def test_cgen():
s = Struct(
"yuck",
[
POD(
np.float32,
"h",
),
POD(np.float32, "order"),
POD(np.float32, "face_jacobian"),
ArrayOf(POD(np.float32, "normal"), 17),
POD(np.uint16, "a_base"),
POD(np.uint16, "b_base"),
#CudaGlobal(POD(np.uint8, "a_ilist_number")),
POD(np.uint8, "b_ilist_number"),
POD(np.uint8, "bdry_flux_number"), # 0 if not on boundary
POD(np.uint8, "reserved"),
POD(np.uint32, "b_global_base"),
])
u = Union(
"yuck",
[
POD(
np.float32,
"h",
),
POD(np.float32, "order"),
POD(np.float32, "face_jacobian"),
ArrayOf(POD(np.float32, "normal"), 17),
POD(np.uint16, "a_base"),
POD(np.uint16, "b_base"),
#CudaGlobal(POD(np.uint8, "a_ilist_number")),
POD(np.uint8, "b_ilist_number"),
POD(np.uint8, "bdry_flux_number"), # 0 if not on boundary
POD(np.uint8, "reserved"),
POD(np.uint32, "b_global_base"),
])
f_decl = FunctionDeclaration(POD(np.uint16, "get_num"), [
POD(np.uint8, "reserved"),
POD(np.uint32, "b_global_base"),
])
f_body = FunctionBody(
f_decl,
Block([
POD(np.uint32, "i"),
For(
"i = 0",
"i < 17",
"++i",
If(
"a > b",
Assign("a", "b"),
Block([
Assign("a", "b-1"),
#Break(),
])),
),
#BlankLine(),
Comment("all done"),
]))
t_decl = Template(
'typename T',
FunctionDeclaration(
Value('CUdeviceptr', 'scan'),
[Value('CUdeviceptr', 'inputPtr'),
Value('int', 'length')]))
print(s)
print(u)
print(f_body)
print(t_decl)
def get_kernel(self, fdata, ilist_data, for_benchmark):
from cgen.cuda import CudaShared, CudaGlobal
from pycuda.tools import dtype_to_ctype
discr = self.discr
given = self.plan.given
fplan = self.plan
d = discr.dimensions
dims = range(d)
elgroup, = discr.element_groups
float_type = given.float_type
f_decl = CudaGlobal(
FunctionDeclaration(Value("void", "apply_flux"), [
Pointer(POD(float_type, "debugbuf")),
Pointer(POD(numpy.uint8, "gmem_facedata")),
] + [
Pointer(POD(float_type, "gmem_fluxes_on_faces%d" % flux_nr))
for flux_nr in range(len(self.fluxes))
]))
cmod = Module()
cmod.append(Include("pycuda-helpers.hpp"))
for dep_expr in self.all_deps:
cmod.extend([
Value(
"texture<%s, 1, cudaReadModeElementType>" %
dtype_to_ctype(float_type, with_fp_tex_hack=True),
"field%d_tex" % self.dep_to_index[dep_expr])
])
if fplan.flux_count != len(self.fluxes):
from warnings import warn
warn(
"Flux count in flux execution plan different from actual flux count.\n"
"You may want to specify the tune_for= kwarg in the Discretization\n"
"constructor.")
cmod.extend([
Line(),
Typedef(POD(float_type, "value_type")),
Line(),
flux_header_struct(float_type, discr.dimensions),
Line(),
face_pair_struct(float_type, discr.dimensions),
Line(),
Define("DIMENSIONS", discr.dimensions),
Define("DOFS_PER_FACE", fplan.dofs_per_face),
Define("THREADS_PER_FACE", fplan.threads_per_face()),
Line(),
Define("CONCURRENT_FACES", fplan.parallel_faces),
Define("BLOCK_MB_COUNT", fplan.mbs_per_block),
Line(),
Define("FACEDOF_NR", "threadIdx.x"),
Define("BLOCK_FACE", "threadIdx.y"),
Line(),
Define("FLUX_COUNT", len(self.fluxes)),
Line(),
Define("THREAD_NUM", "(FACEDOF_NR + BLOCK_FACE*THREADS_PER_FACE)"),
Define("THREAD_COUNT", "(THREADS_PER_FACE*CONCURRENT_FACES)"),
Define(
"COALESCING_THREAD_COUNT",
"(THREAD_COUNT < 0x10 ? THREAD_COUNT : THREAD_COUNT & ~0xf)"),
Line(),
Define("DATA_BLOCK_SIZE", fdata.block_bytes),
Define("ALIGNED_FACE_DOFS_PER_MB",
fplan.aligned_face_dofs_per_microblock()),
Define("ALIGNED_FACE_DOFS_PER_BLOCK",
"(ALIGNED_FACE_DOFS_PER_MB*BLOCK_MB_COUNT)"),
Line(),
Define("FOF_BLOCK_BASE",
"(blockIdx.x*ALIGNED_FACE_DOFS_PER_BLOCK)"),
Line(),
] + ilist_data.code + [
Line(),
Value("texture<index_list_entry_t, 1, cudaReadModeElementType>",
"tex_index_lists"),
Line(),
fdata.struct,
Line(),
CudaShared(Value("flux_data", "data")),
])
if not fplan.direct_store:
cmod.extend([
CudaShared(
ArrayOf(
ArrayOf(POD(float_type, "smem_fluxes_on_faces"),
"FLUX_COUNT"),
"ALIGNED_FACE_DOFS_PER_MB*BLOCK_MB_COUNT")),
Line(),
])
S = Statement
f_body = Block()
from hedge.backends.cuda.tools import get_load_code
f_body.extend(
get_load_code(dest="&data",
base="gmem_facedata + blockIdx.x*DATA_BLOCK_SIZE",
bytes="sizeof(flux_data)",
descr="load face_pair data") +
[S("__syncthreads()"), Line()])
def get_flux_code(flux_writer):
flux_code = Block([])
flux_code.extend([
Initializer(Pointer(Value("face_pair", "fpair")),
"data.facepairs+fpair_nr"),
Initializer(
MaybeUnused(POD(numpy.uint32, "a_index")),
"fpair->a_base + tex1Dfetch(tex_index_lists, "
"fpair->a_ilist_index + FACEDOF_NR)"),
Initializer(
MaybeUnused(POD(numpy.uint32, "b_index")),
"fpair->b_base + tex1Dfetch(tex_index_lists, "
