diff --git a/binder/environment.yml b/binder/environment.yml
index 2df28e8726bb78e60584279bb376bd579a16a0d0..b4acd2e2a083295c272c4082c80ed7f46d2ca498 100644
--- a/binder/environment.yml
+++ b/binder/environment.yml
@@ -7,7 +7,8 @@
# conda env create -f conda_environment_user.yml
# . activate pystencils
#
-# If you have CUDA installed and want to use your GPU, uncomment the last line to install pycuda
+# If you have CUDA or ROCm installed and want to use your GPU, uncomment the last line to install cupy
+# Be careful to install the correct cupy version depending on your CUDA or ROCm version ...
#
# ----------------------------------------------------------------------------------------------------------------------
@@ -33,4 +34,4 @@ dependencies:
- pyevtk # VTK output for serial simulations
- blitzdb # file-based No-SQL database to store simulation results
- pystencils
- #- pycuda # add this if you have CUDA installed
+ #- cupy # add this if you have CUDA or ROCm installed
diff --git a/conftest.py b/conftest.py
index 9f9eac6300f08d0854bb9e5bb67c068cb0dabd96..e71dff56416d4fa2a5c01eab39e1853b90fe1969 100644
--- a/conftest.py
+++ b/conftest.py
@@ -46,7 +46,7 @@ add_path_to_ignore('pystencils_tests/benchmark')
add_path_to_ignore('_local_tmp')
try:
- import pycuda
+ import cupy
except ImportError:
collect_ignore += [os.path.join(SCRIPT_FOLDER, "lbmpy_tests/test_cpu_gpu_equivalence.py")]
diff --git a/doc/notebooks/00_tutorial_lbmpy_walberla_overview.ipynb b/doc/notebooks/00_tutorial_lbmpy_walberla_overview.ipynb
index 2ce352c1aa47351d04d9ad426caa183392c543e4..d1181427c738344f5f937bcaecdd693c66525fc4 100644
--- a/doc/notebooks/00_tutorial_lbmpy_walberla_overview.ipynb
+++ b/doc/notebooks/00_tutorial_lbmpy_walberla_overview.ipynb
@@ -17,14 +17,14 @@
"outputs": [],
"source": [
"try:\n",
- " import pycuda\n",
+ " import cupy\n",
"except ImportError:\n",
- " pycuda = None\n",
+ " cupy = None\n",
" gpu = False\n",
" target = ps.Target.CPU\n",
- " print('No pycuda installed')\n",
+ " print('No cupy installed')\n",
"\n",
- "if pycuda:\n",
+ "if cupy:\n",
" gpu = True\n",
" target = ps.Target.GPU"
]
@@ -136,7 +136,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Note that this code has the relaxation rate and array sizes inserted as numeric constants. This additional information helps the C compiler to generate faster code. Also, having the code in symbolic form makes it easy to generate code for different platforms as well: C(++) for CPUs, optionally with platform specific SIMD instrinsics or CUDA for Nvidia GPUs. To run the lid driven cavity on GPUs all it takes are the following changes:"
+ "Note that this code has the relaxation rate and array sizes inserted as numeric constants. This additional information helps the C compiler to generate faster code. Also, having the code in symbolic form makes it easy to generate code for different platforms as well: C(++) for CPUs, optionally with platform specific SIMD instrinsics or GPUs with CUDA. To run the lid driven cavity on GPUs all it takes are the following changes:"
]
},
{
@@ -603,7 +603,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.2"
+ "version": "3.11.0rc1"
}
},
"nbformat": 4,
diff --git a/doc/notebooks/01_tutorial_predefinedScenarios.ipynb b/doc/notebooks/01_tutorial_predefinedScenarios.ipynb
index 40483f9cd720e6d35df6828890651f5b19dbd8e7..c7f0f93981506ee952e2345766b496294e9466b4 100644
--- a/doc/notebooks/01_tutorial_predefinedScenarios.ipynb
+++ b/doc/notebooks/01_tutorial_predefinedScenarios.ipynb
@@ -8,7 +8,7 @@
{
"data": {
"text/plain": [
- "<module 'pycuda' from '/home/markus/miniconda3/envs/pystencils/lib/python3.8/site-packages/pycuda/__init__.py'>"
+ "<module 'cupy' from '/home/markus/.local/lib/python3.11/site-packages/cupy/__init__.py'>"
]
},
"execution_count": 1,
@@ -18,7 +18,7 @@
],
"source": [
"import pytest\n",
- "pytest.importorskip('pycuda')"
+ "pytest.importorskip('cupy')"
]
},
{
@@ -660,7 +660,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -674,7 +674,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.2"
+ "version": "3.11.0rc1"
}
},
"nbformat": 4,
diff --git a/doc/notebooks/10_tutorial_conservative_allen_cahn_two_phase.ipynb b/doc/notebooks/10_tutorial_conservative_allen_cahn_two_phase.ipynb
index 236ee1b9f3cf6c3cb4ac391289ea008693308b86..ded1b3321281441b8573c78e911c536b9ceaeef4 100644
--- a/doc/notebooks/10_tutorial_conservative_allen_cahn_two_phase.ipynb
+++ b/doc/notebooks/10_tutorial_conservative_allen_cahn_two_phase.ipynb
@@ -30,32 +30,24 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "If `pycuda` is installed the simulation automatically runs on GPU"
+ "If `cupy` is installed the simulation automatically runs on GPU"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "No pycuda installed\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"try:\n",
- " import pycuda\n",
+ " import cupy\n",
"except ImportError:\n",
- " pycuda = None\n",
+ " cupy = None\n",
" gpu = False\n",
" target = ps.Target.CPU\n",
- " print('No pycuda installed')\n",
+ " print('No cupy installed')\n",
"\n",
- "if pycuda:\n",
+ "if cupy:\n",
" gpu = True\n",
" target = ps.Target.GPU"
]
@@ -925,7 +917,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.9"
+ "version": "3.11.0rc1"
}
},
"nbformat": 4,
diff --git a/lbmpy/advanced_streaming/communication.py b/lbmpy/advanced_streaming/communication.py
index 786c60092d85ffbf28d5dfa9880f6d005ca8da4c..9c7dc4ca16af5ddb71dc0e147015af3c9bbde411 100644
--- a/lbmpy/advanced_streaming/communication.py
+++ b/lbmpy/advanced_streaming/communication.py
@@ -12,15 +12,15 @@ class LBMPeriodicityHandling:
def __init__(self, stencil, data_handling, pdf_field_name,
streaming_pattern='pull', ghost_layers=1,
- pycuda_direct_copy=True):
+ cupy_direct_copy=True):
"""
Periodicity Handling for Lattice Boltzmann Streaming.
**On the usage with cuda:**
- - pycuda allows the copying of sliced arrays within device memory using the numpy syntax,
+ - cupy allows the copying of sliced arrays within device memory using the numpy syntax,
e.g. `dst[:,0] = src[:,-1]`. In this implementation, this is the default for periodicity
- handling. Alternatively, if you set `pycuda_direct_copy=False`, GPU kernels are generated and
- compiled. The compiled kernels are almost twice as fast in execution as pycuda array copying,
+ handling. Alternatively, if you set `cupy_direct_copy=False`, GPU kernels are generated and
+ compiled. The compiled kernels are almost twice as fast in execution as cupy array copying,
but especially for large stencils like D3Q27, their compilation can take up to 20 seconds.
Choose your weapon depending on your use case.
"""
@@ -40,7 +40,7 @@ class LBMPeriodicityHandling:
self.inplace_pattern = is_inplace(streaming_pattern)
self.cpu = self.target == Target.CPU
- self.pycuda_direct_copy = self.target == Target.GPU and pycuda_direct_copy
+ self.cupy_direct_copy = self.target == Target.GPU and cupy_direct_copy
def is_copy_direction(direction):
s = 0
@@ -63,7 +63,7 @@ class LBMPeriodicityHandling:
ghost_layers=ghost_layers)
self.comm_slices.append(list(chain.from_iterable(v for k, v in slices_per_comm_dir.items())))
- if self.target == Target.GPU and not pycuda_direct_copy:
+ if self.target == Target.GPU and not cupy_direct_copy:
self.device_copy_kernels = list()
for timestep in timesteps:
self.device_copy_kernels.append(self._compile_copy_kernels(timestep))
@@ -90,7 +90,7 @@ class LBMPeriodicityHandling:
def _periodicity_handling_gpu(self, prev_timestep):
arr = self.dh.gpu_arrays[self.pdf_field_name]
- if self.pycuda_direct_copy:
+ if self.cupy_direct_copy:
for src, dst in self.comm_slices[prev_timestep.idx]:
arr[dst] = arr[src]
else:
diff --git a/lbmpy/max_domain_size_info.py b/lbmpy/max_domain_size_info.py
index 65fa50f97a7401dd37e2d38c037871817e796f56..fcd2ed0984cdcb54600bc63c1d29651a28f3357c 100644
--- a/lbmpy/max_domain_size_info.py
+++ b/lbmpy/max_domain_size_info.py
@@ -26,16 +26,19 @@ Examples:
import warnings
import numpy as np
+import pystencils
-# Optional packages cpuinfo, pycuda and psutil for hardware queries
+# Optional packages cpuinfo, cupy and psutil for hardware queries
try:
from cpuinfo import get_cpu_info
except ImportError:
get_cpu_info = None
try:
- from pycuda.autoinit import device
+ import cupy
+ device = cupy.cuda.Device(pystencils.GPU_DEVICE)
except ImportError:
+ cupy = None
device = None
try:
@@ -114,7 +117,7 @@ def memory_sizes_of_current_machine():
result['L3'] = cpu_info['l3_cache_size']
if device:
- size = device.total_memory() / (1024 * 1024)
+ size = device.mem_info[1] / (1024 * 1024)
result['GPU'] = "{0:.0f} MB".format(size)
if virtual_memory:
@@ -124,7 +127,7 @@ def memory_sizes_of_current_machine():
if not result:
warnings.warn("Couldn't query for any local memory size."
