def kern_CUDA_dense(nsteps, dX, rho_inv, int_m, dec_m,
phi, grid_idcs, prog_bar=None):
"""`NVIDIA CUDA cuBLAS <https://developer.nvidia.com/cublas>`_ implementation
of forward-euler integration.
Function requires a working :mod:`numbapro` installation. It is typically slower
compared to :func:`kern_MKL_sparse` but it depends on your hardware.
Args:
nsteps (int): number of integration steps
dX (numpy.array[nsteps]): vector of step-sizes :math:`\\Delta X_i` in g/cm**2
rho_inv (numpy.array[nsteps]): vector of density values :math:`\\frac{1}{\\rho(X_i)}`
int_m (numpy.array): interaction matrix :eq:`int_matrix` in dense or sparse representation
dec_m (numpy.array): decay matrix :eq:`dec_matrix` in dense or sparse representation
phi (numpy.array): initial state vector :math:`\\Phi(X_0)`
prog_bar (object,optional): handle to :class:`ProgressBar` object
Returns:
numpy.array: state vector :math:`\\Phi(X_{nsteps})` after integration
"""
calc_precision = None
if config['CUDA_precision'] == 32:
calc_precision = np.float32
elif config['CUDA_precision'] == 64:
calc_precision = np.float64
else:
raise Exception("kern_CUDA_dense(): Unknown precision specified.")
#=======================================================================
# Setup GPU stuff and upload data to it
#=======================================================================
try:
from numbapro.cudalib.cublas import Blas # @UnresolvedImport
from numbapro import cuda, float32 # @UnresolvedImport
except ImportError:
raise Exception("kern_CUDA_dense(): Numbapro CUDA libaries not " +
"installed.\nCan not use GPU.")
cubl = Blas()
m, n = int_m.shape
stream = cuda.stream()
cu_int_m = cuda.to_device(int_m.astype(calc_precision), stream)
cu_dec_m = cuda.to_device(dec_m.astype(calc_precision), stream)
cu_curr_phi = cuda.to_device(phi.astype(calc_precision), stream)
cu_delta_phi = cuda.device_array(phi.shape, dtype=calc_precision)
for step in xrange(nsteps):
if prog_bar:
prog_bar.update(step)
cubl.gemv(trans='T', m=m, n=n, alpha=float32(1.0), A=cu_int_m,
x=cu_curr_phi, beta=float32(0.0), y=cu_delta_phi)
cubl.gemv(trans='T', m=m, n=n, alpha=float32(rho_inv[step]),
A=cu_dec_m, x=cu_curr_phi, beta=float32(1.0), y=cu_delta_phi)
cubl.axpy(alpha=float32(dX[step]), x=cu_delta_phi, y=cu_curr_phi)
return cu_curr_phi.copy_to_host()
def kern_CUDA_sparse(nsteps, dX, rho_inv, int_m, dec_m,
phi, grid_idcs, prog_bar=None):
"""`NVIDIA CUDA cuSPARSE <https://developer.nvidia.com/cusparse>`_ implementation
of forward-euler integration.
Function requires a working :mod:`numbapro` installation.
Note:
Currently some bug in :mod:`numbapro` introduces unnecessary array copies and
slows down the execution tremendously.
Args:
nsteps (int): number of integration steps
dX (numpy.array[nsteps]): vector of step-sizes :math:`\\Delta X_i` in g/cm**2
rho_inv (numpy.array[nsteps]): vector of density values :math:`\\frac{1}{\\rho(X_i)}`
int_m (numpy.array): interaction matrix :eq:`int_matrix` in dense or sparse representation
dec_m (numpy.array): decay matrix :eq:`dec_matrix` in dense or sparse representation
phi (numpy.array): initial state vector :math:`\\Phi(X_0)`
prog_bar (object,optional): handle to :class:`ProgressBar` object
Returns:
numpy.array: state vector :math:`\\Phi(X_{nsteps})` after integration
"""
calc_precision = None
if config['CUDA_precision'] == 32:
calc_precision = np.float32
elif config['CUDA_precision'] == 64:
calc_precision = np.float64
else:
raise Exception("kern_CUDA_sparse(): Unknown precision specified.")
print ("kern_CUDA_sparse(): Warning, the performance is slower than " +
"dense cuBLAS or any type of MKL.")
#=======================================================================
# Setup GPU stuff and upload data to it
#=======================================================================
try:
from numbapro.cudalib import cusparse # @UnresolvedImport
from numbapro.cudalib.cublas import Blas
from numbapro import cuda, float32 # @UnresolvedImport
except ImportError:
raise Exception("kern_CUDA_sparse(): Numbapro CUDA libaries not " +
"installed.\nCan not use GPU.")
cusp = cusparse.Sparse()
cubl = Blas()
m, n = int_m.shape
int_m_nnz = int_m.nnz
int_m_csrValA = cuda.to_device(int_m.data.astype(calc_precision))
int_m_csrRowPtrA = cuda.to_device(int_m.indptr)
int_m_csrColIndA = cuda.to_device(int_m.indices)
dec_m_nnz = dec_m.nnz
dec_m_csrValA = cuda.to_device(dec_m.data.astype(calc_precision))
dec_m_csrRowPtrA = cuda.to_device(dec_m.indptr)
dec_m_csrColIndA = cuda.to_device(dec_m.indices)
cu_curr_phi = cuda.to_device(phi.astype(calc_precision))
cu_delta_phi = cuda.device_array(phi.shape, dtype=calc_precision)
descr = cusp.matdescr()
descr.indexbase = cusparse.CUSPARSE_INDEX_BASE_ZERO
for step in xrange(nsteps):
if prog_bar and (step % 5 == 0):
prog_bar.update(step)
cusp.csrmv(trans='T', m=m, n=n, nnz=int_m_nnz,
descr=descr,
alpha=float32(1.0),
csrVal=int_m_csrValA,
csrRowPtr=int_m_csrRowPtrA,
csrColInd=int_m_csrColIndA,
x=cu_curr_phi, beta=float32(0.0), y=cu_delta_phi)
cusp.csrmv(trans='T', m=m, n=n, nnz=dec_m_nnz,
descr=descr,
alpha=float32(rho_inv[step]),
csrVal=dec_m_csrValA,
csrRowPtr=dec_m_csrRowPtrA,
csrColInd=dec_m_csrColIndA,
x=cu_curr_phi, beta=float32(1.0), y=cu_delta_phi)
cubl.axpy(alpha=float32(dX[step]), x=cu_delta_phi, y=cu_curr_phi)
return cu_curr_phi.copy_to_host()
