK)
AI_int = 0
# Setup vector
K = par_reac.K
tic = time.time()
w = getWeights(K)
X_V = numpy.zeros(N * K)
X_Kx = numpy.zeros(N * K)
X_Ky = numpy.zeros(N * K)
X_Kz = numpy.zeros(N * K)
X_Kc = numpy.zeros(N * K)
X_Vc = numpy.zeros(N * K)
for i in range(N * K):
X_V[i] = XV[i // K] * w[i % K] * surface.area[i // K]
X_Kx[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 0]
X_Ky[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 1]
X_Kz[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 2]
X_Kc[i] = XK[i // K]
X_Vc[i] = XV[i // K]
toc = time.time()
# P2M
tic = time.time()
C = 0
getMultipole(Cells, C, surface.xj, surface.yj, surface.zj, X_V, X_Kx, X_Ky,
X_Kz, ind_reac, par_reac.P, par_reac.NCRIT)
toc = time.time()
time_P2M = toc - tic
# M2M
tic = time.time()
for C in reversed(range(1, len(Cells))):
PC = Cells[C].parent
upwardSweep(Cells, C, PC, par_reac.P, ind_reac.II, ind_reac.JJ,
ind_reac.KK, ind_reac.index, ind_reac.combII,
ind_reac.combJJ, ind_reac.combKK, ind_reac.IImii,
ind_reac.JJmjj, ind_reac.KKmkk, ind_reac.index_small,
ind_reac.index_ptr)
toc = time.time()
time_M2M = toc - tic
# Evaluation
IorE = 0 # This evaluation is on charge points, no self-operator
# 0 means it doesn't matter if it is internal or external.
AI_int = 0
phi_reac = numpy.zeros(len(xq))
time_P2P = 0.
time_M2P = 0.
for i in range(len(xq)):
CJ = 0
Kval = 0.
Vval = 0.
source = []
Kval, Vval, source, time_M2P = M2P_nonvec(Cells, CJ, xq[i], Kval, Vval,
ind_reac.index_large,
par_reac, source, time_M2P)
Kval, Vval, AI_int, time_P2P = P2P_nonvec(
Cells, surface, X_V, X_Kx, X_Ky, X_Kz, X_Kc, X_Vc, xq[i], Kval,
Vval, IorE, par_reac, w, source, AI_int, time_P2P)
phi_reac[i] = (-Kval + Vval) / (4 * pi)
return phi_reac, AI_int
def get_phir_gpu(XK, XV, surface, field, par_reac, kernel):
"""
It computes the reaction potential on the GPU and it brings the data
to the cpu.
Arguments
----------
XK : array, input for the double layer potential.
XV : array, input for the single layer potential.
surface : class, surface where we are computing the reaction potential.
field : class, information about the different regions in the molecule.
par_reac: class, fine parameters related to the surface.
Returns
--------
phir_cpu: array, reaction potential brought from the GPU to the cpu.
AI_int : int, counter of the amount of near singular integrals solved.
"""
REAL = par_reac.REAL
Nq = len(field.xq)
N = len(XK)
AI_int = 0
# Setup vector
K = par_reac.K
tic = time.time()
w = getWeights(K)
X_V = numpy.zeros(N * K)
X_Kx = numpy.zeros(N * K)
X_Ky = numpy.zeros(N * K)
X_Kz = numpy.zeros(N * K)
X_Kc = numpy.zeros(N * K)
X_Vc = numpy.zeros(N * K)
for i in range(N * K):
X_V[i] = XV[i // K] * w[i % K] * surface.area[i // K]
X_Kx[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 0]
X_Ky[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 1]
X_Kz[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 2]
X_Kc[i] = XK[i // K]
X_Vc[i] = XV[i // K]
toc = time.time()
sort = surface.sortSource
phir = cuda.to_device(numpy.zeros(Nq, dtype=REAL))
