In certain circumstances (currently, when logging is enabled), VTK-m
libraries depend on the threading library. However, when the VTK-m
package was included from an external project, it did not automatically
find the threads package. This change makes the Threads library loaded
when the VTK-m package is found.
VTK-m has been updated to replace old per device benchmark executables with a device
dependent shared library so that it's able to accept a device adapter at runtime through
the "--device=" argument.
Mask objects allow you to specify which output values should be
generated when a worklet is run. That is, the Mask allows you to skip
the invocation of a worklet for any number of outputs.
af4aef991 Add missing %= operator to ArrayPortalValueReference
9e02cd33a Declare ArrayPortalValueReference::operator= as const
b141f7515 Add more operator= to ArrayPortalValueReference
c8db70ae8 Fix warning about automatic conversions loosing precision
ddc6c91e3 Fix error about constexpr not available on CUDA device
1ca55ac31 Add specialized operators for ArrayPortalValueReference
Acked-by: Kitware Robot <kwrobot@kitware.com>
Acked-by: Allison Vacanti <allison.vacanti@kitware.com>
Merge-request: !1542
Declaring operator= as const seems a little weird because we are
changing the value. But remember that ArrayPortalReference is only a
reference class. The reference itself does not change, just the thing
that it is referencing. So declaring as const is correct and necessary
so that you can set the value of a reference returned from a function
(which is a right hand side value).
For some reason, these changes caused one of the CUDA compilers to
create an error about a variable declared constexpr not being available
on the device. That sounds like a bug in nvcc as the constexpr should
just be evaluated rather than stored in some part of memory. At any
rate, changing the constexpr to a preprocessing macro solves the
problem.
The ArrayPortalValueReference is supposed to behave just like the value
it encapsulates and does so by automatically converting to the base type
when necessary. However, when it is possible to convert that to
something else, it is possible to get errors about ambiguous overloads.
To avoid these, add specialized versions of the operators to specify
which ones should be used.
Also consolidated the CUDA version of an ArrayPortalValueReference to the
standard one. The two implementations were equivalent and we would like
changes to apply to both.
d19524016 Refactor View class and fix its memory leak
Acked-by: Kitware Robot <kwrobot@kitware.com>
Acked-by: Robert Maynard <robert.maynard@kitware.com>
Merge-request: !1546
The timer class now is asynchronous and device independent. it's using an
similiar API as vtkOpenGLRenderTimer with Start(), Stop(), Reset(), Ready(),
and GetElapsedTime() function. For convenience and backward compability, Each
Start() function call will call Reset() internally and each GetElapsedTime()
function call will call Stop() function if it hasn't been called yet for keeping
backward compatibility purpose.
Bascially it can be used in two modes:
* Create a Timer without any device info. vtkm::cont::Timer time;
* It would enable timers for all enabled devices on the machine. Users can get a
specific elapsed time by passing a device id into the GetElapsedtime function.
If no device is provided, it would pick the maximum of all timer results - the
logic behind this decision is that if cuda is disabled, openmp, serial and tbb
roughly give the same results; if cuda is enabled it's safe to return the
maximum elapsed time since users are more interested in the device execution
time rather than the kernal launch time. The Ready function can be handy here
to query the status of the timer.
* Create a Timer with a device id. vtkm::cont::Timer time((vtkm::cont::DeviceAdapterTagCuda()));
* It works as the old timer that times for a specific device id.
The kernel launch component of the runtime device adapter is fairly
pointless. If the hardware supports CUDA we should expect that
VTK-m has the correct kernel versions.
Plus in the original version if the CUDA device was being used
and the kernel launch returns cudaErrorDevicesUnavailable it
was never possible to restore CUDA support. Now what happens
is that the runtime tracker is marked as failed, but the
calling code can always go back and trying the device again.
9580b1921 Introduces SourceInInstall which verifies that VTK-m install its headers
c501500e1 Install missing headers found by VTKmCheckSourceInInstall
baaa47af4 Reduce verbosity of VTKmCheckCopyright
545a2ce91 VTKmCheckSourceInBuild now shows all missing files in a directory
Acked-by: Kitware Robot <kwrobot@kitware.com>
Acked-by: Matt Larsen <larsen30@llnl.gov>
Merge-request: !1532
Previously, we precompiled just about any version of Keys that you could
be expected to use. This caused the time to compile Keys to be
unnecessarily long.
This reduces the compilation to types that are actually likely to be
used as keys. Also removed the less likely to be used build methods.
55570a16a Add most common implementations of Keys templates to library
887f79c6f Make a vtkm_worklet library
Acked-by: Kitware Robot <kwrobot@kitware.com>
Acked-by: Robert Maynard <robert.maynard@kitware.com>
Merge-request: !1520
002bd3419 Improve the compile time of WorkletMap* unit tests
Acked-by: Kitware Robot <kwrobot@kitware.com>
Acked-by: Kenneth Moreland <kmorel@sandia.gov>
Merge-request: !1527
Also moved the Keys<>::SortType outside to KeysSortType. The problem
with having it inside the Keys class was that there was a different
object created for every instances of Keys.
This is only set while compiling device code, and is useful
for code that needs different implementations on devices (e.g.
they call CUDA device intrinsics, etc).