- "Install py-cpuinfo to get cache sizes, psutil for RAM size and pycuda for GPU memory size.")
+ "Install py-cpuinfo to get cache sizes, psutil for RAM size and cupy for GPU memory size.")
return result
diff --git a/lbmpy_tests/advanced_streaming/test_fully_periodic_flow.py b/lbmpy_tests/advanced_streaming/test_fully_periodic_flow.py
index 0c37cfc37c52506d1a373815fa9d1d849bf03cd5..413aaa07320391093bd7c202cc023251aa2f9b67 100644
--- a/lbmpy_tests/advanced_streaming/test_fully_periodic_flow.py
+++ b/lbmpy_tests/advanced_streaming/test_fully_periodic_flow.py
@@ -20,7 +20,7 @@ all_results = dict()
targets = [Target.CPU]
try:
- import pycuda.autoinit
+ import cupy
targets += [Target.GPU]
except Exception:
pass
diff --git a/lbmpy_tests/advanced_streaming/test_periodic_pipe_with_force.py b/lbmpy_tests/advanced_streaming/test_periodic_pipe_with_force.py
index 42b6671a35f61167e63cba0171b12e08dd022b7b..2dc18be870be2a38843b9d673e8e32da4957f5c2 100644
--- a/lbmpy_tests/advanced_streaming/test_periodic_pipe_with_force.py
+++ b/lbmpy_tests/advanced_streaming/test_periodic_pipe_with_force.py
@@ -21,7 +21,7 @@ all_results = dict()
targets = [Target.CPU]
try:
- import pycuda.autoinit
+ import cupy
targets += [Target.GPU]
except Exception:
pass
diff --git a/lbmpy_tests/cumulantmethod/test_flow_around_sphere.py b/lbmpy_tests/cumulantmethod/test_flow_around_sphere.py
index 9be652aa70386ad68030ce34fec43d92d5fafb43..6db3fa9fbd3c91127b305cfda4864464763b2d64 100644
--- a/lbmpy_tests/cumulantmethod/test_flow_around_sphere.py
+++ b/lbmpy_tests/cumulantmethod/test_flow_around_sphere.py
@@ -136,7 +136,7 @@ def flow_around_sphere(stencil, galilean_correction, L_LU, total_steps):
@pytest.mark.parametrize('stencil', [Stencil.D2Q9, Stencil.D3Q19, Stencil.D3Q27])
@pytest.mark.parametrize('galilean_correction', [False, True])
def test_flow_around_sphere_short(stencil, galilean_correction):
- pytest.importorskip('pycuda')
+ pytest.importorskip('cupy')
flow_around_sphere(LBStencil(stencil), galilean_correction, 5, 200)
@@ -144,5 +144,5 @@ def test_flow_around_sphere_short(stencil, galilean_correction):
@pytest.mark.parametrize('galilean_correction', [False, True])
@pytest.mark.longrun
def test_flow_around_sphere_long(stencil, galilean_correction):
- pytest.importorskip('pycuda')
+ pytest.importorskip('cupy')
flow_around_sphere(LBStencil(stencil), galilean_correction, 20, 3000)
diff --git a/lbmpy_tests/cumulantmethod/test_periodic_pipe_flow.ipynb b/lbmpy_tests/cumulantmethod/test_periodic_pipe_flow.ipynb
index b984e24d80049db2d4357c23bc9d2e4974120972..501abf4ceeb695021beea82452c46ed571183f77 100644
--- a/lbmpy_tests/cumulantmethod/test_periodic_pipe_flow.ipynb
+++ b/lbmpy_tests/cumulantmethod/test_periodic_pipe_flow.ipynb
@@ -33,25 +33,16 @@
"cell_type": "code",
"execution_count": 3,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "No pycuda installed\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"try:\n",
- " import pycuda\n",
- " import pycuda.gpuarray as gpuarray\n",
+ " import cupy\n",
"except ImportError:\n",
- " pycuda = None\n",
+ " cupy = None\n",
" target = ps.Target.CPU\n",
- " print('No pycuda installed')\n",
+ " print('No cupy installed')\n",
"\n",
- "if pycuda:\n",
+ "if cupy:\n",
" target = ps.Target.GPU"
]
},
@@ -238,7 +229,28 @@
"cell_type": "code",
"execution_count": 6,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "AttributeError",
+ "evalue": "'tuple' object has no attribute 'items'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[6], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m srt_config \u001b[38;5;241m=\u001b[39m LBMConfig(stencil\u001b[38;5;241m=\u001b[39mstencil, method\u001b[38;5;241m=\u001b[39mMethod\u001b[38;5;241m.\u001b[39mSRT, relaxation_rate\u001b[38;5;241m=\u001b[39mviscous_rr,\n\u001b[1;32m 2\u001b[0m force_model\u001b[38;5;241m=\u001b[39mForceModel\u001b[38;5;241m.\u001b[39mSIMPLE, force\u001b[38;5;241m=\u001b[39mforce, streaming_pattern\u001b[38;5;241m=\u001b[39mstreaming_pattern)\n\u001b[0;32m----> 4\u001b[0m srt_flow \u001b[38;5;241m=\u001b[39m \u001b[43mPeriodicPipeFlow\u001b[49m\u001b[43m(\u001b[49m\u001b[43msrt_config\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mLBMOptimisation\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m srt_flow\u001b[38;5;241m.\u001b[39minit()\n\u001b[1;32m 6\u001b[0m srt_flow\u001b[38;5;241m.\u001b[39mrun(\u001b[38;5;241m400\u001b[39m)\n",
+ "Cell \u001b[0;32mIn[4], line 47\u001b[0m, in \u001b[0;36mPeriodicPipeFlow.__init__\u001b[0;34m(self, lbm_config, lbm_optimisation, config)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimesteps:\n\u001b[1;32m 46\u001b[0m lbm_config \u001b[38;5;241m=\u001b[39m replace(lbm_config, timestep\u001b[38;5;241m=\u001b[39mt, collision_rule\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlb_collision)\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlb_kernels\u001b[38;5;241m.\u001b[39mappend(\u001b[43mcreate_lb_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlbm_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlbm_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 48\u001b[0m \u001b[43m \u001b[49m\u001b[43mlbm_optimisation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlbm_optimisation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 49\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 51\u001b[0m \u001b[38;5;66;03m# Macroscopic Values\u001b[39;00m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdensity \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n",
+ "File \u001b[0;32m~/pystencils/lbmpy/lbmpy/creationfunctions.py:505\u001b[0m, in \u001b[0;36mcreate_lb_function\u001b[0;34m(ast, lbm_config, lbm_optimisation, config, optimization, **kwargs)\u001b[0m\n\u001b[1;32m 502\u001b[0m ast \u001b[38;5;241m=\u001b[39m lbm_config\u001b[38;5;241m.\u001b[39mast\n\u001b[1;32m 504\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ast \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 505\u001b[0m ast \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_lb_ast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlbm_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate_rule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlbm_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlbm_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 506\u001b[0m \u001b[43m \u001b[49m\u001b[43mlbm_optimisation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlbm_optimisation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 508\u001b[0m res \u001b[38;5;241m=\u001b[39m ast\u001b[38;5;241m.\u001b[39mcompile()\n\u001b[1;32m 510\u001b[0m res\u001b[38;5;241m.\u001b[39mmethod \u001b[38;5;241m=\u001b[39m ast\u001b[38;5;241m.\u001b[39mmethod\n",
