def _cusp_solver(M, parameters):
cache_key = lambda t, p: (t, p['ksp_type'], p['pc_type'], p['ksp_rtol'], p[
'ksp_atol'], p['ksp_max_it'], p['ksp_gmres_restart'], p['ksp_monitor'])
module = _cusp_cache.get(cache_key(M.ctype, parameters))
if module:
return module
import codepy.toolchain
from cgen import FunctionBody, FunctionDeclaration
from cgen import Block, Statement, Include, Value
from codepy.bpl import BoostPythonModule
from codepy.cuda import CudaModule
gcc_toolchain = codepy.toolchain.guess_toolchain()
nvcc_toolchain = codepy.toolchain.guess_nvcc_toolchain()
if 'CUSP_HOME' in os.environ:
nvcc_toolchain.add_library('cusp', [os.environ['CUSP_HOME']], [], [])
host_mod = BoostPythonModule()
nvcc_mod = CudaModule(host_mod)
nvcc_includes = [
'thrust/device_vector.h', 'thrust/fill.h', 'cusp/csr_matrix.h',
'cusp/krylov/cg.h', 'cusp/krylov/bicgstab.h', 'cusp/krylov/gmres.h',
'cusp/precond/diagonal.h', 'cusp/precond/smoothed_aggregation.h',
'cusp/precond/ainv.h', 'string'
]
nvcc_mod.add_to_preamble([Include(s) for s in nvcc_includes])
nvcc_mod.add_to_preamble([Statement('using namespace std')])
# We're translating PETSc preconditioner types to CUSP
diag = Statement(
'cusp::precond::diagonal< ValueType, cusp::device_memory >M(A)')
ainv = Statement(
'cusp::precond::scaled_bridson_ainv< ValueType, cusp::device_memory >M(A)'
)
amg = Statement(
'cusp::precond::smoothed_aggregation< IndexType, ValueType, cusp::device_memory >M(A)'
)
none = Statement(
'cusp::identity_operator< ValueType, cusp::device_memory >M(nrows, ncols)'
)
preconditioners = {
'diagonal': diag,
'jacobi': diag,
'ainv': ainv,
'ainvcusp': ainv,
'amg': amg,
'hypre': amg,
'none': none,
None: none
}
try:
precond_call = preconditioners[parameters['pc_type']]
except KeyError:
raise RuntimeError("Cusp does not support preconditioner type %s" %
parameters['pc_type'])
solvers = {
'cg':
Statement('cusp::krylov::cg(A, x, b, monitor, M)'),
'bicgstab':
Statement('cusp::krylov::bicgstab(A, x, b, monitor, M)'),
'gmres':
Statement(
'cusp::krylov::gmres(A, x, b, %(ksp_gmres_restart)d, monitor, M)' %
parameters)
}
try:
solve_call = solvers[parameters['ksp_type']]
except KeyError:
raise RuntimeError("Cusp does not support solver type %s" %
parameters['ksp_type'])
monitor = 'monitor(b, %(ksp_max_it)d, %(ksp_rtol)g, %(ksp_atol)g)' % parameters
nvcc_function = FunctionBody(
FunctionDeclaration(Value('void', '__cusp_solve'), [
Value('CUdeviceptr', '_rowptr'),
Value('CUdeviceptr', '_colidx'),
Value('CUdeviceptr', '_csrdata'),
Value('CUdeviceptr', '_b'),
Value('CUdeviceptr', '_x'),
Value('int', 'nrows'),
Value('int', 'ncols'),
Value('int', 'nnz')
]),
Block([
Statement('typedef int IndexType'),
Statement('typedef %s ValueType' % M.ctype),
Statement(
'typedef typename cusp::array1d_view< thrust::device_ptr<IndexType> > indices'
),
Statement(
'typedef typename cusp::array1d_view< thrust::device_ptr<ValueType> > values'
),
Statement(
'typedef cusp::csr_matrix_view< indices, indices, values, IndexType, ValueType, cusp::device_memory > matrix'
),
Statement(
'thrust::device_ptr< IndexType > rowptr((IndexType *)_rowptr)'
),
Statement(
'thrust::device_ptr< IndexType > colidx((IndexType *)_colidx)'
),
Statement(
'thrust::device_ptr< ValueType > csrdata((ValueType *)_csrdata)'
),
Statement('thrust::device_ptr< ValueType > d_b((ValueType *)_b)'),
Statement('thrust::device_ptr< ValueType > d_x((ValueType *)_x)'),
Statement('indices row_offsets(rowptr, rowptr + nrows + 1)'),
Statement('indices column_indices(colidx, colidx + nnz)'),
Statement('values matrix_values(csrdata, csrdata + nnz)'),
Statement('values b(d_b, d_b + nrows)'),
