ceed_vector.py (7f1dc7b91d4f5ca67db10df8f4f0b60afe166f53) ceed_vector.py (962dc42db4aaccb5017a51a7cc47f13cc4a674c9)
1# Copyright (c) 2017, Lawrence Livermore National Security, LLC. Produced at
2# the Lawrence Livermore National Laboratory. LLNL-CODE-734707. All Rights
3# reserved. See files LICENSE and NOTICE for details.
4#
5# This file is part of CEED, a collection of benchmarks, miniapps, software
6# libraries and APIs for efficient high-order finite element and spectral
7# element discretizations for exascale applications. For more information and
8# source code availability see http://github.com/ceed.
9#
10# The CEED research is supported by the Exascale Computing Project 17-SC-20-SC,
11# a collaborative effort of two U.S. Department of Energy organizations (Office
12# of Science and the National Nuclear Security Administration) responsible for
13# the planning and preparation of a capable exascale ecosystem, including
14# software, applications, hardware, advanced system engineering and early
15# testbed platforms, in support of the nation's exascale computing imperative.
16
17from _ceed_cffi import ffi, lib
18import tempfile
19import numpy as np
1# Copyright (c) 2017, Lawrence Livermore National Security, LLC. Produced at
2# the Lawrence Livermore National Laboratory. LLNL-CODE-734707. All Rights
3# reserved. See files LICENSE and NOTICE for details.
4#
5# This file is part of CEED, a collection of benchmarks, miniapps, software
6# libraries and APIs for efficient high-order finite element and spectral
7# element discretizations for exascale applications. For more information and
8# source code availability see http://github.com/ceed.
9#
10# The CEED research is supported by the Exascale Computing Project 17-SC-20-SC,
11# a collaborative effort of two U.S. Department of Energy organizations (Office
12# of Science and the National Nuclear Security Administration) responsible for
13# the planning and preparation of a capable exascale ecosystem, including
14# software, applications, hardware, advanced system engineering and early
15# testbed platforms, in support of the nation's exascale computing imperative.
16
17from _ceed_cffi import ffi, lib
18import tempfile
19import numpy as np
20import numba.cuda as nbcuda
21import contextlib
22from .ceed_constants import MEM_HOST, COPY_VALUES, NORM_2
23
24# ------------------------------------------------------------------------------
25
26
27class Vector():
28 """Ceed Vector: storing and manipulating vectors."""

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119 array_pointer[0],
120 ffi.sizeof("CeedScalar") *
121 length_pointer[0])
122 # return Numpy array
123 return np.frombuffer(buff, dtype="float64")
124 else:
125 # CUDA array interface
126 # https://numba.pydata.org/numba-doc/latest/cuda/cuda_array_interface.html
20import contextlib
21from .ceed_constants import MEM_HOST, COPY_VALUES, NORM_2
22
23# ------------------------------------------------------------------------------
24
25
26class Vector():
27 """Ceed Vector: storing and manipulating vectors."""

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118 array_pointer[0],
119 ffi.sizeof("CeedScalar") *
120 length_pointer[0])
121 # return Numpy array
122 return np.frombuffer(buff, dtype="float64")
123 else:
124 # CUDA array interface
125 # https://numba.pydata.org/numba-doc/latest/cuda/cuda_array_interface.html
126 import numba.cuda as nbcuda
127 desc = {
128 'shape': (length_pointer[0]),
129 'typestr': '>f8',
130 'data': (int(ffi.cast("intptr_t", array_pointer[0])), False),
131 'version': 2
132 }
133 # return Numba array
134 return nbcuda.from_cuda_array_interface(desc)

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162 length_pointer[0])
163 # return read only Numpy array
164 ret = np.frombuffer(buff, dtype="float64")
165 ret.flags['WRITEABLE'] = False
166 return ret
167 else:
168 # CUDA array interface
169 # https://numba.pydata.org/numba-doc/latest/cuda/cuda_array_interface.html
127 desc = {
128 'shape': (length_pointer[0]),
129 'typestr': '>f8',
130 'data': (int(ffi.cast("intptr_t", array_pointer[0])), False),
131 'version': 2
132 }
133 # return Numba array
134 return nbcuda.from_cuda_array_interface(desc)

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162 length_pointer[0])
163 # return read only Numpy array
164 ret = np.frombuffer(buff, dtype="float64")
165 ret.flags['WRITEABLE'] = False
166 return ret
167 else:
168 # CUDA array interface
169 # https://numba.pydata.org/numba-doc/latest/cuda/cuda_array_interface.html
170 import numba.cuda as nbcuda
170 desc = {
171 'shape': (length_pointer[0]),
172 'typestr': '>f8',
173 'data': (int(ffi.cast("intptr_t", array_pointer[0])), False),
174 'version': 2
175 }
176 # return read only Numba array
177 return nbcuda.from_cuda_array_interface(desc)

--- 148 unchanged lines hidden --- 		171 desc = {
172 'shape': (length_pointer[0]),
173 'typestr': '>f8',
174 'data': (int(ffi.cast("intptr_t", array_pointer[0])), False),
175 'version': 2
176 }
177 # return read only Numba array
178 return nbcuda.from_cuda_array_interface(desc)

--- 148 unchanged lines hidden ---