+ "File \u001b[0;32m~/pystencils/lbmpy/lbmpy/creationfunctions.py:530\u001b[0m, in \u001b[0;36mcreate_lb_ast\u001b[0;34m(update_rule, lbm_config, lbm_optimisation, config, optimization, **kwargs)\u001b[0m\n\u001b[1;32m 525\u001b[0m update_rule \u001b[38;5;241m=\u001b[39m create_lb_update_rule(lbm_config\u001b[38;5;241m.\u001b[39mcollision_rule, lbm_config\u001b[38;5;241m=\u001b[39mlbm_config,\n\u001b[1;32m 526\u001b[0m lbm_optimisation\u001b[38;5;241m=\u001b[39mlbm_optimisation, config\u001b[38;5;241m=\u001b[39mconfig)\n\u001b[1;32m 528\u001b[0m field_types \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(fa\u001b[38;5;241m.\u001b[39mfield\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;28;01mfor\u001b[39;00m fa \u001b[38;5;129;01min\u001b[39;00m update_rule\u001b[38;5;241m.\u001b[39mdefined_symbols \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fa, Field\u001b[38;5;241m.\u001b[39mAccess))\n\u001b[0;32m--> 530\u001b[0m config \u001b[38;5;241m=\u001b[39m \u001b[43mreplace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcollate_types\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfield_types\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mghost_layers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 531\u001b[0m ast \u001b[38;5;241m=\u001b[39m create_kernel(update_rule, config\u001b[38;5;241m=\u001b[39mconfig)\n\u001b[1;32m 533\u001b[0m ast\u001b[38;5;241m.\u001b[39mmethod \u001b[38;5;241m=\u001b[39m update_rule\u001b[38;5;241m.\u001b[39mmethod\n",
+ "File \u001b[0;32m/usr/lib/python3.11/dataclasses.py:1492\u001b[0m, in \u001b[0;36mreplace\u001b[0;34m(obj, **changes)\u001b[0m\n\u001b[1;32m 1485\u001b[0m changes[f\u001b[38;5;241m.\u001b[39mname] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(obj, f\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 1487\u001b[0m \u001b[38;5;66;03m# Create the new object, which calls __init__() and\u001b[39;00m\n\u001b[1;32m 1488\u001b[0m \u001b[38;5;66;03m# __post_init__() (if defined), using all of the init fields we've\u001b[39;00m\n\u001b[1;32m 1489\u001b[0m \u001b[38;5;66;03m# added and/or left in 'changes'. If there are values supplied in\u001b[39;00m\n\u001b[1;32m 1490\u001b[0m \u001b[38;5;66;03m# changes that aren't fields, this will correctly raise a\u001b[39;00m\n\u001b[1;32m 1491\u001b[0m \u001b[38;5;66;03m# TypeError.\u001b[39;00m\n\u001b[0;32m-> 1492\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__class__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mchanges\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m<string>:24\u001b[0m, in \u001b[0;36m__init__\u001b[0;34m(self, target, backend, function_name, data_type, default_number_float, default_number_int, iteration_slice, ghost_layers, cpu_openmp, cpu_vectorize_info, cpu_blocking, omp_single_loop, gpu_indexing, gpu_indexing_params, default_assignment_simplifications, cpu_prepend_optimizations, use_auto_for_assignments, index_fields, coordinate_names, allow_double_writes, skip_independence_check)\u001b[0m\n",
+ "File \u001b[0;32m~/pystencils/pystencils/pystencils/config.py:177\u001b[0m, in \u001b[0;36mCreateKernelConfig.__post_init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_type(dtype)\n\u001b[1;32m 176\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_type, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m--> 177\u001b[0m dt \u001b[38;5;241m=\u001b[39m \u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata_type\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# The copy is necessary because BasicType has sympy shinanigans\u001b[39;00m\n\u001b[1;32m 178\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_type \u001b[38;5;241m=\u001b[39m defaultdict(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mDataTypeFactory(dt))\n\u001b[1;32m 180\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_type, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_type, defaultdict):\n",
+ "File \u001b[0;32m/usr/lib/python3.11/copy.py:102\u001b[0m, in \u001b[0;36mcopy\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(rv, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n\u001b[0;32m--> 102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_reconstruct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrv\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/usr/lib/python3.11/copy.py:273\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 271\u001b[0m state \u001b[38;5;241m=\u001b[39m deepcopy(state, memo)\n\u001b[1;32m 272\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__setstate__\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 273\u001b[0m \u001b[43my\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__setstate__\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 275\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(state, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(state) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n",
+ "File \u001b[0;32m~/.local/lib/python3.11/site-packages/sympy/core/basic.py:144\u001b[0m, in \u001b[0;36mBasic.__setstate__\u001b[0;34m(self, state)\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__setstate__\u001b[39m(\u001b[38;5;28mself\u001b[39m, state):\n\u001b[0;32m--> 144\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, value \u001b[38;5;129;01min\u001b[39;00m \u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m():\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28msetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, name, value)\n",
+ "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'items'"
+ ]
+ }
+ ],
"source": [
"srt_config = LBMConfig(stencil=stencil, method=Method.SRT, relaxation_rate=viscous_rr,\n",
" force_model=ForceModel.SIMPLE, force=force, streaming_pattern=streaming_pattern)\n",
@@ -250,32 +262,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.colorbar.Colorbar at 0x127d43bb0>"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1152x432 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"srt_u = srt_flow.get_trimmed_velocity_array()\n",
"ps.plot.vector_field_magnitude(srt_u[30,:,:,:])\n",
@@ -291,7 +280,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -306,7 +295,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -317,7 +306,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -327,32 +316,9 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.colorbar.Colorbar at 0x127d1df10>"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1152x432 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"cm_impl_f_u = cm_impl_f_flow.get_trimmed_velocity_array()\n",
"ps.plot.vector_field_magnitude(cm_impl_f_u[30,:,:,:])\n",
@@ -361,7 +327,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -377,7 +343,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -394,7 +360,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -405,7 +371,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -415,32 +381,9 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.colorbar.Colorbar at 0x1272c5760>"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1152x432 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"cm_expl_f_u = cm_expl_f_flow.get_trimmed_velocity_array()\n",
"ps.plot.vector_field_magnitude(cm_expl_f_u[30,:,:,:])\n",
@@ -449,7 +392,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -474,7 +417,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.9"
+ "version": "3.11.0rc1"
},
"vscode": {
"interpreter": {