"fpair->b_ilist_index + FACEDOF_NR)"),
Line(),
flux_writer(),
Line(),
S("fpair_nr += CONCURRENT_FACES")
])
return flux_code
flux_computation = Block([
Comment("fluxes for dual-sided (intra-block) interior face pairs"),
While("fpair_nr < data.header.same_facepairs_end",
get_flux_code(lambda: self.write_interior_flux_code(True))),
Line(),
Comment("work around nvcc assertion failure"),
S("fpair_nr+=1"),
S("fpair_nr-=1"),
Line(),
Comment(
"fluxes for single-sided (inter-block) interior face pairs"),
While("fpair_nr < data.header.diff_facepairs_end",
get_flux_code(lambda: self.write_interior_flux_code(False))),
Line(),
Comment("fluxes for single-sided boundary face pairs"),
While(
"fpair_nr < data.header.bdry_facepairs_end",
get_flux_code(
lambda: self.write_boundary_flux_code(for_benchmark))),
])
f_body.extend_log_block("compute the fluxes", [
Initializer(POD(numpy.uint32, "fpair_nr"), "BLOCK_FACE"),
If("FACEDOF_NR < DOFS_PER_FACE", flux_computation)
])
if not fplan.direct_store:
f_body.extend([Line(), S("__syncthreads()"), Line()])
f_body.extend_log_block(
"store fluxes",
[
#Assign("debugbuf[blockIdx.x]", "FOF_BLOCK_BASE"),
#Assign("debugbuf[0]", "FOF_BLOCK_BASE"),
#Assign("debugbuf[0]", "sizeof(face_pair)"),
For(
"unsigned word_nr = THREAD_NUM",
"word_nr < ALIGNED_FACE_DOFS_PER_MB*BLOCK_MB_COUNT",
"word_nr += COALESCING_THREAD_COUNT",
Block([
Assign(
"gmem_fluxes_on_faces%d[FOF_BLOCK_BASE+word_nr]"
% flux_nr,
"smem_fluxes_on_faces[%d][word_nr]" % flux_nr)
for flux_nr in range(len(self.fluxes))
]
#+[If("isnan(smem_fluxes_on_faces[%d][word_nr])" % flux_nr,
#Block([
#Assign("debugbuf[blockIdx.x]", "word_nr"),
#])
#)
#for flux_nr in range(len(self.fluxes))]
))
])
if False:
f_body.extend([
Assign("debugbuf[blockIdx.x*96+32+BLOCK_FACE*32+threadIdx.x]",
"fpair_nr"),
Assign("debugbuf[blockIdx.x*96+16]",
"data.header.same_facepairs_end"),
Assign("debugbuf[blockIdx.x*96+17]",
"data.header.diff_facepairs_end"),
Assign("debugbuf[blockIdx.x*96+18]",
"data.header.bdry_facepairs_end"),
])
# finish off ----------------------------------------------------------
cmod.append(FunctionBody(f_decl, f_body))
if not for_benchmark and "cuda_dump_kernels" in discr.debug:
from hedge.tools import open_unique_debug_file
open_unique_debug_file("flux_gather", ".cu").write(str(cmod))
#from pycuda.tools import allow_user_edit
mod = SourceModule(
#allow_user_edit(cmod, "kernel.cu", "the flux kernel"),
cmod,
keep="cuda_keep_kernels" in discr.debug)
expr_to_texture_map = dict(
(dep_expr,
mod.get_texref("field%d_tex" % self.dep_to_index[dep_expr]))
for dep_expr in self.all_deps)
index_list_texref = mod.get_texref("tex_index_lists")
index_list_texref.set_address(ilist_data.device_memory,
ilist_data.bytes)
index_list_texref.set_format(
cuda.dtype_to_array_format(ilist_data.type), 1)
index_list_texref.set_flags(cuda.TRSF_READ_AS_INTEGER)
func = mod.get_function("apply_flux")
block = (fplan.threads_per_face(), fplan.parallel_faces, 1)
func.prepare(
(2 + len(self.fluxes)) * "P",
texrefs=expr_to_texture_map.values() + [index_list_texref])
if "cuda_flux" in discr.debug:
print "flux: lmem=%d smem=%d regs=%d" % (
func.local_size_bytes, func.shared_size_bytes, func.num_regs)
return block, func, expr_to_texture_map
def get_kernel(self, diff_op, elgroup, for_benchmark=False):
from cgen import \
Pointer, POD, Value, ArrayOf, Const, \
Module, FunctionDeclaration, FunctionBody, Block, \
Comment, Line, Define, Include, \
Initializer, If, For, Statement, Assign
from pycuda.tools import dtype_to_ctype
from cgen.cuda import CudaShared, CudaGlobal
discr = self.discr
d = discr.dimensions
dims = range(d)
plan = self.plan
given = plan.given
elgroup, = discr.element_groups
float_type = given.float_type
f_decl = CudaGlobal(FunctionDeclaration(Value("void", "apply_diff_mat_smem"),
[Pointer(POD(float_type, "debugbuf")), Pointer(POD(float_type, "field")), ]
+ [Pointer(POD(float_type, "drst%d_global" % i)) for i in dims]
))
par = plan.parallelism
cmod = Module([
Include("pycuda-helpers.hpp"),
])
if float_type == numpy.float64:
cmod.append(Value("texture<fp_tex_double, 1, cudaReadModeElementType>",
"diff_rst_mat_tex"))
elif float_type == numpy.float32:
rst_channels = given.devdata.make_valid_tex_channel_count(d)
cmod.append(Value("texture<float%d, 1, cudaReadModeElementType>"
% rst_channels, "diff_rst_mat_tex"))
else:
raise ValueError("unsupported float type: %s" % float_type)
# only preimage size variation is supported here
assert plan.image_dofs_per_el == given.dofs_per_el()