Statement('values x(d_x, d_x + ncols)'),
Statement('thrust::fill(x.begin(), x.end(), (ValueType)0)'),
Statement(
'matrix A(nrows, ncols, nnz, row_offsets, column_indices, matrix_values)'
),
Statement('cusp::%s_monitor< ValueType > %s' %
('verbose' if parameters['ksp_monitor'] else 'default',
monitor)), precond_call, solve_call
]))
host_mod.add_to_preamble(
[Include('boost/python/extract.hpp'),
Include('string')])
host_mod.add_to_preamble([Statement('using namespace boost::python')])
host_mod.add_to_preamble([Statement('using namespace std')])
nvcc_mod.add_function(nvcc_function)
host_mod.add_function(
FunctionBody(
FunctionDeclaration(Value('void', 'solve'), [
Value('object', '_rowptr'),
Value('object', '_colidx'),
Value('object', '_csrdata'),
Value('object', '_b'),
Value('object', '_x'),
Value('object', '_nrows'),
Value('object', '_ncols'),
Value('object', '_nnz')
]),
Block([
Statement(
'CUdeviceptr rowptr = extract<CUdeviceptr>(_rowptr.attr("gpudata"))'
),
Statement(
'CUdeviceptr colidx = extract<CUdeviceptr>(_colidx.attr("gpudata"))'
),
Statement(
'CUdeviceptr csrdata = extract<CUdeviceptr>(_csrdata.attr("gpudata"))'
),
Statement(
'CUdeviceptr b = extract<CUdeviceptr>(_b.attr("gpudata"))'
),
Statement(
'CUdeviceptr x = extract<CUdeviceptr>(_x.attr("gpudata"))'
),
Statement('int nrows = extract<int>(_nrows)'),
Statement('int ncols = extract<int>(_ncols)'),
Statement('int nnz = extract<int>(_nnz)'),
Statement(
'__cusp_solve(rowptr, colidx, csrdata, b, x, nrows, ncols, nnz)'
)
])))
nvcc_toolchain.cflags.append('-arch')
nvcc_toolchain.cflags.append('sm_20')
nvcc_toolchain.cflags.append('-O3')
module = nvcc_mod.compile(gcc_toolchain,
nvcc_toolchain,
debug=configuration["debug"])
_cusp_cache[cache_key(M.ctype, parameters)] = module
return module
'PyObject* newShape = Py_BuildValue("(i)", intLength)',
'PyObject* kwargs = Py_BuildValue("{sOsOs%s}", "shape", newShape, "dtype", type, "gpudata", diffResult)' % ptr_sz_uint_conv,
'PyObject* GPUArrayClass = PyObject_GetAttrString(gpuArray, "__class__")',
'PyObject* remoteResult = PyObject_Call(GPUArrayClass, args, kwargs)',
'return remoteResult']
host_mod.add_function(
FunctionBody(
FunctionDeclaration(Pointer(Value("PyObject", "adjacentDifference")),
[Pointer(Value("PyObject", "gpuArray"))]),
Block([Statement(x) for x in statements])))
host_mod.add_to_preamble([Include('boost/python/extract.hpp')])
cuda_mod = CudaModule(host_mod)
cuda_mod.add_to_preamble([Include('cuda.h')])
globalIndex = 'int index = blockIdx.x * blockDim.x + threadIdx.x'
compute_diff = 'outputPtr[index] = inputPtr[index] - inputPtr[index-1]'
launch = ['CUdeviceptr output',
'cuMemAlloc(&output, sizeof(T) * length)',
'int bSize = 256',
'int gSize = (length-1)/bSize + 1',
'diffKernel<<<gSize, bSize>>>((T*)inputPtr, length, (T*)output)',
'return output']
diff =[
Template('typename T',
CudaGlobal(FunctionDeclaration(Value('void', 'diffKernel'),
[Value('T*', 'inputPtr'),
Value('int', 'length'),
#!python
import pycuda
import pycuda.autoinit
import pycuda.gpuarray as gpuarray
import numpy as np
from cgen import *
from codepy.bpl import BoostPythonModule
from codepy.cuda import CudaModule
#Make a host_module, compiled for CPU
host_mod = BoostPythonModule()
#Make a device module, compiled with NVCC
nvcc_mod = CudaModule(host_mod)
#Describe device module code
#NVCC includes
nvcc_includes = [
'thrust/sort.h',
'thrust/device_vector.h',
'cuda.h',
]
#Add includes to module
nvcc_mod.add_to_preamble([Include(x) for x in nvcc_includes])
#NVCC function
nvcc_function = FunctionBody(
FunctionDeclaration(
Value('void', 'my_sort'),