m_gpu = cuda.to_device(X_V[sort].astype(REAL))
mx_gpu = cuda.to_device(X_Kx[sort].astype(REAL))
my_gpu = cuda.to_device(X_Ky[sort].astype(REAL))
mz_gpu = cuda.to_device(X_Kz[sort].astype(REAL))
mKc_gpu = cuda.to_device(X_Kc[sort].astype(REAL))
mVc_gpu = cuda.to_device(X_Vc[sort].astype(REAL))
AI_int_gpu = cuda.to_device(numpy.zeros(Nq, dtype=numpy.int32))
xkDev = cuda.to_device(surface.xk.astype(REAL))
wkDev = cuda.to_device(surface.wk.astype(REAL))
get_phir = kernel.get_function("get_phir")
GSZ = int(numpy.ceil(float(Nq) / par_reac.BSZ))
get_phir(phir,
field.xq_gpu,
field.yq_gpu,
field.zq_gpu,
m_gpu,
mx_gpu,
my_gpu,
mz_gpu,
mKc_gpu,
mVc_gpu,
surface.xjDev,
surface.yjDev,
surface.zjDev,
surface.AreaDev,
surface.kDev,
surface.vertexDev,
numpy.int32(len(surface.xj)),
numpy.int32(Nq),
numpy.int32(par_reac.K),
xkDev,
wkDev,
REAL(par_reac.threshold),
AI_int_gpu,
numpy.int32(len(surface.xk)),
surface.XskDev,
surface.WskDev,
block=(par_reac.BSZ, 1, 1),
grid=(GSZ, 1))
AI_aux = numpy.zeros(Nq, dtype=numpy.int32)
AI_aux = cuda.from_device(AI_int_gpu, Nq, dtype=numpy.int32)
AI_int = numpy.sum(AI_aux)
phir_cpu = numpy.zeros(Nq, dtype=REAL)
phir_cpu = cuda.from_device(phir, Nq, dtype=REAL)
return phir_cpu, AI_int
def project(XK, XV, LorY, surfSrc, surfTar, K_diag, V_diag, IorE, self, param,
ind0, timing, kernel):
"""
It computes the single and double layer potentials.
Arguments
----------
XK : array, input for the double layer potential.
XV : array, input for the single layer potential.
LorY : int, Laplace (1) or Yukawa (2).
surfSrc: class, source surface, the one that contains the gauss points.
surfTar: class, target surface, the one that contains the collocation
points.
K_diag : array, diagonal elements of the double layer integral operator.
V_diag : array, diagonal elements of the single layer integral operator.
IorE : int, internal (1) or external (2).
self : int, position in the surface array of the source surface.
param : class, parameters related to the surface.
ind0 : array, it contains the indices related to the treecode computation.
timing : class, it contains timing information for different parts of the
code.
kernel : pycuda source module.
Returns
--------
K_lyr : array, double layer potential.
V_lyr : array, single layer potential.
"""
if param.GPU == 1:
tic = cuda.Event()
toc = cuda.Event()
else:
tic = Event()
toc = Event()
REAL = param.REAL
Ns = len(surfSrc.triangle)
L = numpy.sqrt(2 * surfSrc.area) # Representative length
tic.record()
K = param.K
w = getWeights(K)
X_V = numpy.zeros(Ns * K)
X_Kx = numpy.zeros(Ns * K)
X_Ky = numpy.zeros(Ns * K)
X_Kz = numpy.zeros(Ns * K)
X_Kc = numpy.zeros(Ns * K)
X_Vc = numpy.zeros(Ns * K)
NsK = numpy.arange(Ns * K)
X_V[:] = XV[NsK // K] * w[NsK % K] * surfSrc.area[NsK // K]
X_Kx[:] = XK[NsK // K] * w[NsK % K] * surfSrc.area[
NsK // K] * surfSrc.normal[NsK // K, 0]
X_Ky[:] = XK[NsK // K] * w[NsK % K] * surfSrc.area[
NsK // K] * surfSrc.normal[NsK // K, 1]
X_Kz[:] = XK[NsK // K] * w[NsK % K] * surfSrc.area[