assert plan.aligned_image_dofs_per_microblock == given.microblock.aligned_floats
# FIXME: aligned_image_dofs_per_microblock must be divisible
# by this, therefore hardcoding for now.
chunk_size = 16
cmod.extend([
Line(),
Define("DIMENSIONS", discr.dimensions),
Define("IMAGE_DOFS_PER_EL", plan.image_dofs_per_el),
Define("PREIMAGE_DOFS_PER_EL", plan.preimage_dofs_per_el),
Define("ALIGNED_IMAGE_DOFS_PER_MB", plan.aligned_image_dofs_per_microblock),
Define("ALIGNED_PREIMAGE_DOFS_PER_MB", plan.aligned_preimage_dofs_per_microblock),
Define("ELS_PER_MB", given.microblock.elements),
Define("IMAGE_DOFS_PER_MB", "(IMAGE_DOFS_PER_EL*ELS_PER_MB)"),
Line(),
Define("CHUNK_SIZE", chunk_size),
Define("CHUNK_DOF", "threadIdx.x"),
Define("PAR_MB_NR", "threadIdx.y"),
Define("CHUNK_NR", "threadIdx.z"),
Define("IMAGE_MB_DOF", "(CHUNK_NR*CHUNK_SIZE+CHUNK_DOF)"),
Define("IMAGE_EL_DOF", "(IMAGE_MB_DOF - mb_el*IMAGE_DOFS_PER_EL)"),
Line(),
Define("MACROBLOCK_NR", "blockIdx.x"),
Line(),
Define("PAR_MB_COUNT", par.parallel),
Define("INLINE_MB_COUNT", par.inline),
Define("SEQ_MB_COUNT", par.serial),
Line(),
Define("GLOBAL_MB_NR_BASE",
"(MACROBLOCK_NR*PAR_MB_COUNT*INLINE_MB_COUNT*SEQ_MB_COUNT)"),
Define("GLOBAL_MB_NR",
"(GLOBAL_MB_NR_BASE"
"+ (seq_mb_number*PAR_MB_COUNT + PAR_MB_NR)*INLINE_MB_COUNT)"),
Define("GLOBAL_MB_IMAGE_DOF_BASE", "(GLOBAL_MB_NR*ALIGNED_IMAGE_DOFS_PER_MB)"),
Define("GLOBAL_MB_PREIMAGE_DOF_BASE", "(GLOBAL_MB_NR*ALIGNED_PREIMAGE_DOFS_PER_MB)"),
Line(),
CudaShared(
ArrayOf(
ArrayOf(
ArrayOf(
POD(float_type, "smem_field"),
"PAR_MB_COUNT"),
"INLINE_MB_COUNT"),
"ALIGNED_PREIMAGE_DOFS_PER_MB")),
Line(),
])
S = Statement
f_body = Block([
Initializer(Const(POD(numpy.uint16, "mb_el")),
"IMAGE_MB_DOF / IMAGE_DOFS_PER_EL"),
Line(),
])
# ---------------------------------------------------------------------
def get_load_code():
mb_img_dofs = plan.aligned_image_dofs_per_microblock
mb_preimg_dofs = plan.aligned_preimage_dofs_per_microblock
preimg_dofs_over_dofs = (mb_preimg_dofs+mb_img_dofs-1) // mb_img_dofs
load_code = []
store_code = []
var_num = 0
for load_block in range(preimg_dofs_over_dofs):
for inl in range(par.inline):
# load and store are split for better pipelining
# compiler can't figure that out because of branch
var = "tmp%d" % var_num
var_num += 1
load_code.append(POD(float_type, var))
block_addr = "%d * ALIGNED_IMAGE_DOFS_PER_MB + IMAGE_MB_DOF" % load_block
load_instr = Assign(var,
"field[GLOBAL_MB_PREIMAGE_DOF_BASE"
" + %d*ALIGNED_PREIMAGE_DOFS_PER_MB"
" + %s]" % (inl, block_addr))
store_instr = Assign(
"smem_field[PAR_MB_NR][%d][%s]" % (inl, block_addr),
var
)
if (load_block+1)*mb_img_dofs >= mb_preimg_dofs:
cond = "%s < ALIGNED_PREIMAGE_DOFS_PER_MB" % block_addr
load_instr = If(cond, load_instr)
store_instr = If(cond, store_instr)
load_code.append(load_instr)
store_code.append(store_instr)
return Block(load_code + [Line()] + store_code)
def get_scalar_diff_code():
code = []
for inl in range(par.inline):
for axis in dims:
code.append(
Initializer(POD(float_type, "d%drst%d" % (inl, axis)), 0))
code.append(Line())
tex_channels = ["x", "y", "z", "w"]
store_code = Block()
for inl in range(par.inline):
for rst_axis in dims:
store_code.append(Assign(
"drst%d_global[GLOBAL_MB_IMAGE_DOF_BASE + "
"%d*ALIGNED_IMAGE_DOFS_PER_MB + IMAGE_MB_DOF]"
% (rst_axis, inl),
"d%drst%d" % (inl, rst_axis)
))
from hedge.backends.cuda.tools import unroll
code.extend([
Comment("everybody needs to be done with the old data"),
S("__syncthreads()"),
Line(),
get_load_code(),
Line(),
Comment("all the new data must be loaded"),
S("__syncthreads()"),
Line(),
])
if float_type == numpy.float32:
code.append(Value("float%d" % rst_channels, "dmat_entries"))
code.extend([
POD(float_type, "field_value%d" % inl)
for inl in range(par.inline)
]+[Line()])
def unroll_body(j):
result = [
Assign("field_value%d" % inl,
"smem_field[PAR_MB_NR][%d][mb_el*PREIMAGE_DOFS_PER_EL+%s]" % (inl, j))
for inl in range(par.inline)
]
if float_type == numpy.float32:
