Some functions are supposed to behave correctly when given a NaN. This
might only be valid if floation point exceptions are not trapped, so
disable trapping for these tests.
Some of the code in the base `vtkm` namespace is device specific. For
example, the functions in `Math.h` are customized for specific devices.
Thus, we want this code to be specially compiled and run on these
devices.
Previously, we made a header file and then added separate tests to each
device package. That was created before we had ways of running on any
device. Now, it is much easier to compile the test a single time for all
devices and use the `ALL_BACKENDS` feature of `vtkm_unit_tests` CMake
function to automatically create the test for all devices.
It is very easy to cause ODR violations with DeviceAdapterTagCuda.
If you include that header from a C++ file and a CUDA file inside
the same program we an ODR violation. The reasons is that the C++
versions will say the tag is invalid, and the CUDA will say the
tag is valid.
The solution to this is that any compilation unit that includes
DeviceAdapterTagCuda from a version of VTK-m that has CUDA enabled
must be invoked by the cuda compiler.
`vtkm::cont::testing` now initializes with logging enabled and support
for device being passed on the command line, `vtkm::testing` only
enables logging.
VTK-m has been updated to replace old per device worklet testing executables with a device
dependent shared library so that it's able to accept a device adapter
at runtime.
Meanwhile, it updates the testing infrastructure APIs. vtkm::cont::testing::Run
function would call ForceDevice when needed and if users need the device
adapter info at runtime, RunOnDevice function would pass the adapter into the functor.
Optional Parser is bumped from 1.3 to 1.7.
Sandia National Laboratories recently changed management from the
Sandia Corporation to the National Technology & Engineering Solutions
of Sandia, LLC (NTESS). The copyright statements need to be updated
accordingly.
Also fix some issues that caused the compile to fail when trying to
run some of the math functions on a CUDA device. In particular, CUDA
is picky about using a global const on a device when the const type is
not one of the basic C types.