NsK // K] * surfSrc.normal[NsK // K, 2]
X_Kc[:] = XK[NsK // K]
X_Vc[:] = XV[NsK // K]
toc.record()
toc.synchronize()
timing.time_mass += tic.time_till(toc) * 1e-3
tic.record()
C = 0
getMultipole(surfSrc.tree, C, surfSrc.xj, surfSrc.yj, surfSrc.zj, X_V,
X_Kx, X_Ky, X_Kz, ind0, param.P, param.NCRIT)
toc.record()
toc.synchronize()
timing.time_P2M += tic.time_till(toc) * 1e-3
tic.record()
for C in reversed(range(1, len(surfSrc.tree))):
PC = surfSrc.tree[C].parent
upwardSweep(surfSrc.tree, C, PC, param.P, ind0.II, ind0.JJ, ind0.KK,
ind0.index, ind0.combII, ind0.combJJ, ind0.combKK,
ind0.IImii, ind0.JJmjj, ind0.KKmkk, ind0.index_small,
ind0.index_ptr)
toc.record()
toc.synchronize()
timing.time_M2M += tic.time_till(toc) * 1e-3
tic.record()
X_V = X_V[surfSrc.sortSource]
X_Kx = X_Kx[surfSrc.sortSource]
X_Ky = X_Ky[surfSrc.sortSource]
X_Kz = X_Kz[surfSrc.sortSource]
X_Kc = X_Kc[surfSrc.sortSource]
X_Vc = X_Vc[surfSrc.sortSource]
toc.record()
toc.synchronize()
timing.time_sort += tic.time_till(toc) * 1e-3
param.Nround = len(surfTar.twig) * param.NCRIT
K_aux = numpy.zeros(param.Nround)
V_aux = numpy.zeros(param.Nround)
### CPU code
if param.GPU == 0:
K_aux, V_aux = M2P_sort(surfSrc, surfTar, K_aux, V_aux, self,
ind0.index_large, param, LorY, timing)
K_aux, V_aux = P2P_sort(surfSrc, surfTar, X_V, X_Kx, X_Ky, X_Kz, X_Kc,
X_Vc, K_aux, V_aux, self, LorY, K_diag, V_diag,
IorE, L, w, param, timing)
### GPU code
elif param.GPU == 1:
K_gpu = cuda.to_device(K_aux.astype(REAL))
V_gpu = cuda.to_device(V_aux.astype(REAL))
if surfTar.offsetMlt[self, len(surfTar.twig)] > 0:
K_gpu, V_gpu = M2P_gpu(surfSrc, surfTar, K_gpu, V_gpu, self, ind0,
param, LorY, timing, kernel)
K_gpu, V_gpu = P2P_gpu(surfSrc, surfTar, X_V, X_Kx, X_Ky, X_Kz, X_Kc,
X_Vc, K_gpu, V_gpu, self, LorY, K_diag, IorE, L,
w, param, timing, kernel)
tic.record()
K_aux = cuda.from_device(K_gpu, len(K_aux), dtype=REAL)
V_aux = cuda.from_device(V_gpu, len(V_aux), dtype=REAL)
toc.record()
toc.synchronize()
timing.time_trans += tic.time_till(toc) * 1e-3
tic.record()
K_lyr = K_aux[surfTar.unsort]
V_lyr = V_aux[surfTar.unsort]
toc.record()
toc.synchronize()
timing.time_sort += tic.time_till(toc) * 1e-3
return K_lyr, V_lyr
def project_Kt(XKt, LorY, surfSrc, surfTar, Kt_diag, self, param, ind0, timing,
kernel):
"""
It computes the adjoint double layer potential.
Arguments
----------
XKt : array, input for the adjoint double layer potential.
LorY : int, Laplace (1) or Yukawa (2).
surfSrc: class, source surface, the one that contains the gauss points.
surfTar: class, target surface, the one that contains the collocation points.
Kt_diag: array, diagonal elements of the adjoint double layer integral
operator.
self : int, position in the surface array of the source surface.
param : class, parameters related to the surface.
ind0 : array, it contains the indices related to the treecode computation.
timing : class, it contains timing information for different parts of the
code.
kernel : pycuda source module.
Returns
--------
Kt_lyr: array, adjoint double layer potential.