result.append(Assign("dmat_entries",
"tex1Dfetch(diff_rst_mat_tex, IMAGE_EL_DOF + %s*IMAGE_DOFS_PER_EL)" % j))
result.extend(
S("d%drst%d += dmat_entries.%s * field_value%d"
% (inl, axis, tex_channels[axis], inl))
for inl in range(par.inline)
for axis in dims)
elif float_type == numpy.float64:
result.extend(
S("d%(inl)drst%(axis)d += "
"fp_tex1Dfetch(diff_rst_mat_tex, %(axis)d "
"+ DIMENSIONS*(IMAGE_EL_DOF + %(j)d*IMAGE_DOFS_PER_EL))"
"* field_value%(inl)d" % {
"inl": inl,
"axis": axis,
"j": j
})
for inl in range(par.inline)
for axis in dims)
else:
assert False
return result
code.append(If("IMAGE_MB_DOF < IMAGE_DOFS_PER_MB", Block(unroll(unroll_body,
total_number=plan.preimage_dofs_per_el)
+[store_code])))
return code
f_body.extend([
For("unsigned short seq_mb_number = 0",
"seq_mb_number < SEQ_MB_COUNT",
"++seq_mb_number",
Block(get_scalar_diff_code())
)
])
# finish off ----------------------------------------------------------
cmod.append(FunctionBody(f_decl, f_body))
if not for_benchmark and "cuda_dump_kernels" in discr.debug:
from hedge.tools import open_unique_debug_file
open_unique_debug_file("diff", ".cu").write(str(cmod))
mod = SourceModule(cmod,
keep="cuda_keep_kernels" in discr.debug,
#options=["--maxrregcount=16"]
)
func = mod.get_function("apply_diff_mat_smem")
if "cuda_diff" in discr.debug:
print "diff: lmem=%d smem=%d regs=%d" % (
func.local_size_bytes,
func.shared_size_bytes,
func.registers)
diff_rst_mat_texref = mod.get_texref("diff_rst_mat_tex")
gpu_diffmats = self.gpu_diffmats(diff_op, elgroup)
if given.float_type == numpy.float32:
gpu_diffmats.bind_to_texref_ext(diff_rst_mat_texref, rst_channels)
elif given.float_type == numpy.float64:
gpu_diffmats.bind_to_texref_ext(diff_rst_mat_texref,
allow_double_hack=True)
else:
assert False
assert given.microblock.aligned_floats % chunk_size == 0
block = (
chunk_size,
plan.parallelism.parallel,
given.microblock.aligned_floats//chunk_size)
func.prepare(
["PP"] + discr.dimensions*["P"],
texrefs=[diff_rst_mat_texref])
return block, func
def get_boundary_flux_mod(fluxes, fvi, discr, dtype):
from cgen import \
FunctionDeclaration, FunctionBody, Typedef, Struct, \
Const, Reference, Value, POD, MaybeUnused, \
Statement, Include, Line, Block, Initializer, Assign, \
CustomLoop, For
from pytools import to_uncomplex_dtype, flatten
from codepy.bpl import BoostPythonModule
mod = BoostPythonModule()
mod.add_to_preamble([
Include("cstdlib"),
Include("algorithm"),
Line(),
Include("boost/foreach.hpp"),
Line(),
Include("hedge/face_operators.hpp"),
])
S = Statement
mod.add_to_module([
S("using namespace hedge"),
S("using namespace pyublas"),
Line(),
Typedef(POD(dtype, "value_type")),
Typedef(POD(to_uncomplex_dtype(dtype), "uncomplex_type")),
])
arg_struct = Struct("arg_struct", [
Value("numpy_array<value_type>", "flux%d_on_faces" % i)
for i in range(len(fluxes))
]+[
Value("numpy_array<value_type>", arg_name)
for arg_name in fvi.arg_names
])
mod.add_struct(arg_struct, "ArgStruct")
mod.add_to_module([Line()])
fdecl = FunctionDeclaration(
Value("void", "gather_flux"),
[
Const(Reference(Value("face_group<face_pair<straight_face> >" , "fg"))),
Reference(Value("arg_struct", "args"))
])
from pymbolic.mapper.stringifier import PREC_PRODUCT
def gen_flux_code():
f2cm = FluxToCodeMapper()
result = [
Assign("fof%d_it[loc_fof_base+i]" % flux_idx,
"uncomplex_type(fp.int_side.face_jacobian) * " +
flux_to_code(f2cm, False, flux_idx, fvi, flux.op.flux, PREC_PRODUCT))
for flux_idx, flux in enumerate(fluxes)
]
return [
Initializer(Value("value_type", cse_name), cse_str)
for cse_name, cse_str in f2cm.cse_name_list] + result
fbody = Block([
Initializer(
Const(Value("numpy_array<value_type>::iterator", "fof%d_it" % i)),
"args.flux%d_on_faces.begin()" % i)
for i in range(len(fluxes))
]+[
Initializer(
Const(Value("numpy_array<value_type>::const_iterator",
"%s_it" % arg_name)),
"args.%s.begin()" % arg_name)
for arg_name in fvi.arg_names
]+[
Line(),