diff --git a/lbmpy_tests/full_scenarios/phasefield_allen_cahn/phasefield-capillary-wave.ipynb b/lbmpy_tests/full_scenarios/phasefield_allen_cahn/phasefield-capillary-wave.ipynb
index e8339d83477a4f7b2a953d945bcb89c24b0700e7..bbfa78d9843a65ca964b61614a820f66f16bf39a 100644
--- a/lbmpy_tests/full_scenarios/phasefield_allen_cahn/phasefield-capillary-wave.ipynb
+++ b/lbmpy_tests/full_scenarios/phasefield_allen_cahn/phasefield-capillary-wave.ipynb
@@ -33,32 +33,24 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "If `pycuda` is installed the simulation automatically runs on GPU"
+ "If `cupy` is installed the simulation automatically runs on GPU"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "No pycuda installed\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"try:\n",
- " import pycuda\n",
+ " import cupy\n",
"except ImportError:\n",
- " pycuda = None\n",
+ " cupy = None\n",
" gpu = False\n",
" target = ps.Target.CPU\n",
- " print('No pycuda installed')\n",
+ " print('No cupy installed')\n",
"\n",
- "if pycuda:\n",
+ "if cupy:\n",
" gpu = True\n",
" target = ps.Target.GPU"
]
@@ -1123,7 +1115,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.9"
+ "version": "3.11.0rc1"
}
},
"nbformat": 4,
diff --git a/lbmpy_tests/full_scenarios/phasefield_allen_cahn/phasefield-gravity-wave.ipynb b/lbmpy_tests/full_scenarios/phasefield_allen_cahn/phasefield-gravity-wave.ipynb
index 451c8872115a524fa05f3df49a0025267c3aea76..c7fa30260906c06776a6fbcea8846a4489d3cf01 100644
--- a/lbmpy_tests/full_scenarios/phasefield_allen_cahn/phasefield-gravity-wave.ipynb
+++ b/lbmpy_tests/full_scenarios/phasefield_allen_cahn/phasefield-gravity-wave.ipynb
@@ -32,32 +32,24 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "If `pycuda` is installed the simulation automatically runs on GPU"
+ "If `cupy` is installed the simulation automatically runs on GPU"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "No pycuda installed\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"try:\n",
- " import pycuda\n",
+ " import cupy\n",
"except ImportError:\n",
- " pycuda = None\n",
+ " cupy = None\n",
" gpu = False\n",
" target = ps.Target.CPU\n",
- " print('No pycuda installed')\n",
+ " print('No cupy installed')\n",
"\n",
- "if pycuda:\n",
+ "if cupy:\n",
" gpu = True\n",
" target = ps.Target.GPU"
]
@@ -1208,7 +1200,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.9"
+ "version": "3.11.0rc1"
}
},
"nbformat": 4,
diff --git a/lbmpy_tests/full_scenarios/shear_wave/scenario_shear_wave.py b/lbmpy_tests/full_scenarios/shear_wave/scenario_shear_wave.py
index 9005e9687e9e6bf57d88f91557597812ad105036..cd5e73b7953216c3dd22c79490ecf29c178dc944 100644
--- a/lbmpy_tests/full_scenarios/shear_wave/scenario_shear_wave.py
+++ b/lbmpy_tests/full_scenarios/shear_wave/scenario_shear_wave.py
@@ -215,7 +215,7 @@ def create_full_parameter_study():
def test_shear_wave():
- pytest.importorskip('pycuda')
+ pytest.importorskip('cupy')
params = {
'l_0': 32,
'u_0': 0.096,
diff --git a/lbmpy_tests/phasefield/test_n_phase_boyer_analytical.ipynb b/lbmpy_tests/phasefield/test_n_phase_boyer_analytical.ipynb
index bfb151700a55e22373bf0e1088eed44d46c628d9..36b2e7dfd24afdcdcb97ba1d50cbd2a9d5a77ed4 100644
--- a/lbmpy_tests/phasefield/test_n_phase_boyer_analytical.ipynb
+++ b/lbmpy_tests/phasefield/test_n_phase_boyer_analytical.ipynb
@@ -415,7 +415,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -429,7 +429,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.2"
+ "version": "3.11.0rc1"
}
},
"nbformat": 4,
diff --git a/lbmpy_tests/phasefield/test_n_phase_boyer_noncoupled.ipynb b/lbmpy_tests/phasefield/test_n_phase_boyer_noncoupled.ipynb
index 6958e868e7fc0dfae36db8d5cc67a58a19cb98fb..4cb099e864b12e962d1badd898e25e38c32390ae 100644
--- a/lbmpy_tests/phasefield/test_n_phase_boyer_noncoupled.ipynb
+++ b/lbmpy_tests/phasefield/test_n_phase_boyer_noncoupled.ipynb
@@ -6,21 +6,19 @@
"metadata": {},
"outputs": [
{
- "ename": "Skipped",
- "evalue": "could not import 'pycuda': No module named 'pycuda'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mSkipped\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m/var/folders/07/0d7kq8fd0sx24cs53zz90_qc0000gp/T/ipykernel_16968/622163826.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpytest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpytest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimportorskip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pycuda'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/_pytest/outcomes.py\u001b[0m in \u001b[0;36mimportorskip\u001b[0;34m(modname, minversion, reason)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"could not import {modname!r}: {exc}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mSkipped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_module_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 213\u001b[0m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mminversion\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mSkipped\u001b[0m: could not import 'pycuda': No module named 'pycuda'"
- ]
+ "data": {
+ "text/plain": [
+ "<module 'cupy' from '/home/markus/.local/lib/python3.11/site-packages/cupy/__init__.py'>"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
"import pytest\n",
- "pytest.importorskip('pycuda')"
+ "pytest.importorskip('cupy')"
]
},
{
@@ -315,7 +313,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.9"
+ "version": "3.11.0rc1"
}
},
"nbformat": 4,
diff --git a/lbmpy_tests/phasefield/test_numerical_1D_nphase_model.ipynb b/lbmpy_tests/phasefield/test_numerical_1D_nphase_model.ipynb
index 695b4e8414d1c367e410678fe0e213cc59bf3d62..3f46dd27315ceaef81da34e8a6442abf4c573d6b 100644
--- a/lbmpy_tests/phasefield/test_numerical_1D_nphase_model.ipynb
+++ b/lbmpy_tests/phasefield/test_numerical_1D_nphase_model.ipynb
@@ -8,7 +8,7 @@
{
"data": {
"text/plain": [
- "<module 'pycuda' from '/home/markus/miniconda3/envs/pystencils/lib/python3.8/site-packages/pycuda/__init__.py'>"
+ "<module 'cupy' from '/home/markus/.local/lib/python3.11/site-packages/cupy/__init__.py'>"
]
},
"execution_count": 1,
@@ -18,7 +18,7 @@
],
"source": [
"import pytest\n",
- "pytest.importorskip('pycuda')"
+ "pytest.importorskip('cupy')"
]
},
{
@@ -435,7 +435,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -449,7 +449,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.2"
+ "version": "3.11.0rc1"
}
},
"nbformat": 4,
diff --git a/lbmpy_tests/test_boundary_handling.py b/lbmpy_tests/test_boundary_handling.py
index 23900e0c645b9b72cc906975ee224009ca491fe8..e174adf54745b5f9d170a0d92840011ae88309ca 100644
--- a/lbmpy_tests/test_boundary_handling.py
+++ b/lbmpy_tests/test_boundary_handling.py
@@ -26,7 +26,7 @@ def mirror_stencil(direction, mirror_axis):
def test_simple(target):
if target == Target.GPU:
import pytest
- pytest.importorskip('pycuda')
+ pytest.importorskip('cupy')
dh = create_data_handling((4, 4), parallel=False, default_target=target)
dh.add_array('pdfs', values_per_cell=9, cpu=True, gpu=target != Target.CPU)
diff --git a/lbmpy_tests/test_cpu_gpu_equivalence.py b/lbmpy_tests/test_cpu_gpu_equivalence.py