"""
if param.GPU == 1:
tic = cuda.Event()
toc = cuda.Event()
else:
tic = Event()
toc = Event()
REAL = param.REAL
Ns = len(surfSrc.triangle)
tic.record()
K = param.K
w = getWeights(K)
X_Kt = numpy.zeros(Ns * K)
X_Ktc = numpy.zeros(Ns * K)
NsK = numpy.arange(Ns * K)
X_Kt[:] = XKt[NsK // K] * w[NsK % K] * surfSrc.area[NsK // K]
X_Ktc[:] = XKt[NsK // K]
toc.record()
toc.synchronize()
timing.time_mass += tic.time_till(toc) * 1e-3
tic.record()
C = 0
X_aux = numpy.zeros(Ns * K)
getMultipole(surfSrc.tree, C, surfSrc.xj, surfSrc.yj, surfSrc.zj, X_Kt,
X_aux, X_aux, X_aux, ind0, param.P, param.NCRIT)
toc.record()
toc.synchronize()
timing.time_P2M += tic.time_till(toc) * 1e-3
tic.record()
for C in reversed(range(1, len(surfSrc.tree))):
PC = surfSrc.tree[C].parent
upwardSweep(surfSrc.tree, C, PC, param.P, ind0.II, ind0.JJ, ind0.KK,
ind0.index, ind0.combII, ind0.combJJ, ind0.combKK,
ind0.IImii, ind0.JJmjj, ind0.KKmkk, ind0.index_small,
ind0.index_ptr)
toc.record()
toc.synchronize()
timing.time_M2M += tic.time_till(toc) * 1e-3
tic.record()
X_Kt = X_Kt[surfSrc.sortSource]
X_Ktc = X_Ktc[surfSrc.sortSource]
toc.record()
toc.synchronize()
timing.time_sort += tic.time_till(toc) * 1e-3
param.Nround = len(surfTar.twig) * param.NCRIT
Ktx_aux = numpy.zeros(param.Nround)
Kty_aux = numpy.zeros(param.Nround)
Ktz_aux = numpy.zeros(param.Nround)
### CPU code
if param.GPU == 0:
if surfTar.offsetMlt[self, len(surfTar.twig)] > 0:
Ktx_aux, Kty_aux, Ktz_aux = M2PKt_sort(
surfSrc, surfTar, Ktx_aux, Kty_aux, Ktz_aux, self,
ind0.index_large, param, LorY, timing)
Ktx_aux, Kty_aux, Ktz_aux = P2PKt_sort(surfSrc, surfTar, X_Kt, X_Ktc,
Ktx_aux, Kty_aux, Ktz_aux, self,
LorY, w, param, timing)
### GPU code
elif param.GPU == 1:
Ktx_gpu = cuda.to_device(Ktx_aux.astype(REAL))
Kty_gpu = cuda.to_device(Kty_aux.astype(REAL))
Ktz_gpu = cuda.to_device(Ktz_aux.astype(REAL))
if surfTar.offsetMlt[self, len(surfTar.twig)] > 0:
Ktx_gpu, Kty_gpu, Ktz_gpu = M2PKt_gpu(surfSrc, surfTar, Ktx_gpu,
Kty_gpu, Ktz_gpu, self, ind0,
param, LorY, timing, kernel)
Ktx_gpu, Kty_gpu, Ktz_gpu = P2PKt_gpu(surfSrc, surfTar, X_Kt, X_Ktc,
Ktx_gpu, Kty_gpu, Ktz_gpu, self,
LorY, w, param, timing, kernel)
tic.record()
Ktx_aux = cuda.from_device(Ktx_gpu, len(Ktx_aux), dtype=REAL)
Kty_aux = cuda.from_device(Kty_gpu, len(Kty_aux), dtype=REAL)
Ktz_aux = cuda.from_device(Ktz_gpu, len(Ktz_aux), dtype=REAL)
toc.record()
toc.synchronize()
timing.time_trans += tic.time_till(toc) * 1e-3
tic.record()
Kt_lyr = (Ktx_aux[surfTar.unsort]*surfTar.normal[:,0] +
Kty_aux[surfTar.unsort]*surfTar.normal[:,1] +
Ktz_aux[surfTar.unsort]*surfTar.normal[:,2])
if abs(Kt_diag) > 1e-12: # if same surface
Kt_lyr += Kt_diag * XKt
toc.record()
toc.synchronize()
timing.time_sort += tic.time_till(toc) * 1e-3
return Kt_lyr
def get_dphirdr_gpu(XK, XV, surface, field, par_reac, kernel):
REAL = par_reac.REAL
Nq = len(field.xq)
N = len(XK)
MV = numpy.zeros(len(XK))
L = numpy.sqrt(2 * surface.area) # Representative length
AI_int = 0
# Setup vector
K = par_reac.K
tic = time.time()
w = getWeights(K)
X_V = numpy.zeros(N * K)
X_Kx = numpy.zeros(N * K)
X_Ky = numpy.zeros(N * K)
X_Kz = numpy.zeros(N * K)