CustomLoop("BOOST_FOREACH(const face_pair<straight_face> &fp, fg.face_pairs)", Block(
list(flatten([
Initializer(Value("node_number_t", "%s_ebi" % where),
"fp.%s.el_base_index" % where),
Initializer(Value("index_lists_t::const_iterator", "%s_idx_list" % where),
"fg.index_list(fp.%s.face_index_list_number)" % where),
Line(),
]
for where in ["int_side", "ext_side"]
))+[
Line(),
Initializer(Value("node_number_t", "loc_fof_base"),
"fg.face_length()*(fp.%(where)s.local_el_number*fg.face_count"
" + fp.%(where)s.face_id)" % {"where": "int_side"}),
Line(),
For(
"unsigned i = 0",
"i < fg.face_length()",
"++i",
Block(
[
Initializer(MaybeUnused(
Value("node_number_t", "%s_idx" % where)),
"%(where)s_ebi + %(where)s_idx_list[i]"
% {"where": where})
for where in ["int_side", "ext_side"]
]+gen_flux_code()
)
)
]))
])
mod.add_function(FunctionBody(fdecl, fbody))
#print "----------------------------------------------------------------"
#print mod.generate()
#raw_input("[Enter]")
return mod.compile(get_flux_toolchain(discr, fluxes))
def create_native(self):
from cgen import (ArrayOf, POD, Block, For, Statement, Struct)
from cgen import dtype_to_ctype
import numpy
members = []
code = []
for pk, pv in config.parameters.iteritems():
if isinstance(pv, int):
members.append(POD(numpy.int, pk))
code.append(
Statement("params.%s = extract<%s>(cppdict[\"%s\"])" %
(pk, dtype_to_ctype(numpy.int), pk)))
elif isinstance(pv, float):
members.append(POD(numpy.float64, pk))
code.append(
Statement("params.%s = extract<%s>(cppdict[\"%s\"])" %
(pk, dtype_to_ctype(numpy.float64), pk)))
elif isinstance(pv, list):
if isinstance(pv[0], int):
members.append(ArrayOf(POD(numpy.int, pk), len(pv)))
code.append(
Block([
Statement("list v = extract<%s>(cppdict[\"%s\"])" %
(list.__name__, pk)),
For(
"unsigned int i = 0", "i<len(v)", "++i",
Statement("params.%s[i] = extract<%s>(v[i])" %
(pk, dtype_to_ctype(numpy.int)))),
]))
elif isinstance(pv[0], float):
members.append(ArrayOf(POD(numpy.float64, pk), len(pv)))
code.append(
Block([
Statement("list v = extract<%s>(cppdict[\"%s\"])" %
(list.__name__, pk)),
For(
"unsigned int i = 0", "i < len(v)", "++i",
Block([
Statement(
"params.%s[i] = extract<%s>(v[i])" %
(pk, dtype_to_ctype(numpy.float64))),
Statement(
"//std::cout << params.%s[i] << std::endl"
% (pk))
])),
]))
mystruct = Struct('Parameters', members)
mycode = Block(code)
# print mystruct
# print mycode
from jinja2 import Template
tpl = Template("""
#include <boost/python.hpp>
#include <boost/python/object.hpp>
#include <boost/python/extract.hpp>
#include <boost/python/list.hpp>
#include <boost/python/dict.hpp>
#include <boost/python/str.hpp>
#include <stdexcept>
#include <iostream>
{{my_struct}}
Parameters params;
void CopyDictionary(boost::python::object pydict)
{
using namespace boost::python;
extract< dict > cppdict_ext(pydict);
if(!cppdict_ext.check()){
throw std::runtime_error(
"PassObj::pass_dict: type error: not a python dict.");
}
dict cppdict = cppdict_ext();
list keylist = cppdict.keys();
{{my_extractor}}
}
BOOST_PYTHON_MODULE({{my_module}})
{
boost::python::def("copy_dict", &CopyDictionary);
}
""")
rendered_tpl = tpl.render(my_module="NativeParameters",
my_extractor=mycode,
my_struct=mystruct)
# print rendered_tpl
from codepy.toolchain import NVCCToolchain
import codepy.toolchain
kwargs = codepy.toolchain._guess_toolchain_kwargs_from_python_config()
# print kwargs
kwargs["cc"] = "nvcc"
# kwargs["cflags"]=["-m64","-x","cu","-Xcompiler","-fPIC","-ccbin","/opt/local/bin/g++-mp-4.4"]
kwargs["cflags"] = ["-m64", "-x", "cu", "-Xcompiler", "-fPIC"]
kwargs["include_dirs"].append("/usr/local/cuda/include")
kwargs["defines"] = []
kwargs["ldflags"] = ["-shared"]
# kwargs["libraries"]=["python2.7"]
kwargs["libraries"] = ["python2.6"]
print kwargs
toolchain = NVCCToolchain(**kwargs)
from codepy.libraries import add_boost_python
add_boost_python(toolchain)
from codepy.jit import extension_from_string
mymod = extension_from_string(toolchain, "NativeParameters",
