diff --git a/examples/taylor_green/taylor_green_monoscale.py b/examples/taylor_green/taylor_green_monoscale.py
index 6f06a949506767b17b263c9266579172739e0ba4..d599b58deef39647775e230e82295e4627731985 100644
--- a/examples/taylor_green/taylor_green_monoscale.py
+++ b/examples/taylor_green/taylor_green_monoscale.py
@@ -34,7 +34,7 @@ def do_solve(npts, viscosity=1./1600., lcfl=0.125, cfl=0.50):
prune_threshold=1.2, override_cache=False, verbose=2)
kernel_config = KernelConfig(autotuner_config=autotuner_config)
- dim = 3
+ dim = 1
box = Box(length=(2*pi,)*dim)
@@ -44,7 +44,7 @@ def do_solve(npts, viscosity=1./1600., lcfl=0.125, cfl=0.50):
name='V', is_vector=True)
vorti = Field(domain=box, formula=init_vorticity,
name='W', is_vector=True,
- nb_components=(1 if dim==2 else 3))
+ nb_components=(1 if dim==2 else dim))
d3d = Discretization(resolution=resolution)
@@ -66,10 +66,10 @@ def do_solve(npts, viscosity=1./1600., lcfl=0.125, cfl=0.50):
# method = {SpaceDiscretization: SpaceDiscretization.FDC4}
# )
- poisson = Poisson(name='poisson', velocity=velo, vorticity=vorti,
- variables={velo:d3d, vorti: d3d})
- diffusion = Diffusion(name='diffusion', input_field=vorti, viscosity=viscosity,
- variables={vorti: d3d})
+ # poisson = Poisson(name='poisson', velocity=velo, vorticity=vorti,
+ # variables={velo:d3d, vorti: d3d})
+ # diffusion = Diffusion(name='diffusion', input_field=vorti, viscosity=viscosity,
+ # variables={vorti: d3d})
splitting = StrangSplitting(splitting_dim=dim, order=StrangOrder.STRANG_SECOND_ORDER,
method={KernelConfig: kernel_config})
@@ -88,6 +88,6 @@ def do_solve(npts, viscosity=1./1600., lcfl=0.125, cfl=0.50):
problem.finalize()
if __name__=='__main__':
- N = 721
+ N = 128*128 + 1
do_solve(N)
diff --git a/hysop/__init__.py b/hysop/__init__.py
index bf30fb5f76c4386cc4ad27c05887f0e38342b4aa..feb4b0717e7c1c3061a00285652cdfce439f9119 100644
--- a/hysop/__init__.py
+++ b/hysop/__init__.py
@@ -16,7 +16,7 @@ __FFTW_ENABLED__ = "ON" is "ON"
__SCALES_ENABLED__ = "ON" is "ON"
__OPTIMIZE__ = "OFF" is "ON"
-__VERBOSE__ = True
+__VERBOSE__ = "ON" in ["1", "3"]
__DEBUG__ = "ON" in ["2", "3"]
__TRACE__ = "ON" in ["5"]
__KERNEL_DEBUG__ = "ON" in ["4", "3"]
@@ -25,8 +25,8 @@ __PROFILE__ = "OFF" in ["0", "1"]
__ENABLE_LONG_TESTS__ = "OFF" is "ON"
# OpenCL
-__DEFAULT_PLATFORM_ID__ = 0
-__DEFAULT_DEVICE_ID__ = 1
+__DEFAULT_PLATFORM_ID__ = 1
+__DEFAULT_DEVICE_ID__ = 0
diff --git a/hysop/backend/device/codegen/kernels/directional_remesh.py b/hysop/backend/device/codegen/kernels/directional_remesh.py
index 562e1ada679c8d4d0b17ee7339bda70762e9eb27..ecd550c02558f3be2d8c2730b974d959352652b2 100644
--- a/hysop/backend/device/codegen/kernels/directional_remesh.py
+++ b/hysop/backend/device/codegen/kernels/directional_remesh.py
@@ -11,7 +11,7 @@ from hysop.tools.misc import Utils
from hysop.tools.types import check_instance
from hysop.deps import np
-from hysop.constants import DirectionLabels, BoundaryCondition, Backend
+from hysop.constants import DirectionLabels, BoundaryCondition, Backend, Precision
from hysop.core.arrays.all import OpenClArray
from hysop.backend.device.codegen.base.opencl_codegen import OpenClCodeGenerator
@@ -1051,6 +1051,28 @@ class DirectionalRemeshKernel(KernelCodeGenerator):
nparticles_options = [1,2,4,8,16]
use_atomics_options = [True] if force_atomics else [False, True]
+ if True in use_atomics_options:
+ msg=None
+ if ftype == 'float':
+ if not cl_env.has_extension('cl_khr_local_int32_base_atomics'):
+ msg='Your device does not support int32 atomics (cl_khr_local_int32_base_atomics).'
