def _call_join_multi(api, ncols, col_left, col_right, ctxt):
join_result_ptr = ffi.new("gdf_join_result_type**", None)
api(ncols, col_left, col_right, join_result_ptr, ctxt)
join_result = join_result_ptr[0]
print('join_result', join_result)
dataptr = libgdf.gdf_join_result_data(join_result)
print(dataptr)
datasize = libgdf.gdf_join_result_size(join_result)
print(datasize)
addr = ctypes.c_uint64(int(ffi.cast("uintptr_t", dataptr)))
print(hex(addr.value))
memptr = cuda.driver.MemoryPointer(context=cuda.current_context(),
pointer=addr,
size=4 * datasize)
print(memptr)
ary = cuda.devicearray.DeviceNDArray(shape=(datasize, ),
strides=(4, ),
dtype=np.dtype(np.int32),
gpu_data=memptr)
joined_idx = ary.reshape(2, datasize // 2).copy_to_host()
print(joined_idx)
libgdf.gdf_join_result_free(join_result)
return joined_idx
def apply_join(col_lhs, col_rhs, how):
"""Returns a tuple of the left and right joined indices as gpu arrays.
"""
if (len(col_lhs) != len(col_rhs)):
msg = "Unequal #columns in list 'col_lhs' and list 'col_rhs'"
raise ValueError(msg)
joiner = _join_how_api[how]
join_result_ptr = ffi.new("gdf_join_result_type**", None)
if (how == 'left'):
list_lhs = []
list_rhs = []
for i in range(len(col_lhs)):
list_lhs.append(col_lhs[i].cffi_view)
list_rhs.append(col_rhs[i].cffi_view)
# Call libgdf
joiner(len(col_lhs), list_lhs, list_rhs, join_result_ptr)
else:
joiner(col_lhs[0].cffi_view, col_rhs[0].cffi_view, join_result_ptr)
# Extract result
join_result = join_result_ptr[0]
dataptr = libgdf.gdf_join_result_data(join_result)
datasize = libgdf.gdf_join_result_size(join_result)
ary = _as_numba_devarray(intaddr=int(ffi.cast("uintptr_t", dataptr)),
nelem=datasize,
dtype=np.int32)
ary = ary.reshape(2, datasize // 2)
yield ((ary[0], ary[1]) if datasize > 0 else (ary, ary))
libgdf.gdf_join_result_free(join_result)
def apply_join(col_lhs, col_rhs, how):
"""Returns a tuple of the left and right joined indices as gpu arrays.
"""
joiner = _join_how_api[how]
join_result_ptr = ffi.new("gdf_join_result_type**", None)
# Call libgdf
joiner(col_lhs.cffi_view, col_rhs.cffi_view, join_result_ptr)
# Extract result
join_result = join_result_ptr[0]
dataptr = libgdf.gdf_join_result_data(join_result)
datasize = libgdf.gdf_join_result_size(join_result)
ary = _as_numba_devarray(intaddr=int(ffi.cast("uintptr_t", dataptr)),
nelem=datasize, dtype=np.int32)
ary = ary.reshape(2, datasize // 2)
yield ((ary[0], ary[1]) if datasize > 0 else (ary, ary))
libgdf.gdf_join_result_free(join_result)
def apply_join(col_lhs, col_rhs, how):
"""Returns a tuple of the left and right joined indices as gpu arrays.
"""
joiner = _join_how_api[how]
join_result_ptr = ffi.new("gdf_join_result_type**", None)
# Call libgdf
joiner(col_lhs.cffi_view, col_rhs.cffi_view, join_result_ptr)
# Extract result
join_result = join_result_ptr[0]
dataptr = libgdf.gdf_join_result_data(join_result)
datasize = libgdf.gdf_join_result_size(join_result)
ary = _as_numba_devarray(intaddr=int(ffi.cast("uintptr_t", dataptr)),
nelem=datasize, dtype=np.int32)
ary = ary.reshape(2, datasize // 2)
yield ((ary[0], ary[1]) if datasize > 0 else (ary, ary))
libgdf.gdf_join_result_free(join_result)