rendered_tpl)
mymod.copy_dict(config.parameters)
def get_kernel(self, diff_op_cls, elgroup, for_benchmark=False):
from cgen import \
Pointer, POD, Value, ArrayOf, \
Module, FunctionDeclaration, FunctionBody, Block, \
Line, Define, Include, \
Initializer, If, For, Statement, Assign
from cgen import dtype_to_ctype
from cgen.cuda import CudaShared, CudaGlobal
discr = self.discr
d = discr.dimensions
dims = range(d)
given = self.plan.given
par = self.plan.parallelism
diffmat_data = self.gpu_diffmats(diff_op_cls, elgroup)
elgroup, = discr.element_groups
float_type = given.float_type
f_decl = CudaGlobal(
FunctionDeclaration(
Value("void", "apply_diff_mat"),
[
Pointer(POD(numpy.uint8, "gmem_diff_rst_mat")),
#Pointer(POD(float_type, "debugbuf")),
] +
[Pointer(POD(float_type, "drst%d_global" % i)) for i in dims]))
rst_channels = given.devdata.make_valid_tex_channel_count(d)
cmod = Module([
Include("pycuda-helpers.hpp"),
Line(),
Value(
"texture<fp_tex_%s, 1, cudaReadModeElementType>" %
dtype_to_ctype(float_type), "field_tex"),
Line(),
Define("DIMENSIONS", discr.dimensions),
Define("DOFS_PER_EL", given.dofs_per_el()),
Line(),
Define("SEGMENT_DOF", "threadIdx.x"),
Define("PAR_MB_NR", "threadIdx.y"),
Line(),
Define("MB_SEGMENT", "blockIdx.x"),
Define("MACROBLOCK_NR", "blockIdx.y"),
Line(),
Define("DOFS_PER_SEGMENT", self.plan.segment_size),
Define("SEGMENTS_PER_MB", self.plan.segments_per_microblock()),
Define("ALIGNED_DOFS_PER_MB", given.microblock.aligned_floats),
Define("ELS_PER_MB", given.microblock.elements),
Line(),
Define("PAR_MB_COUNT", par.parallel),
Define("INLINE_MB_COUNT", par.inline),
Define("SEQ_MB_COUNT", par.serial),
Line(),
Define("THREAD_NUM", "(SEGMENT_DOF+PAR_MB_NR*DOFS_PER_SEGMENT)"),
Define("COALESCING_THREAD_COUNT",
"(PAR_MB_COUNT*DOFS_PER_SEGMENT)"),
Line(),
Define("MB_DOF_BASE", "(MB_SEGMENT*DOFS_PER_SEGMENT)"),
Define("MB_DOF", "(MB_DOF_BASE+SEGMENT_DOF)"),
Define(
"GLOBAL_MB_NR_BASE",
"(MACROBLOCK_NR*PAR_MB_COUNT*INLINE_MB_COUNT*SEQ_MB_COUNT)"),
Define(
"GLOBAL_MB_NR", "(GLOBAL_MB_NR_BASE"
"+ (seq_mb_number*PAR_MB_COUNT + PAR_MB_NR)*INLINE_MB_COUNT)"),
Define("GLOBAL_MB_DOF_BASE", "(GLOBAL_MB_NR*ALIGNED_DOFS_PER_MB)"),
Line(),
Define("DIFFMAT_SEGMENT_FLOATS", diffmat_data.block_floats),
Define("DIFFMAT_SEGMENT_BYTES",
"(DIFFMAT_SEGMENT_FLOATS*%d)" % given.float_size()),
Define("DIFFMAT_COLUMNS", diffmat_data.matrix_columns),
Line(),
CudaShared(
ArrayOf(POD(float_type, "smem_diff_rst_mat"),
"DIFFMAT_COLUMNS*DOFS_PER_SEGMENT")),
Line(),
])
S = Statement
f_body = Block()
f_body.extend_log_block("calculate responsibility data", [
Initializer(POD(numpy.uint16, "mb_el"), "MB_DOF/DOFS_PER_EL"),
])
from hedge.backends.cuda.tools import get_load_code
f_body.extend(
get_load_code(
dest="smem_diff_rst_mat",
base="gmem_diff_rst_mat + MB_SEGMENT*DIFFMAT_SEGMENT_BYTES",
bytes="DIFFMAT_SEGMENT_BYTES",
descr="load diff mat segment") +
[S("__syncthreads()"), Line()])
# ---------------------------------------------------------------------
def get_scalar_diff_code():
code = []
for inl in range(par.inline):
for axis in dims:
code.append(
Initializer(POD(float_type, "d%drst%d" % (inl, axis)),
0))
code.append(Line())
def get_mat_entry(row, col, axis):
return ("smem_diff_rst_mat["
"%(row)s*DIFFMAT_COLUMNS + %(axis)s*DOFS_PER_EL"
" + %(col)s"
"]" % {
"row": row,
"col": col,
"axis": axis
})
tex_channels = ["x", "y", "z", "w"]
from hedge.backends.cuda.tools import unroll
code.extend([
POD(float_type, "field_value%d" % inl)
for inl in range(par.inline)
] + [Line()] + unroll(
lambda j: [
Assign(
"field_value%d" % inl,
"fp_tex1Dfetch(field_tex, GLOBAL_MB_DOF_BASE + %d*ALIGNED_DOFS_PER_MB "
"+ mb_el*DOFS_PER_EL + %s)" % (inl, j))
for inl in range(par.inline)
] + [Line()] + [
S("d%drst%d += %s * field_value%d" %
(inl, axis, get_mat_entry("SEGMENT_DOF", j, axis), inl))
for axis in dims for inl in range(par.inline)
] + [Line()], given.dofs_per_el(), self.plan.max_unroll))
store_code = Block()
