Several revisions ago, the ability to use virtual methods in the
execution environment was deprecated. Completely remove this
functionality for the VTK-m 2.0 release.
Deprecate `VirtualObjectHandle` and all other classes that are used to
implement objects with virtual methods in the execution environment.
Additionally, the code is updated so that if the
`VTKm_NO_DEPRECATED_VIRTUAL` flag is set none of the code is compiled at
all. This opens us up to opportunities that do not work with virtual
methods such as backends that do not support virtual methods and dynamic
libraries for CUDA.
This class was used indirectly by the old `ArrayHandle`, through
`ArrayHandleTransfer`, to move data to and from a device. This
functionality has been replaced in the new `ArrayHandle`s through the
`Buffer` class (which can be compiled into libraries rather than make
every translation unit compile their own template).
This commit removes `ArrayManagerExecution` and all the implementations
that the device adapters were required to make. None of this code was in
any use anymore.
Now that we have atomic free functions (e.g. `vtkm::AtomicAdd()`), we no
longer need special implementations for control and each execution
device. (Well, technically we do have special implementations for each,
but they are handled with compiler directives in the free functions.)
Convert the old atomic interface classes (`AtomicInterfaceControl` and
`AtomicInterfaceExecution`) to use the new atomic free functions. This
will allow us to test the new atomic functions everywhere that atomics
are used in VTK-m.
Once verified, we can deprecate the old atomic interface classes.
The buffer class encapsulates the movement of raw C arrays between
host and devices.
The `Buffer` class itself is not associated with any device. Instead,
`Buffer` is used in conjunction with a new templated class named
`DeviceAdapterMemoryManager` that can allocate data on a given
device and transfer data as necessary. `DeviceAdapterMemoryManager`
will eventually replace the more complicated device adapter classes
that manage data on a device.
The code in `DeviceAdapterMemoryManager` is actually enclosed in
virtual methods. This allows us to limit the number of classes that
need to be compiled for a device. Rather, the implementation of
`DeviceAdapterMemoryManager` is compiled once with whatever compiler
is necessary, and then the `RuntimeDeviceInformation` is used to
get the correct object instance.
The `ArrayHandleStreaming` class stems from an old research project
experimenting with bringing data from an `ArrayHandle` in parts and
overlapping device transfer and execution. It works, but only in very
limited contexts. Thus, it is not actually used today. Plus, the feature
requires global indexing to be permutated throughout the worklet
dispatching classes of VTK-m for no further reason.
Because it is not really used, there are other more promising approaches
on the horizon, and it makes further scheduling improvements difficult,
we are removing this functionality.
1f1688483 Initial infrastructure to allow WorkletMapField to have 3D scheduling
Acked-by: Kitware Robot <kwrobot@kitware.com>
Acked-by: Kenneth Moreland <kmorel@sandia.gov>
Merge-request: !1938
Marked the old versions of PrepareFor* that do not use tokens as
deprecated and moved all of the code to use the new versions that
require a token. This makes the scope of the execution object more
explicit so that it will be kept while in use and can potentially be
reclaimed afterward.
BitFields are:
- Stored in memory using a contiguous buffer of bits.
- Accessible via portals, a la ArrayHandle.
- Portals operate on individual bits or words.
- Operations may be atomic for safe use from concurrent kernels.
The new BitFieldToUnorderedSet device algorithm produces an ArrayHandle
containing the indices of all set bits, in no particular order.
The new AtomicInterface classes provide an abstraction into bitwise
atomic operations across control and execution environments and are used
to implement the BitPortals.
Having VTKM_EXEC on algorithms for CPU devices was problematic because
the algorithms were specific to the CPU, but during a CUDA compile it
would try to compile device code (for no reasons since it was never
called on a device).
Remove these identifiers for the idea that a device implementation knows
specifically what function modifiers to use and does not need the VTK-m
defined catch-alls.
The previous implementation of DeviceAdapterRuntimeDetector caused
multiple differing definitions of the same class to exist and
was causing the runtime device tracker to report CUDA as disabled
when it actually was enabled.
The ODR was caused by having a default implementation for
DeviceAdapterRuntimeDetector and a specific specialization for
CUDA. If a library had both CUDA and C++ sources it would pick up
both implementations and would have undefined behavior. In general
it would think the CUDA backend was disabled.
To avoid this kind of situation in the future I have reworked VTK-m
so that each device adapter must implement DeviceAdapterRuntimeDetector
for that device.
By hard coding the PrepareForDevice to know about all the different VTK-m
devices, we can have a single base class do the execution allocation, and not
have that logic repeated in each child class.
By using WrappedBinaryOperator we will not get warnings on vs2017 when
scanning <32bit arrays, and at the same time also properly support
fancy arrays.