+ elif ftype == 'double':
+ if not cl_env.has_extension('cl_khr_int64_base_atomics'):
+ msg='Your device does not support int64 atomics (cl_khr_int64_base_atomics).'
+ else:
+ msg = 'Atomic remeshing kernel has not been implemented for {} yet.'.format(precision)
+ raise RuntimeError(msg)
+
+ if msg:
+ if force_atomics:
+ msg += '\nParameter force_atomics was set to True, aborting...'
+ raise RuntimeError(msg)
+ else:
+ msg += '\nAtomic version of the remeshing kernel will be disabled.'
+ import warnings
+ warnings.warn(msg)
+ use_atomics_options.remove(True)
+
autotuner_flag = autotuner_config.autotuner_flag
if (autotuner_flag == AutotunerFlags.ESTIMATE):
max_workitem_workload = [1,1,1]
diff --git a/hysop/constants.py b/hysop/constants.py
index e85cc975b76b788c6c2ee7622f57241054d7d40e..1cca17bde937c3eacc9efb87a79bc85409a16323 100644
--- a/hysop/constants.py
+++ b/hysop/constants.py
@@ -22,7 +22,7 @@ HYSOP_ROOT=os.path.dirname(inspect.getfile(hysop))
PI = math.pi
-HYSOP_REAL = np.float32
+HYSOP_REAL = np.float64
"""Set default type for real numbers"""
SIZEOF_HYSOP_REAL = int(HYSOP_REAL(1.).nbytes)
"""Size in memory of hysop real default type"""
diff --git a/hysop/core/mpi/topology.py b/hysop/core/mpi/topology.py
index afe862db5b26974ed78097d6247ca54b956e1228..400ec94c504d3c1efeadc15e75baef9f13dadaeb 100644
--- a/hysop/core/mpi/topology.py
+++ b/hysop/core/mpi/topology.py
@@ -349,12 +349,14 @@ class Cartesian(Topology):
assert shape is None, 'shape ' + msg
assert dim is None, 'dim ' + msg
self.cutdir = npw.asboolarray(cutdir).copy()
- self.dimension = self.cutdir[self.cutdir].size
+ self.dimension = self.cutdir[self.cutdir != 0].size
shape = npw.asdimarray(MPI.Compute_dims(origin_size,
self.dimension))
+
self._optimizeshape(shape)
+
self.shape = npw.dim_ones(self.domain.dimension)
- self.shape[self.cutdir] = shape
+ self.shape[self.cutdir != 0] = shape
else:
if dim is not None:
@@ -394,7 +396,7 @@ class Cartesian(Topology):
self._isperiodic = tuple(isperiodic)
# compute real dim ...
- self.dimension = self.shape[self.cutdir].size
+ self.dimension = self.shape[self.cutdir != 0].size
# Special care for the 1 process case:
if origin_size == 1:
diff --git a/hysop/tools/enum.py b/hysop/tools/enum.py
index dfd0c06af8a7303697bcc1794b14e6d83c19374e..5638134881189c16e2c3c9f59c7cb0ea9813b7aa 100644
--- a/hysop/tools/enum.py
+++ b/hysop/tools/enum.py
@@ -221,10 +221,10 @@ class EnumFactory(object):
return '{}({})'.format(self.svalue(),self.value())
def __eq__(self, other):
- assert isinstance(other, self.__class__)
+ assert isinstance(other, self.__class__), 'other.cls = {}'.format(other.__class__)
return self._value == other._value
def __ne__(self, other):
- assert isinstance(other, self.__class__)
+ assert isinstance(other, self.__class__), 'other.cls = {}'.format(other.__class__)
return self._value != other._value
#pickling