Previously we always ran DynamicTransformCont even if we knew all the types.
By checking for Dynamic types first, we save roughly 3% on the binary size.
This also is a good starting point for a redesign of DynamicTransformCont
39142d83 Add convenience tags like FieldInPoint, FieldInCell, to WorkletMapPointToCell
f34119b6 Clarify the name of worklet for point to cell operations.
2c767e10 Add WorkletMapTopologyBase to make Dispatcher templates easier to read.
05d397cb Remove unnecessary template parameters from DispatcherMapField
Acked-by: Kitware Robot <kwrobot@kitware.com>
Merge-request: !246
CUDA default constructors, destructors, and assignment operators
Several classes exclusively work in the control environment. However, CUDA likes to add __device__ to constructors, destructors, and assignment operators it automatically creates. This in turn causes warnings about the __device__ function using host-only classes (like boost::shared_ptr). Solve this problem by adding explicit methods for all of these.
See merge request !245
DispatcherMapField was templated on the device adapter but it
actually doesn't need to be, only BasicInvoke and subsequent
methods need to be templated on the device.
The DynamicArrayHandle and DynamicCellSet classes exclusively work in
the control environment. However, CUDA likes to add __device__ to
constructors, destructors, and assignment operators it automatically
adds. This in turn causes warnings about the __device__ function using
host-only classes (like boost::shared_ptr). Solve this problem by adding
explicit methods for all of these.
The CellSet classes all exclusively work in the control environment.
However, CUDA likes to add __device__ to constructors, destructors, and
assignment operators it automatically adds. This in turn causes warnings
about the __device__ function using host-only classes (like
boost::shared_ptr). Solve this problem by adding explicit methods for
all of these.
The ArrayHandle classes all exclusively work in the control environment.
However, CUDA likes to add __device__ to constructors, destructors, and
assignment operators it automatically adds. This in turn causes warnings
about the __device__ function using host-only classes (like
boost::shared_ptr). Solve this problem by adding explicit methods for
all of these.
Implemented this by wrapping up all these default objects in a macro.
This also solved the problem of other constructors that are necessary
for array handles such as a constructor that takes the base array
handle.
There is a strange nvcc warning in CUDA 7.5 that sometimes happens on MSVC
that causes it to emit a warning for an undefined method that is clearly
defined. The CUDA development team is aware of the problem and is going
to fix it, but these changes will work around the problem for now.
Thanks to Tom Fogal from NVIDIA for these fixes.
Under CUDA, the default constructors and destructors created are exported
as __host__ and __device__, which causes problems because they used a boost
pointer that only works on the host. The explicit copy constructors and
destructors do the same thing as the default ones except declared to only
work on the host.
This now allows for even more efficient construction of uniform point
coordinates when running under the 3d scheduler, since we don't need to go
from 3d index to flat index to 3d index, instead we stay in 3d index
Change Fetches to use ThreadIndices instead of Invocation.
Previously, all Fetch objects received an Invocation object in their
Load and Store methods. The point of this was that it allowed the Fetch
to get data from any of the execution objects. However, every Fetch
either just got data directly from its associated execution object or
else used a secondary execution object (the input domain) to get indices
into their own execution object.
This left two potential areas for improvement. First, pulling data out
of the Invocation object was unnecessarily complicated. It would be much
nicer to get data directly from the associated execution object. Second,
when getting index information from the input domain, it was often the
case that extra computations were necessary (particularly on structured
cell sets). There was no way to share the index information among
Fetches, and therefore the computations were replicated.
This change removes the Invocation from the Fetch Load and Store.
Instead, it passes the associated execution object and a new object type
called the ThreadIndices. The ThreadIndices are customized for the input
domain and therefore have all the information needed for a redirected
lookup. It is also a thread-local object so it can cache computed
indices and save on computation time.
See merge request !233
Array handles for cuda device pointers have been implemented. The data for
these handles exists solely on the exec side (info such as length can be
queried from the cont side).
Previously, all Fetch objects received an Invocation object in their
Load and Store methods. The point of this was that it allowed the Fetch
to get data from any of the execution objects. However, every Fetch
either just got data directly from its associated execution object or
else used a secondary execution object (the input domain) to get indices
into their own execution object.
This left two potential areas for improvement. First, pulling data out
of the Invocation object was unnecessarily complicated. It would be much
nicer to get data directly from the associated execution object. Second,
when getting index information from the input domain, it was often the
case that extra computations were necessary (particularly on structured
cell sets). There was no way to share the index information among
Fetches, and therefore the computations were replicated.
This change removes the Invocation from the Fetch Load and Store.
Instead, it passes the associated execution object and a new object type
called the ThreadIndices. The ThreadIndices are customized for the input
domain and therefore have all the information needed for a redirected
lookup. It is also a thread-local object so it can cache computed
indices and save on computation time.
The current CUDA compiler is crashing when using _Pragma in code blocks,
which were being used with clang on apple. Turn these off when compiling
with CUDA.
9a8809f9 Add CellSetPermutation which allows custom iteration over a cell set.
66f6db5a IsWriteableArrayHandle now can tell if an array handle can be written too
20f3fb50 Update VertexClustering to use vtkm::cont::CellSetSingleType.
154896b7 Extend the test for DataSetSingleType.
Acked-by: Kitware Robot <kwrobot@kitware.com>
Merge-request: !228
When you create a CellSetPermutation you provide an array of the cell ids that
you want to iterate. This allows the user to do custom blanking of a data set,
or to do multi iteration over a set of cells.
8bc40880 Add a test for CellSetExplicit::GetIndices
fa81d1de CellSetExplicit always calls methods using "this->"
9e496306 Allow incremental construction of CellSetSingleType.
3e307879 Mark CellSetExplicit::Fill as host side only.
Acked-by: Kitware Robot <kwrobot@kitware.com>
Merge-request: !227