for inl in range(par.inline):
for rst_axis in dims:
store_code.append(
Assign(
"drst%d_global[GLOBAL_MB_DOF_BASE"
" + %d*ALIGNED_DOFS_PER_MB + MB_DOF]" %
(rst_axis, inl),
"d%drst%d" % (inl, rst_axis),
))
code.append(If("MB_DOF < DOFS_PER_EL*ELS_PER_MB", store_code))
return code
f_body.extend([
For("unsigned short seq_mb_number = 0",
"seq_mb_number < SEQ_MB_COUNT", "++seq_mb_number",
Block(get_scalar_diff_code()))
])
# finish off ----------------------------------------------------------
cmod.append(FunctionBody(f_decl, f_body))
if not for_benchmark and "cuda_dump_kernels" in discr.debug:
from hedge.tools import open_unique_debug_file
open_unique_debug_file("diff", ".cu").write(str(cmod))
mod = SourceModule(
cmod,
keep="cuda_keep_kernels" in discr.debug,
#options=["--maxrregcount=10"]
)
field_texref = mod.get_texref("field_tex")
func = mod.get_function("apply_diff_mat")
func.prepare(discr.dimensions * [float_type] + ["P"],
block=(self.plan.segment_size, par.parallel, 1),
texrefs=[field_texref])
if "cuda_diff" in discr.debug:
print "diff: lmem=%d smem=%d regs=%d" % (
func.local_size_bytes, func.shared_size_bytes, func.num_regs)
return func, field_texref
def make_lift(self, fgroup, with_scale, dtype):
discr = self.discr
from cgen import (FunctionDeclaration, FunctionBody, Typedef, Const,
Reference, Value, POD, Statement, Include, Line,
Block, Initializer, Assign, For, If, Define)
from pytools import to_uncomplex_dtype
from codepy.bpl import BoostPythonModule
mod = BoostPythonModule()
S = Statement
mod.add_to_preamble([
Include("hedge/face_operators.hpp"),
Include("hedge/volume_operators.hpp"),
Include("boost/foreach.hpp"),
])
mod.add_to_module([
S("namespace ublas = boost::numeric::ublas"),
S("using namespace hedge"),
S("using namespace pyublas"),
Line(),
Define("DOFS_PER_EL", fgroup.ldis_loc.node_count()),
Define("FACES_PER_EL", fgroup.ldis_loc.face_count()),
Define("DIMENSIONS", discr.dimensions),
Line(),
Typedef(POD(dtype, "value_type")),
Typedef(POD(to_uncomplex_dtype(dtype), "uncomplex_type")),
])
def if_(cond, result, else_=None):
if cond:
return [result]
else:
if else_ is None:
return []
else:
return [else_]
fdecl = FunctionDeclaration(Value("void", "lift"), [
Const(
Reference(Value("face_group<face_pair<straight_face> >",
"fg"))),
Value("ublas::matrix<uncomplex_type>", "matrix"),
Value("numpy_array<value_type>", "field"),
Value("numpy_array<value_type>", "result")
] + if_(
with_scale,
Const(
Reference(Value("numpy_array<double>",
"elwise_post_scaling")))))
def make_it(name, is_const=True, tpname="value_type"):
if is_const:
const = "const_"
else:
const = ""
return Initializer(
Value("numpy_array<%s>::%siterator" % (tpname, const),
name + "_it"), "%s.begin()" % name)
fbody = Block([
make_it("field"),
make_it("result", is_const=False),
] + if_(with_scale, make_it("elwise_post_scaling", tpname="double")) + [
Line(),
For(
"unsigned fg_el_nr = 0", "fg_el_nr < fg.element_count()",
"++fg_el_nr",
Block([
Initializer(Value("node_number_t", "dest_el_base"),
"fg.local_el_write_base[fg_el_nr]"),
Initializer(Value("node_number_t", "src_el_base"),
"FACES_PER_EL*fg.face_length()*fg_el_nr"),
Line(),
For(
"unsigned i = 0", "i < DOFS_PER_EL", "++i",
Block([
Initializer(Value("value_type", "tmp"), 0),
Line(),
For(
"unsigned j = 0",
"j < FACES_PER_EL*fg.face_length()", "++j",
S("tmp += matrix(i, j)*field_it[src_el_base+j]"
)),
Line(),
] + if_(
with_scale,
Assign(
"result_it[dest_el_base+i]",
"tmp * value_type(*elwise_post_scaling_it)"),
Assign("result_it[dest_el_base+i]", "tmp")))),
] + if_(with_scale, S("elwise_post_scaling_it++"))))
])
mod.add_function(FunctionBody(fdecl, fbody))
#print "----------------------------------------------------------------"
#print FunctionBody(fdecl, fbody)
#raw_input()
return mod.compile(self.discr.toolchain).lift
def make_diff(self, elgroup, dtype, shape):
"""
:param shape: If non-square, the resulting code takes two element_ranges
arguments and supports non-square matrices.