X_Kc = numpy.zeros(N * K)
X_Vc = numpy.zeros(N * K)
for i in range(N * K):
X_V[i] = XV[i // K] * w[i % K] * surface.area[i // K]
X_Kx[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 0]
X_Ky[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 1]
X_Kz[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 2]
X_Kc[i] = XK[i // K]
X_Vc[i] = XV[i // K]
toc = time.time()
time_set = toc - tic
sort = surface.sortSource
dphir_x = cuda.to_device(numpy.zeros(Nq, dtype=REAL))
dphir_y = cuda.to_device(numpy.zeros(Nq, dtype=REAL))
dphir_z = cuda.to_device(numpy.zeros(Nq, dtype=REAL))
m_gpu = cuda.to_device(X_V[sort].astype(REAL))
mx_gpu = cuda.to_device(X_Kx[sort].astype(REAL))
my_gpu = cuda.to_device(X_Ky[sort].astype(REAL))
mz_gpu = cuda.to_device(X_Kz[sort].astype(REAL))
mKc_gpu = cuda.to_device(X_Kc[sort].astype(REAL))
mVc_gpu = cuda.to_device(X_Vc[sort].astype(REAL))
AI_int_gpu = cuda.to_device(numpy.zeros(Nq, dtype=numpy.int32))
xkDev = cuda.to_device(surface.xk.astype(REAL))
wkDev = cuda.to_device(surface.wk.astype(REAL))
get_dphirdr = kernel.get_function("get_dphirdr")
GSZ = int(numpy.ceil(float(Nq) / par_reac.BSZ))
get_dphirdr(dphir_x,
dphir_y,
dphir_z,
field.xq_gpu,
field.yq_gpu,
field.zq_gpu,
m_gpu,
mx_gpu,
my_gpu,
mz_gpu,
mKc_gpu,
mVc_gpu,
surface.xjDev,
surface.yjDev,
surface.zjDev,
surface.AreaDev,
surface.kDev,
surface.vertexDev,
numpy.int32(len(surface.xj)),
numpy.int32(Nq),
numpy.int32(par_reac.K),
xkDev,
wkDev,
REAL(par_reac.threshold),
AI_int_gpu,
numpy.int32(len(surface.xk)),
surface.XskDev,
surface.WskDev,
block=(par_reac.BSZ, 1, 1),
grid=(GSZ, 1))
AI_aux = numpy.zeros(Nq, dtype=numpy.int32)
AI_aux = cuda.from_device(AI_int_gpu, Nq, dtype=numpy.int32)
AI_int = numpy.sum(AI_aux)
dphir_x_cpu = numpy.zeros(Nq, dtype=REAL)
dphir_x_cpu = cuda.from_device(dphir_x, Nq, dtype=REAL)
dphir_y_cpu = numpy.zeros(Nq, dtype=REAL)
dphir_y_cpu = cuda.from_device(dphir_y, Nq, dtype=REAL)
dphir_z_cpu = numpy.zeros(Nq, dtype=REAL)
dphir_z_cpu = cuda.from_device(dphir_z, Nq, dtype=REAL)
return dphir_x_cpu, dphir_y_cpu, dphir_z_cpu, AI_int
def get_dphirdr(XK, XV, surface, xq, Cells, par_reac, ind_reac):
REAL = par_reac.REAL
N = len(XK)
MV = numpy.zeros(len(XK))
L = numpy.sqrt(2 * surface.area) # Representative length
AI_int = 0
# Setup vector
K = par_reac.K
tic = time.time()
w = getWeights(K)
X_V = numpy.zeros(N * K)
X_Kx = numpy.zeros(N * K)
X_Ky = numpy.zeros(N * K)
X_Kz = numpy.zeros(N * K)
X_Kc = numpy.zeros(N * K)
X_Vc = numpy.zeros(N * K)
for i in range(N * K):
X_V[i] = XV[i // K] * w[i % K] * surface.area[i // K]
X_Kx[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 0]
X_Ky[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 1]
X_Kz[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 2]
X_Kc[i] = XK[i // K]
X_Vc[i] = XV[i // K]
toc = time.time()
time_set = toc - tic
# Evaluation
IorE = 0 # This evaluation is on charge points, no self-operator
AI_int = 0
dphix_reac = numpy.zeros(len(xq))
dphiy_reac = numpy.zeros(len(xq))
dphiz_reac = numpy.zeros(len(xq))
time_P2P = 0.
time_M2P = 0.
for i in range(len(xq)):
CJ = 0
dKxval = 0.
dKyval = 0.
dKzval = 0.
dVxval = 0.
dVyval = 0.
dVzval = 0.