index 7683153ba871c804c68385e187884ad473614d56..f519d3f5746582da49e2406eb9ff31848d05be8c 100644
--- a/lbmpy_tests/test_cpu_gpu_equivalence.py
+++ b/lbmpy_tests/test_cpu_gpu_equivalence.py
@@ -31,7 +31,7 @@ def run_equivalence_test(domain_size, lbm_config, lbm_opt, config, time_steps=13
((18, 20), Method.MRT, True, (4, 2), 'zyxf'),
((7, 11, 18), Method.TRT, False, False, 'numpy')])
def test_force_driven_channel_short(scenario):
- pytest.importorskip("pycuda")
+ pytest.importorskip("cupy")
ds = scenario[0]
method = scenario[1]
compressible = scenario[2]
diff --git a/lbmpy_tests/test_diffusion.py b/lbmpy_tests/test_diffusion.py
index 646531241f1fbece7274b552dda3917ced6e518f..4fce37d1ae79c7becb63827c91868497d962f4b3 100644
--- a/lbmpy_tests/test_diffusion.py
+++ b/lbmpy_tests/test_diffusion.py
@@ -77,7 +77,7 @@ def test_diffusion():
The hydrodynamic field is not simulated, instead a constant velocity is assumed.
"""
- pytest.importorskip("pycuda")
+ pytest.importorskip("cupy")
# Parameters
domain_size = (1600, 160)
omega = 1.38
diff --git a/lbmpy_tests/test_gpu_block_size_limiting.py b/lbmpy_tests/test_gpu_block_size_limiting.py
index e619b53b8b0bcfd2c8f6ce49183c5be7f64da7d9..f3bfc805e65cdb3f3df0ea34aff7aaaa4a86c3b6 100644
--- a/lbmpy_tests/test_gpu_block_size_limiting.py
+++ b/lbmpy_tests/test_gpu_block_size_limiting.py
@@ -6,7 +6,7 @@ from pystencils import Target, CreateKernelConfig
def test_gpu_block_size_limiting():
- pytest.importorskip("pycuda")
+ pytest.importorskip("cupy")
too_large = 2048*2048
lbm_config = LBMConfig(method=Method.CUMULANT, stencil=LBStencil(Stencil.D3Q19),
relaxation_rate=1.8, compressible=True)
diff --git a/lbmpy_tests/test_lbstep.py b/lbmpy_tests/test_lbstep.py
index f5184b37a64f039bd75d278e967d4d6e7c63ece8..629d106e3379a9c0996374f47bf46d5d817d569e 100644
--- a/lbmpy_tests/test_lbstep.py
+++ b/lbmpy_tests/test_lbstep.py
@@ -6,7 +6,7 @@ from pystencils import Target, CreateKernelConfig
from lbmpy.scenarios import create_fully_periodic_flow, create_lid_driven_cavity
try:
- import pycuda.driver
+ import cupy
gpu_available = True
except ImportError:
gpu_available = False
diff --git a/lbmpy_tests/test_poisuille_channel.py b/lbmpy_tests/test_poisuille_channel.py
index e0dec156bad1aafb8158ccb84f14846ed31cba44..264d75ff2d0af94f3326fd7636fff75732dc491d 100644
--- a/lbmpy_tests/test_poisuille_channel.py
+++ b/lbmpy_tests/test_poisuille_channel.py
@@ -18,7 +18,7 @@ def test_poiseuille_channel(target, stencil_name, zero_centered, moment_space_co
# Cuda
if target == ps.Target.GPU:
import pytest
- pytest.importorskip("pycuda")
+ pytest.importorskip("cupy")
cspace_info = CollisionSpace.RAW_MOMENTS if moment_space_collision else CollisionSpace.POPULATIONS
poiseuille_channel(target=target, stencil_name=stencil_name, zero_centered=zero_centered, collision_space_info=cspace_info)
diff --git a/lbmpy_tests/test_shear_flow.py b/lbmpy_tests/test_shear_flow.py
index 1ab7ca2137893aadd2eac5c4732d4cb80da2d890..9b22d0a5b0a8cd36ce53b0e32c649428959ecf9a 100644
--- a/lbmpy_tests/test_shear_flow.py
+++ b/lbmpy_tests/test_shear_flow.py
@@ -67,7 +67,7 @@ def test_shear_flow(target, stencil_name, zero_centered):
# Cuda
if target == ps.Target.GPU:
- pytest.importorskip("pycuda")
+ pytest.importorskip("cupy")
# LB parameters
stencil = LBStencil(stencil_name)
diff --git a/lbmpy_tests/test_simple_equilibrium_conservation.py b/lbmpy_tests/test_simple_equilibrium_conservation.py
index 0c39825cd195810aab5b10bb8df8ea770926576e..d5692e7d06b42e4173edc6c3c3231be06182f960 100644
--- a/lbmpy_tests/test_simple_equilibrium_conservation.py
+++ b/lbmpy_tests/test_simple_equilibrium_conservation.py
@@ -13,7 +13,7 @@ import pytest
@pytest.mark.parametrize('delta_equilibrium', [False, True])
def test_simple_equilibrium_conservation(setup, method, compressible, delta_equilibrium):
if setup[0] == Target.GPU:
- pytest.importorskip("pycuda")
+ pytest.importorskip("cupy")
if method == Method.SRT and not delta_equilibrium:
pytest.skip()
@@ -30,11 +30,11 @@ def test_simple_equilibrium_conservation(setup, method, compressible, delta_equi
func = create_lb_function(lbm_config=lbm_config, config=config)
if setup[0] == Target.GPU:
- import pycuda.gpuarray as gpuarray
- gpu_src, gpu_dst = gpuarray.to_gpu(src), gpuarray.to_gpu(dst)
+ import cupy
+ gpu_src, gpu_dst = cupy.asarray(src), cupy.asarray(dst)
func(src=gpu_src, dst=gpu_dst)
- gpu_src.get(src)
- gpu_dst.get(dst)
+ src[:] = gpu_src.get()
+ dst[:] = gpu_dst.get()
else:
func(src=src, dst=dst)
diff --git a/lbmpy_tests/test_sparse_lbm.ipynb b/lbmpy_tests/test_sparse_lbm.ipynb
index da2914ba88f9cd90e70a22f47885c3ebbb7283f4..00af223b14b729e99c9eebd6b6f3d794f2428f22 100644
--- a/lbmpy_tests/test_sparse_lbm.ipynb
+++ b/lbmpy_tests/test_sparse_lbm.ipynb
@@ -17,25 +17,16 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "No pycuda installed\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"try:\n",
- " import pycuda\n",
- " import pycuda.gpuarray as gpuarray\n",
+ " import cupy\n",
"except ImportError:\n",
- " pycuda = None\n",
+ " cupy = None\n",
" target = ps.Target.CPU\n",
- " print('No pycuda installed')\n",
+ " print('No cupy installed')\n",
"\n",
- "if pycuda:\n",
+ "if cupy:\n",
" target = ps.Target.GPU"
]
},
@@ -79,14 +70,12 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
- "<Figure size 1152x432 with 2 Axes>"
+ "<Figure size 1600x600 with 2 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -168,16 +157,7 @@
"cell_type": "code",
"execution_count": 7,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING:root:Using Nodes is experimental and not fully tested. Double check your generated code!\n",
- "WARNING:root:Using Nodes is experimental and not fully tested. Double check your generated code!\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"cqc = method.conserved_quantity_computation\n",
"inp_eqs = cqc.equilibrium_input_equations_from_init_values(force_substitution=False)\n",
@@ -221,13 +201,13 @@
"outputs": [
{
"data": {
- "image/png": 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zpuuHkE+wt/CEcK1s5c+4yH878d9g032r/EhX+Sy1/7PXJ2O6Z/dLK5haUnHVtNfKrGdnrX/uXA7orn4Qd+hqOEXn9Qv9oAhkwf0X3ZfukKITekfhU+UFac0UPwqDoKZz28lPF+dJT28Z9HzRKNzrxYfzB8WY5tGauooxqZwcjwR/GDq6YlPBEWkWYyoGcFnAGjwoOabP83VPveVIKM6W3jT3xJPIYMAc2qdsPt35QddvNklSgAqeamRXkvUeYc7CNHJU/CebFRxRprMw7UHGQho1eJgM0YdTh1Aw8mfkab8v77y5I54eVFb6Il448pI7HHdXxJXC3+o4V1uXGOPCOBcqDRxN/XrwTO0KjohagyvNqoW7GI/KSVujnQ0KfdnMB2Y7M9YA3jhPadGGPlllmr0AkrvXvDLFgzuLKRewo18Wk3jCP7S3qnllwF5Zn0I07CymOEDLe+Fm7GJXE+2I+6lN6b6VWSrzRXNXwN6qHEbs8M98fVh44tfYLMpB+faYG+eKt4QpF3Z3P9Uf+gH6AGTBmuk7+Z28OJX/nmazzMLAeDeEK834grZAFpuYSnGcE07YyT+cSoBSBt3LbHw7J92NOEtlpi70mt+nEJcwpeGausX9pBBrmtFj4otlFo4e64e0iIt40oCN3C+VLvWf5OmLevyP2lKZaQv0kWG+pNvBFK1PCKlylK7jHlN++7uE6W2IxnfCzlyJzaAcM0RZitauhD3TZMusfC9a/4BFadykHISbdR7zpB9ryzCG50Qd+vP/yf7whW6Y/ELG+/JAaV9CDI1yNjEgrvxoIGGnp24Hw8J2+ipA4ThPHwX/RZ/DjGnHVg2mB+UPLkzVVwWPUYp/izEJDwM8iiEaWUtTjAkQHXiqwfOl8MSTUmSI3we69p4g1eB6UP6t61MNnm+SCsQu0F8Tvz2cNZh6cESZqjARQbJjQB+UabgbmCpMB6tL0PGJMO3dvnI03yxPo8wYlAeesHUVvcTOEbHid7McqUwoVp7FZZObyf2mcj+OU3h/yzxRb6hHQcHPzqB3C8tdG+xoPKX4WXDk+uZe88oUD+4lTA+SU+t5QA7PIibhoR71mFfmcC3ylAvcwI/FEmPXsKaRjQIFhVpLs1TmnnPXtHxLmNJwTdzinYU8a0OuXmapzL3mxrlyLmHKhW3hx9pp2OQjm/X6/3TNxuMGma6WWThaz71zRVrFlIuwsx9jBLuFQ7/8rdzX6pcoWq/5PXnF5tpyeJAMUIZdvV8SDupEj/VDzD/3R5BB6z6otMw91yelmNJwTdxjO0AHTL/E2MDcqbWCf6nu9Vz/pHwuYUrDtXIzVxp06JJDyKP4P20VB/0xJ+QM48sLebB4QutPB/Nf2audvZ4jfK5Ukc9C8UHPJ8W/7uk4CZsu0F7Lj4Kk/vKqNzWYSF3hL9r9VYKwFlNJmpeGqcXUmqdaPCo/iqAfL+VhK34trqPxJDypMpZGHxQPW8Uven40jgBdiymKQ1/UZDCrxaTwTfumWjxwhFE8FmL05bygpf/e1dTiUvij8QQeBmLGPOoTPE1j4R5kPQGOZn2QyoO76Li+Gv6eAE/MrdgE8WddTNSoW+zq2NUcjaeFwtGWZuOZcHeZVy7gwfsEE56jnM7+WpU0LjBZTBekt0fULCbxhD9fFFK3s+Ou/Blr/qsLm/9hqHoJOMZnE1E8VuEu3cSkbM8y2TIrpS5z1wXEWUziorkcwKN8wkumWf+/gHUv76Uyz/oSZbb73HilAEuYushBuGIlMn1o3DZWYF/0KFtmUlS9oG3zPNsHEKaRyWISnl5yGL5iUX7I4Pe6dh/bM7xlyxzCCUvT+X3IJ7GzmHrJQfnQ9pmj7z4HTcoZO7NlVoDm64cYRHSfxdNLBuBQXvQDH+lCwb6LTpB0V0y2zAo/G5+Epcn6ZAFXFpMw4N90rjTioazogYcXrrJbbE5Li54tswJ1Wf+kYEZ3FlNHOUwn6Yx5MmednYqzgPtB4ZAh/cikw38eAushnRydHZWpxKQNMbx1IaPPdIVFWC6tX+X5bu7BhX4lmMgCIibli7By/+mImeeDkZuO51JTiunSfGril2LqxVMRHsmDhV48KWXRxKCQTtpruFgLW4RLCRyKp1Ag8fJKF7vW6DRaTaKPxhHFL8VE2C/FTenRZIQ/15RiOmJd+lqFBj/95Lfii4lxK3NzPIkPxgkuxkBseOJIBgbbFubmOIIE8THh1j11CCV23J/vzdWU35hwbo7Co0O1OXFCf8R8jP6bP5hlZ02r/lvJF/eXvXgCUzAs/P4VHKNNG8uZVvPKNK8cJsIU8RMSk0yXyhGC1NhLmGrS2DvsEib8KfviIlLc0C/ECslabKwvqA+xwc26ZGZ6yIH2O5Yp5N167hrywb6mHGIcq/c95BAAKK8ec+OQXbCvKgeVOR4P6as+D8Biu6Mces294+Jxf2058EUe/RDKNPqk7Ka1jnLoOb9XcSdzNTmIWxR5yCFuExOw+Ka1HMb0w3iI3Xr9EBfvajIYQVD/0b1RZvSG2TVTaxmARXlMdUH3n8mr9fqEbIO5mhxGbql3nISBHLCzLx3HsAHzpXa2zMqj9/onLkcWkwLgH9poHH66F+7XclwyZ30Yy449jA+yS5X7jCWvdPHy+OcA6nm4GW0WlexwIWDW6BmFoBIAYDDy+0w3dJhhoPqd/KbG8hhq9svkG7JWjdIAyxtdKCkXjZ4XY1JYcJN3io80KMNgFI4wnGO0mvcY/MSqwXQSucIDnLo2OSLJGkwK25ynGjwpJYqLrKh3RQ1K4e+OJ5WZjpLjsWiPi21azydTypPC3XybUxmoP0Ud6ESQbko5Io7CHoqnGjwjfnaiM2jwIo2yfKOLz/c2jcLfS5tjPMP8W2Xms2t4Yizlxdo0ThIgZ0p5GtMtGnvHfN9Rfs3GuRo8cbkpr9z8kTxlKTLE0XUX45zKydjGp6KMbfRPKKOyE249m5lb5mlWEDlUljAHmSasaZjETTukbq2aGo7ShJYwRf6r7S2kBwbdnz2/DOlgR3mX8hRHX70Hp66idhcntIZJz3hZxZwkq2CM0mHxf64hbuiXQxq4XwcHtjB0l4PypH0Xz10DTsW7RTkAf9P0loPyazY3zhVW+S32ZXrWoz1MsJQfYwlfz5yMv/Lr0h6Uz1lzbwoBRl3VbWGMe3U5CPtwPLFs5M6LFv5gGN4nM7qbjw/Kp8v8firYeKN8ryaHkduPKHuKK3WPYVvLIYxTTdcPmbJdTQYRFupfWIPTHk7muZ1kMEEa86tanxCZeLqq+yXFOYIcwD/IYbRfjrhSXlq3BXAwPzpr/fMU5DASznF6zFFKDfoGdDSzPu15HFsPWZwwAYbcNcPE/GOFZ+HJAEU8duPwb9goEkMjJa2cYTBLF0In4ZQW8Qn348nDU49STOSdM7MFQZQ3jXYpTi6d2K8UUxyn6j7CWcIRaZdiWirz3jyV4pl4UZnpEKl3fFJEHdk098gTpIzlpvGzo5iOc9VU8lQqu151ibIVYVI5qUP0V5v9UEpYJUfFmBSwF09FHAFcZWUhFpuBL/nzxnjV3AtPKmeoQ1MfPJYdfvbmqVR2h6tLUWV5qfufI/fm7b3UpZEIvpIZdmJgy4+5FRPuvftvsjtafQJTMEN5Ve7ZpFUPl8Z86nxoiyGNE1vpEZ9wU3s9CbTssYSpqL2FZCMMl8wvQ3JLmMLzs+0IZy1Xi5iUJn/ixu642YtiuVlPoOziGRtrmONNRn7s8EMJwiKa88MHM/qx8OTZIAfZQcYhGPbJEQBRuC5yUH7Vc1eARzhvSg5gLzFR+brIAUxjnq3mxmmxr9oeAhiVmTY1nM+r+5NNPyMn9I3N5KA8zp57U44IY21bIPpV5SDs9E/0f8Fkx42ojM3kAADl03N+H8qMfU05vKv84ZXxhjZAffxa99gzI78wjjSTg/IIc5apPo/5gmXv9UNcvmvKIODgGL7YwPfM9JDBLMPH//GrWp8QP8I5yTFJd8l5VTmMuNmYlvZF6PomE5WvWVsYMzt7/UP8COdNySEQLfzUh1QW4fGS/ZEehH5kCvNiuqu4GQlkYZiamZ/ChV2fdJxpg0ndaVqDW2m8n32QeJZiSqLFzqHTjz3IW1c6AMZBVu93wLSafngIznC/Ze+AaVeeavEoPHWJCerweffofpB9UrlTLhTmyfOkMtIxMGhO53DpPgyadAKb7a6UJ4Uj3VmblxuT83t8Mv/dtS6RdAUmeKJ9ByUCbuLj/pfs73EvGT1/8nVpLDtvknmRFuoNMsNstjcC3RFP8BG4oejB7MqT+DxUmzsTD8dThE0AgadN+x7qksrI+PZrTIb82OXE7qbcPCoOOtzfMk9JYRjjT/phla/bvDLBgzOLKRMueJ2MceEBctJ19vwypCO7FlMUdfu2pj5Fqa1h4suUSUFPHOWBsh8uprFGfrPP9eWmLnwvmzNaP5WN8iP0u7wQS18E4YcCZwqj+xOjMF3koHzOnrsCGpwn4Lc9ri4H4f5AMJEVbQFFWnZ+Rfl0NWsPSps5Xpe5sfJJzRHkwEszZIFiE3y0i5Ndgq3loDwvmnsDHIzYZ5irykG4X+tidzB9Gzx8rIuvmZjXzYz8mraHMbNu8/tZ4dbHrNbjA1zz/0bwPxtjEoyDs5McwhiVQsB/04BxM9BpgKu2hRHOF8JOn8/YQBk+Gf1nVicZhDzPWp8Q+YblgM6GTaC8fEQOzJ2690nK8+L1j7A/KJ1bbQ/Apy0EfQvus81ZCv7K3DjSgcndAFjEc88kvajjqsyrJPhJpR0j0dnnMH0srCeLzJKMbjzM4XiSHGj84fNSJqoYOmN24VzLHIonccQEEgVRXJdpc+D8+5VIOhRHcCCO6I+mTlRu6haTvW90v4RXj5uapXzfUa6xPAOIHn0TisU4byZj1+y/KfsReeLFEJOkQcmkOkT/dPLJnPx6mSNyFMoe+u7gvqZ9KJ5Ub6gzfFnELpkYG/0Tc6lrmRhLjOEdOeL+ITw7q29Smelffi+btvSRrqUyd5tXFmKq5acXTyGf5nYhTw8KxxxlwqN76jZyZxfZNCbL/eMUKLpRGBRiw1cA8qbuoSRIlfsP8uPZiQIzSiq+bVpfhaXb3FV5FbUhheslhzDXKpFFMzmM5f1VQo/7q2Zz4wPKoeg407FRtJRDqA9DVuKp6dz7gHJgTl1qmslhBNBtfn9AOTBm0yddtV8a5dBl/XBAGdTo1lq3hVEUw0vQcN/EPqAcGBPRpZWYZnIQL13XP0eTA+QLE3PZk/lsgWBO1iAvCiJdFERgeVPKmyE6sF908Wbli4sSvSCycBTv/lJYFq9gbm6UF5McGhgTTs7oG/iSXTL47I5P+R6RJ3agIBPsyQhracc0xdnr5qA8oRxhp1Soux/LXXyc0V7chHQOylGA9yB8KPbZzYChr+KIgJqJx2PMC3+V5xHbHC/PQl16jyIKJ/XrauaIPAkTf6LIzrh4nKtZUO/K5xE5igrIZDKrvIvCdLk9KE+0r7CbBh5od3y1djKB42EP05En2g/9IOP8g+xsPyz/nvPKTUzCU9x3B3kpziXzy01MI38955VFmEL5IxuMGJSvpYbxGiU/u53OWQxN+fSQgzLrOXe1HBb6Dcmh59zYcliWw9D+1PZ6zL0th2U59JzfWw4LclA76LV+sAwWZDBNCB5fALden1gOy3Louf65STlEdXX1trmCn9zVeV1zh3WOgNLdX18KexcFu/JB6WGectIa/cTRb1YeX/PRoeqTeJrtkLkmMVHeh+IowvUgvtjFUrOrJo6+9/2heBI3KBWP1i/B+aF4ApC46jJWkFehORxH4BZP53w+WVjks4IdiifxU7Ob5qwCnxmpB09MuFF8Y6++SBRPvfqlUkyl/AT6L5lfFmEa69LReArlDzb1HRMU/Y+u9V++5OTlD7vVf9B1ycvUHnLoOXctqhviLDX3IIeec2PLIa1hiVv9U4+5t+WQ8B6c4r/n/N5yCMRnbMmix/rBMshwH3tJDj3WJ5ZDTHp0LxuFmWYAAAPPSURBVP57rn9uVQ4RY8u3z968eTN7KnI5G3P4Z/XZgyfmUDkRLIZdzjToq+6QA8gRjXkqk4p52ubJHG1zRAjzZJ7KGNgO5bq0zREhzJN5KmNgPZTr0To/4al4YuHBUW/DSxzZ4Qx+zp0eFM2yUeKzw3cKR/wxLPbnuvjilTP5WbOEr/AIxnNeGPACgPS4v/bRcoJwLCOOLIcDiMRyOIAQBMFyuA85SM4eGzZE7bawQVCnx5ZDJ6I3sjmyHIQtq7e/WwX/hiz92AyYATNgBsyAGTADZsAMmIGdGNBihONq+B8Odo/ynzfDzkXZbLpBYR+O6kExj4IfQxiO5OFrBJQzbMgJf5QY/rCX8PgPxzrJJp9PZJPPA25dl+z0J5knY+BDhbEcrixRy+HKAhiztxzuRw6jrD02LIjcbWGBmM7elkNnwheyO7ochM8K/gXZ2dsMmAEzYAbMgBkwA2bADJiBG2dACx6Oefqv7OlonHER9H+yB4X/jRfxJuBbDscQk+VgORyDgeujcFu4vgxAYDlYDsdg4BgoLmkPiptV8D8/RtGMwgyYATNgBsyAGTADZsAMmAEzcBED/Plu+BIgJISb3f82/RiwHPpxvZaT5bDGTr9nlkM/rpdysgyWmOnrbzn05XspN8thiZm+/rvLIafgZxLM7hcbM2AGzIAZMANmwAyYATNgBszArTDAOoYFU2xwe/d+zEj7e8uhPcclOVgOJSy1D2M5tOd4KwfLYIuhPs8thz48b+ViOWwx1Of57nLIKfiZAKcT4z7Fcy5mwAyYATNgBsyAGTADZsAMmIEzGNAny6xjhj/rjaL/R/6pX/TYt3szYDnszeh56VkO5/G2dyzLYW9G69OzDOo5axHDcmjBan2alkM9Zy1inCsHxWNDPhcvCGbmxcz16PinrFeK9FtdngxnCLKXGTADZsAMmAEzYAbMgBkwA4dk4HOtYV4JGesYNi19fkiUTx+U5XAMGVsOlsMxGLg+CreF68sABJaD5XAMBo6B4pz28PUI/U9pEZ69efNm5je+Dfhp9PxQbnbC2JgBM2AGzIAZMANmwAyYATNgBsyAGTADZsAMmAEzYAbMgBkwAx0ZGDewoNjnxcD3adYnCn4CKCDb/b/T9ZGur+T+q2wbM2AGzIAZMANmwAyYATNgBsyAGTADZsAMmAEzYAbMgBkwA2agMQPSyX+gLNDRY1Du//x4O//NKvhDEEX6TPe/1/XNUgIhrG0zYAbMgBkwA2bADJgBM2AGzIAZMANmwAyYATNgBsyAGTADZuByBkYF/7uyOVJ/0fw/0Z+X3H99hH4AAAAASUVORK5CYII=\n",
+ "image/png": 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",
"text/latex": [
"$\\displaystyle \\left\\{ d_{0} : {d}_{0}^{0}, \\ d_{1} : {d}_{0}^{1}, \\ d_{2} : {d}_{0}^{2}, \\ d_{3} : {d}_{0}^{3}, \\ d_{4} : {d}_{0}^{4}, \\ d_{5} : {d}_{0}^{5}, \\ d_{6} : {d}_{0}^{6}, \\ d_{7} : {d}_{0}^{7}, \\ d_{8} : {d}_{0}^{8}, \\ f_{0} : {f}_{0}^{0}, \\ f_{1} : {f}_{\\mathbf{idx}_{0}^{0}}^{0}, \\ f_{2} : {f}_{\\mathbf{idx}_{0}^{1}}^{0}, \\ f_{3} : {f}_{\\mathbf{idx}_{0}^{2}}^{0}, \\ f_{4} : {f}_{\\mathbf{idx}_{0}^{3}}^{0}, \\ f_{5} : {f}_{\\mathbf{idx}_{0}^{4}}^{0}, \\ f_{6} : {f}_{\\mathbf{idx}_{0}^{5}}^{0}, \\ f_{7} : {f}_{\\mathbf{idx}_{0}^{6}}^{0}, \\ f_{8} : {f}_{\\mathbf{idx}_{0}^{7}}^{0}\\right\\}$"
],
"text/plain": [