"""
from hedge._internal import UniformElementRanges
assert isinstance(elgroup.ranges, UniformElementRanges)
ldis = elgroup.local_discretization
discr = self.discr
from cgen import (
FunctionDeclaration, FunctionBody, Typedef,
Const, Reference, Value, POD,
Statement, Include, Line, Block, Initializer, Assign,
For, If,
Define)
from pytools import to_uncomplex_dtype
from codepy.bpl import BoostPythonModule
mod = BoostPythonModule()
# {{{ preamble
S = Statement
mod.add_to_preamble([
Include("hedge/volume_operators.hpp"),
Include("boost/foreach.hpp"),
])
mod.add_to_module([
S("namespace ublas = boost::numeric::ublas"),
S("using namespace hedge"),
S("using namespace pyublas"),
Line(),
Define("ROW_COUNT", shape[0]),
Define("COL_COUNT", shape[1]),
Define("DIMENSIONS", discr.dimensions),
Line(),
Typedef(POD(dtype, "value_type")),
Typedef(POD(to_uncomplex_dtype(dtype), "uncomplex_type")),
])
fdecl = FunctionDeclaration(
Value("void", "diff"),
[
Const(Reference(Value("uniform_element_ranges", "from_ers"))),
Const(Reference(Value("uniform_element_ranges", "to_ers"))),
Value("numpy_array<value_type>", "field")
]+[
Value("ublas::matrix<uncomplex_type>", "diffmat_rst%d" % rst)
for rst in range(discr.dimensions)
]+[
Value("numpy_array<value_type>", "result%d" % i)
for i in range(discr.dimensions)
]
)
# }}}
# {{{ set-up
def make_it(name, is_const=True, tpname="value_type"):
if is_const:
const = "const_"
else:
const = ""
return Initializer(
Value("numpy_array<%s>::%siterator" % (tpname, const), name+"_it"),
"%s.begin()" % name)
fbody = Block([
If("ROW_COUNT != diffmat_rst%d.size1()" % i,
S('throw(std::runtime_error("unexpected matrix size"))'))
for i in range(discr.dimensions)
] + [
If("COL_COUNT != diffmat_rst%d.size2()" % i,
S('throw(std::runtime_error("unexpected matrix size"))'))
for i in range(discr.dimensions)
]+[
If("ROW_COUNT != to_ers.el_size()",
S('throw(std::runtime_error("unsupported image element size"))')),
If("COL_COUNT != from_ers.el_size()",
S('throw(std::runtime_error("unsupported preimage element size"))')),
If("from_ers.size() != to_ers.size()",
S('throw(std::runtime_error("image and preimage element groups '
'do nothave the same element count"))')),
Line(),
make_it("field"),
]+[
make_it("result%d" % i, is_const=False)
for i in range(discr.dimensions)
]+[
Line(),
# }}}
# {{{ computation
For("element_number_t eg_el_nr = 0",
"eg_el_nr < to_ers.size()",
"++eg_el_nr",
Block([
Initializer(
Value("node_number_t", "from_el_base"),
"from_ers.start() + eg_el_nr*COL_COUNT"),
Initializer(
Value("node_number_t", "to_el_base"),
"to_ers.start() + eg_el_nr*ROW_COUNT"),
Line(),
For("unsigned i = 0",
"i < ROW_COUNT",
"++i",
Block([
Initializer(Value("value_type", "drst_%d" % rst), 0)
for rst in range(discr.dimensions)
]+[
Line(),
]+[
For("unsigned j = 0",
"j < COL_COUNT",
"++j",
Block([
S("drst_%(rst)d += "
"diffmat_rst%(rst)d(i, j)*field_it[from_el_base+j]"
% {"rst":rst})
for rst in range(discr.dimensions)
])
),
Line(),
]+[
Assign("result%d_it[to_el_base+i]" % rst,
"drst_%d" % rst)
for rst in range(discr.dimensions)
])
)
])
)
])
# }}}
# {{{ compilation
mod.add_function(FunctionBody(fdecl, fbody))
#print "----------------------------------------------------------------"
#print mod.generate()
#raw_input()
compiled_func = mod.compile(self.discr.toolchain).diff
if self.discr.instrumented:
from hedge.tools import time_count_flop
compiled_func = time_count_flop(compiled_func,
discr.diff_timer, discr.diff_counter,
discr.diff_flop_counter,
flops=discr.dimensions*(
2 # mul+add
* ldis.node_count() * len(elgroup.members)
* ldis.node_count()
+
2 * discr.dimensions
* len(elgroup.members) * ldis.node_count()),
increment=discr.dimensions)
return compiled_func