source = []
dKxval, dVxval, source, time_M2P = M2P_nonvec(Cells, CJ, xq[i], dKxval,
dVxval,
ind_reac.index_large,
par_reac, source,
time_M2P)
dKxval, dKyval, dKzval, dVxval, \
dVyval, dVzval, AI_int, time_P2P = P2P_nonvec_derivative(Cells, surface, X_V, X_Kx, X_Ky, X_Kz, X_Kc, X_Vc,
xq[i], dKxval, dKyval, dKzval, dVxval, dVyval, dVzval, IorE, par_reac, w, source, AI_int, time_P2P)
dphix_reac[i] = (-dKxval + dVxval) / (4 * pi)
dphiy_reac[i] = (-dKyval + dVyval) / (4 * pi)
dphiz_reac[i] = (-dKzval + dVzval) / (4 * pi)
# print '\tTime set: %f'%time_P2M
# print '\tTime P2M: %f'%time_P2M
# print '\tTime M2M: %f'%time_M2M
# print '\tTime M2P: %f'%time_M2P
# print '\tTime P2P: %f'%time_P2P
return dphix_reac, dphiy_reac, dphiz_reac, AI_int
def get_phir(XK, XV, surface, xq, Cells, par_reac, ind_reac):
"""
It computes the reaction potential using direct interacion.
To compute this potential we need more terms in the Taylor expansion, that
is the reason why we need fine parameters (par_reac class) and a different
array of indices (ind_reac) than ind0.
Arguments
----------
XK : array, input for the double layer potential.
XV : array, input for the single layer potential.
surface : class, surface where we are computing the reaction potential.
xq : array, it contains the position of the charges.
Cells : array, it contains the tree cells.
par_reac: class, fine parameters related to the surface.
ind_reac: array, it contains the indices related to the treecode
computation.
Returns
--------
phi_reac: array, reaction potential.
AI_int : int, counter of the amount of near singular integrals solved.
"""
N = len(XK)
AI_int = 0
# Setup vector
K = par_reac.K
tic = time.time()
w = getWeights(K)
X_V = numpy.zeros(N * K)
X_Kx = numpy.zeros(N * K)
X_Ky = numpy.zeros(N * K)
X_Kz = numpy.zeros(N * K)
X_Kc = numpy.zeros(N * K)
X_Vc = numpy.zeros(N * K)
for i in range(N * K):
X_V[i] = XV[i // K] * w[i % K] * surface.area[i // K]
X_Kx[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 0]
X_Ky[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 1]
X_Kz[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 2]
X_Kc[i] = XK[i // K]
X_Vc[i] = XV[i // K]
# Evaluation
IorE = 0 # This evaluation is on charge points, no self-operator
# 0 means it doesn't matter if it is internal or external.
AI_int = 0
phi_reac = numpy.zeros(len(xq))
source = list(range(len(surface.xj)))
source = numpy.int32(numpy.array(source))
m, mx, my, mz, mKc, mVc = X_V, X_Kx, X_Ky, X_Kz, X_Kc, X_Vc
LorY = 1
s_xj = surface.xj[source]
s_yj = surface.yj[source]
s_zj = surface.zj[source]
s_m = m[source]
s_mx = mx[source]
s_my = my[source]
s_mz = mz[source]
s_mKc = mKc[source]
s_mVc = mVc[source]
tri = source / par_reac.K # Triangle
k = source % par_reac.K # Gauss point
K_diag = 0
V_diag = 0
xq_arr = numpy.ravel(numpy.array([xq[:, 0]]))
yq_arr = numpy.ravel(numpy.array([xq[:, 1]]))
zq_arr = numpy.ravel(numpy.array([xq[:, 2]]))
direct_c(
int(LorY), K_diag, V_diag, int(IorE),
numpy.ravel(surface.vertex[surface.triangle[:]]), numpy.int32(tri),
numpy.int32(k), surface.xi, surface.yi, surface.zi, s_xj, s_yj, s_zj,
xq_arr, yq_arr, zq_arr, s_m, s_mx, s_my, s_mz, s_mKc, s_mVc,
numpy.array([-1], dtype=numpy.int32), surface.area, surface.sglInt_int,
surface.sglInt_ext, surface.xk, surface.wk, surface.Xsk, surface.Wsk,
par_reac.kappa, par_reac.threshold, par_reac.eps, w[0], AI_int,
numpy.ravel(phi_reac))
return phi_reac, AI_int