- "{d₀: d_C__0, d₁: d_C__1, d₂: d_C__2, d₃: d_C__3, d₄: d_C__4, d₅: d_C__5, d₆: d_C__6, d₇: d_C__7, d₈: d_C__8, f₀: f_C__0, f₁: f_26b74c363b15, f₂: f_e111196926c6, f₃: f_e3f72afe7d66, f₄: f_0b929cb3f1da, f₅: f_3f75acc2de6d, \n",
- "f₆: f_3e20adce708c, f₇: f_3a33f411da6b, f₈: f_68da5b60e7d8}"
+ "{d₀: d_C__0, d₁: d_C__1, d₂: d_C__2, d₃: d_C__3, d₄: d_C__4, d₅: d_C__5, d₆: d_C__6, d₇: d_C__7, d₈: d_C__8, f₀: f_C__0, f₁: f_9d0b5fd58d5c, f₂: f_c7175adc2ede, f₃: f_f2d66aa0a9c\n",
+ "3, f₄: f_de2a090a38e9, f₅: f_4eafba53d499, f₆: f_92d378c6bf21, f₇: f_bc4af68ef6a9, f₈: f_13012fb559c4}"
]
},
"execution_count": 8,
@@ -255,15 +235,7 @@
"cell_type": "code",
"execution_count": 9,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING:root:Using Nodes is experimental and not fully tested. Double check your generated code!\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"collision_rule = method.get_collision_rule()\n",
"update_rule = collision_rule.new_with_substitutions(symbol_subs)\n",
@@ -320,17 +292,17 @@
" global pdf_arr, pdf_arr_tmp, index_arr\n",
" handle_ubb()\n",
" if target == ps.Target.GPU:\n",
- " gpu_pdf_arr = gpuarray.to_gpu(pdf_arr)\n",
- " gpu_pdf_arr_tmp = gpuarray.to_gpu(pdf_arr_tmp)\n",
- " gpu_index_arr = gpuarray.to_gpu(index_arr)\n",
+ " gpu_pdf_arr = cupy.asarray(pdf_arr)\n",
+ " gpu_pdf_arr_tmp = cupy.asarray(pdf_arr_tmp)\n",
+ " gpu_index_arr = cupy.asarray(index_arr)\n",
" \n",
" kernel_stream_collide(f=gpu_pdf_arr[:mapping.num_fluid_cells], \n",
" d=gpu_pdf_arr_tmp[:mapping.num_fluid_cells], \n",
" idx=gpu_index_arr)\n",
" \n",
- " pdf_arr = gpu_pdf_arr.get()\n",
- " pdf_arr_tmp = gpu_pdf_arr_tmp.get()\n",
- " index_arr = gpu_index_arr.get()\n",
+ " pdf_arr[:] = gpu_pdf_arr.get()\n",
+ " pdf_arr_tmp[:] = gpu_pdf_arr_tmp.get()\n",
+ " index_arr[:] = gpu_index_arr.get()\n",
" else:\n",
" kernel_stream_collide(f=pdf_arr[:mapping.num_fluid_cells], \n",
" d=pdf_arr_tmp[:mapping.num_fluid_cells], \n",
@@ -359,14 +331,12 @@
"outputs": [
{
"data": {
- "image/png": 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",
"text/plain": [
- "<Figure size 1152x432 with 1 Axes>"
+ "<Figure size 1600x600 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -389,13 +359,24 @@
"metadata": {},
"outputs": [
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING:root:Using Nodes is experimental and not fully tested. Double check your generated code!\n",
- "WARNING:root:Using Nodes is experimental and not fully tested. Double check your generated code!\n",
- "WARNING:root:Using Nodes is experimental and not fully tested. Double check your generated code!\n",
- "WARNING:root:Lhs\"dir of type \"int64_t\" is assigned with a different datatype rhs: \"indexField[0](dir)\" of type \"int32_t\".\n"
+ "ename": "AttributeError",
+ "evalue": "'tuple' object has no attribute 'items'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[14], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m channel:\n\u001b[0;32m----> 2\u001b[0m reference \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_channel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdomain_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforce\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlb_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 4\u001b[0m reference \u001b[38;5;241m=\u001b[39m create_lid_driven_cavity(domain_size, relaxation_rate\u001b[38;5;241m=\u001b[39momega, lid_velocity\u001b[38;5;241m=\u001b[39mlid_velocity,\n\u001b[1;32m 5\u001b[0m compressible\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+ "File \u001b[0;32m~/pystencils/lbmpy/lbmpy/scenarios.py:142\u001b[0m, in \u001b[0;36mcreate_channel\u001b[0;34m(domain_size, force, pressure_difference, u_max, diameter_callback, duct, wall_boundary, parallel, data_handling, **kwargs)\u001b[0m\n\u001b[1;32m 140\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mforce\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m([force, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m][:dim])\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m data_handling\u001b[38;5;241m.\u001b[39mperiodicity[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m--> 142\u001b[0m step \u001b[38;5;241m=\u001b[39m \u001b[43mLatticeBoltzmannStep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_handling\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_handling\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mforce_driven_channel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m pressure_difference:\n\u001b[1;32m 144\u001b[0m inflow \u001b[38;5;241m=\u001b[39m FixedDensity(\u001b[38;5;241m1.0\u001b[39m \u001b[38;5;241m+\u001b[39m pressure_difference)\n",
+ "File \u001b[0;32m~/pystencils/lbmpy/lbmpy/lbstep.py:122\u001b[0m, in \u001b[0;36mLatticeBoltzmannStep.__init__\u001b[0;34m(self, domain_size, lbm_kernel, periodicity, kernel_params, data_handling, name, optimization, velocity_data_name, density_data_name, density_data_index, compute_velocity_in_every_step, compute_density_in_every_step, velocity_input_array_name, time_step_order, flag_interface, alignment_if_vectorized, fixed_loop_sizes, timeloop_creation_function, lbm_config, lbm_optimisation, config, **method_parameters)\u001b[0m\n\u001b[1;32m 119\u001b[0m lbm_config \u001b[38;5;241m=\u001b[39m replace(lbm_config, temporary_field_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tmp_arr_name)\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m time_step_order \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstream_collide\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m--> 122\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lbmKernels \u001b[38;5;241m=\u001b[39m [\u001b[43mcreate_lb_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlbm_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlbm_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 123\u001b[0m \u001b[43m \u001b[49m\u001b[43mlbm_optimisation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlbm_optimisation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 124\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m]\n\u001b[1;32m 125\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m time_step_order \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcollide_stream\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 126\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lbmKernels \u001b[38;5;241m=\u001b[39m [create_lb_function(lbm_config\u001b[38;5;241m=\u001b[39mlbm_config,\n\u001b[1;32m 127\u001b[0m lbm_optimisation\u001b[38;5;241m=\u001b[39mlbm_optimisation,\n\u001b[1;32m 128\u001b[0m config\u001b[38;5;241m=\u001b[39mconfig,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 132\u001b[0m config\u001b[38;5;241m=\u001b[39mconfig,\n\u001b[1;32m 133\u001b[0m kernel_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstream_pull_only\u001b[39m\u001b[38;5;124m'\u001b[39m)]\n",
+ "File \u001b[0;32m~/pystencils/lbmpy/lbmpy/creationfunctions.py:505\u001b[0m, in \u001b[0;36mcreate_lb_function\u001b[0;34m(ast, lbm_config, lbm_optimisation, config, optimization, **kwargs)\u001b[0m\n\u001b[1;32m 502\u001b[0m ast \u001b[38;5;241m=\u001b[39m lbm_config\u001b[38;5;241m.\u001b[39mast\n\u001b[1;32m 504\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ast \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 505\u001b[0m ast \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_lb_ast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlbm_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate_rule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlbm_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlbm_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 506\u001b[0m \u001b[43m \u001b[49m\u001b[43mlbm_optimisation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlbm_optimisation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 508\u001b[0m res \u001b[38;5;241m=\u001b[39m ast\u001b[38;5;241m.\u001b[39mcompile()\n\u001b[1;32m 510\u001b[0m res\u001b[38;5;241m.\u001b[39mmethod \u001b[38;5;241m=\u001b[39m ast\u001b[38;5;241m.\u001b[39mmethod\n",
+ "File \u001b[0;32m~/pystencils/lbmpy/lbmpy/creationfunctions.py:530\u001b[0m, in \u001b[0;36mcreate_lb_ast\u001b[0;34m(update_rule, lbm_config, lbm_optimisation, config, optimization, **kwargs)\u001b[0m\n\u001b[1;32m 525\u001b[0m update_rule \u001b[38;5;241m=\u001b[39m create_lb_update_rule(lbm_config\u001b[38;5;241m.\u001b[39mcollision_rule, lbm_config\u001b[38;5;241m=\u001b[39mlbm_config,\n\u001b[1;32m 526\u001b[0m lbm_optimisation\u001b[38;5;241m=\u001b[39mlbm_optimisation, config\u001b[38;5;241m=\u001b[39mconfig)\n\u001b[1;32m 528\u001b[0m field_types \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(fa\u001b[38;5;241m.\u001b[39mfield\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;28;01mfor\u001b[39;00m fa \u001b[38;5;129;01min\u001b[39;00m update_rule\u001b[38;5;241m.\u001b[39mdefined_symbols \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fa, Field\u001b[38;5;241m.\u001b[39mAccess))\n\u001b[0;32m--> 530\u001b[0m config \u001b[38;5;241m=\u001b[39m \u001b[43mreplace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcollate_types\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfield_types\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mghost_layers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 531\u001b[0m ast \u001b[38;5;241m=\u001b[39m create_kernel(update_rule, config\u001b[38;5;241m=\u001b[39mconfig)\n\u001b[1;32m 533\u001b[0m ast\u001b[38;5;241m.\u001b[39mmethod \u001b[38;5;241m=\u001b[39m update_rule\u001b[38;5;241m.\u001b[39mmethod\n",
+ "File \u001b[0;32m/usr/lib/python3.11/dataclasses.py:1492\u001b[0m, in \u001b[0;36mreplace\u001b[0;34m(obj, **changes)\u001b[0m\n\u001b[1;32m 1485\u001b[0m changes[f\u001b[38;5;241m.\u001b[39mname] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(obj, f\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 1487\u001b[0m \u001b[38;5;66;03m# Create the new object, which calls __init__() and\u001b[39;00m\n\u001b[1;32m 1488\u001b[0m \u001b[38;5;66;03m# __post_init__() (if defined), using all of the init fields we've\u001b[39;00m\n\u001b[1;32m 1489\u001b[0m \u001b[38;5;66;03m# added and/or left in 'changes'. If there are values supplied in\u001b[39;00m\n\u001b[1;32m 1490\u001b[0m \u001b[38;5;66;03m# changes that aren't fields, this will correctly raise a\u001b[39;00m\n\u001b[1;32m 1491\u001b[0m \u001b[38;5;66;03m# TypeError.\u001b[39;00m\n\u001b[0;32m-> 1492\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__class__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mchanges\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m<string>:24\u001b[0m, in \u001b[0;36m__init__\u001b[0;34m(self, target, backend, function_name, data_type, default_number_float, default_number_int, iteration_slice, ghost_layers, cpu_openmp, cpu_vectorize_info, cpu_blocking, omp_single_loop, gpu_indexing, gpu_indexing_params, default_assignment_simplifications, cpu_prepend_optimizations, use_auto_for_assignments, index_fields, coordinate_names, allow_double_writes, skip_independence_check)\u001b[0m\n",
+ "File \u001b[0;32m~/pystencils/pystencils/pystencils/config.py:177\u001b[0m, in \u001b[0;36mCreateKernelConfig.__post_init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_type(dtype)\n\u001b[1;32m 176\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_type, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m--> 177\u001b[0m dt \u001b[38;5;241m=\u001b[39m \u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata_type\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# The copy is necessary because BasicType has sympy shinanigans\u001b[39;00m\n\u001b[1;32m 178\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_type \u001b[38;5;241m=\u001b[39m defaultdict(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mDataTypeFactory(dt))\n\u001b[1;32m 180\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_type, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_type, defaultdict):\n",
+ "File \u001b[0;32m/usr/lib/python3.11/copy.py:102\u001b[0m, in \u001b[0;36mcopy\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(rv, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n\u001b[0;32m--> 102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_reconstruct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrv\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/usr/lib/python3.11/copy.py:273\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 271\u001b[0m state \u001b[38;5;241m=\u001b[39m deepcopy(state, memo)\n\u001b[1;32m 272\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__setstate__\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 273\u001b[0m \u001b[43my\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__setstate__\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 275\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(state, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(state) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n",
+ "File \u001b[0;32m~/.local/lib/python3.11/site-packages/sympy/core/basic.py:144\u001b[0m, in \u001b[0;36mBasic.__setstate__\u001b[0;34m(self, state)\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__setstate__\u001b[39m(\u001b[38;5;28mself\u001b[39m, state):\n\u001b[0;32m--> 144\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, value \u001b[38;5;129;01min\u001b[39;00m \u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m():\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28msetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, name, value)\n",
+ "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'items'"
]
}
],
@@ -410,7 +391,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -437,7 +418,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.9"
+ "version": "3.11.0rc1"
}
},
"nbformat": 4,
diff --git a/setup.py b/setup.py
index 1d055a76b2bf5266440618eb5a22fefc2a437c27..87ca1837bbb20d6cdd74106d3ac909fa7135a473 100644
--- a/setup.py
+++ b/setup.py
@@ -107,7 +107,7 @@ setup(name='lbmpy',
"Source Code": "https://i10git.cs.fau.de/pycodegen/lbmpy",
},
extras_require={
- 'gpu': ['pycuda'],
+ 'gpu': ['cupy'],
'opencl': ['pyopencl'],
'alltrafos': ['islpy', 'py-cpuinfo'],
'interactive': ['scipy', 'scikit-image', 'cython', 'matplotlib',