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.
Sometimes the CUDA runtime would not allocate sufficient stack
space for the particle advection code to run. This issue was exposed by
!1737 -- for some reason, once those changes to unrelated filters/worklets
are added to VTK, CUDA allocates less stack and the following tests would
fail:
UnitTestLagrangianFilterCUDA
UnitTestLagrangianStructuresFilterCUDA
UnitTestStreamlineFilterCUDA
UnitTestStreamSurfaceFilterCUDA
These were fixed by increasing the stack size in the particle advection
worklet Run(...) methods.
An RAII helper has been added that will restore the previous stack size
in case an exception is thrown, and the KDTree code has been updated
to use this helper when it adjusts the CUDA stack allocation.
- Use AtomicInterface to implement device-specific atomic operations.
- Remove DeviceAdapterAtomicArrayImplementations.
- Extend supported atomic types to include unsigned 32/64-bit ints.
- Add a static_assert to check that AtomicArray type is supported.
- Add documentation for AtomicArrayExecutionObject, including a CAS
example.
- Add a `T Get(idx)` method to AtomicArrayExecutionObject that does
an atomic load, and update existing CAS usage to use this instead
of `Add(idx, 0)`.
VTK-m now offers a more GPU aware set of defaults for kernel scheduling.
When VTK-m first launches a kernel we do system introspection and determine
what GPU's are on the machine and than match this information to a preset
table of values. The implementation is designed in a way that allows for
VTK-m to offer both specific presets for a given GPU ( V100 ) or for
an entire generation of cards ( Pascal ).
Currently VTK-m offers preset tables for the following GPU's:
- Tesla V100
- Tesla P100
If the hardware doesn't match a specific GPU card we than try to find the
nearest know hardware generation and use those defaults. Currently we offer
defaults for
- Older than Pascal Hardware
- Pascal Hardware
- Volta+ Hardware
Some users have workloads that don't align with the defaults provided by
VTK-m. When that is the cause, it is possible to override the defaults
by binding a custom function to `vtkm::cont::cuda::InitScheduleParameters`.
As shown below:
```cpp
ScheduleParameters CustomScheduleValues(char const* name,
int major,
int minor,
int multiProcessorCount,
int maxThreadsPerMultiProcessor,
int maxThreadsPerBlock)
{
ScheduleParameters params {
64 * multiProcessorCount, //1d blocks
64, //1d threads per block
64 * multiProcessorCount, //2d blocks
{ 8, 8, 1 }, //2d threads per block
64 * multiProcessorCount, //3d blocks
{ 4, 4, 4 } }; //3d threads per block
return params;
}
vtkm::cont::cuda::InitScheduleParameters(&CustomScheduleValues);
```
661fb64de AtomicInterfaceControl functions are marked with VTKM_SUPPRESS_EXEC_WARNINGS
0c70f9b9a Add BitFieldIn/Out/InOut worklet signature tags.
a66510e81 Add ArrayHandleBitField, a boolean-valued AH backed by a BitField.
56cc5c3d3 Add support for BitFields.
d01b97382 Allow VTKM_SUPPRESS_EXEC_WARNINGS to be used inside macros.
2f2ca9370 Add bit operations FindFirstSetBit and CountSetBits to Math.h.
Acked-by: Kitware Robot <kwrobot@kitware.com>
Merge-request: !1629
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.
When reducing an input type that differs from the output type
you need to write a custom binary operator that also implements
how to do the unary transformation.
This change allows you to set a subclass of
vtkm::cont::ExecutionObjectBase as a functor
used in ArrayHandleTransform. This latter class will then detect that
the functor is an ExecObject and will call PrepareForExecution with the
appropriate device to get the actual Functor object.
This change allows you to use virtual objects and other device dependent
objects as functors for ArrayHandleTransform without knowing a priori
what device the portal will be used on.
1. Have a per-thread pinned array for cuda errors
2. Check for errors before scheduling new tasks and at explicit sync points
3. Remove explicit synchronizations from most places
Addresses part 2 of #168
This is done to not only reduce the amount of code that users need
to generate but to reduce the amount of errors when using
the RuntimeDeviceTracker. If the runtime device tracker is initially
used in a library by a c++ file it will never properly detect the
cuda backend. By moving the code into vtkm_cont we can make sure
this problem doesn't occur.
This allows for easier host side logic when determining grid and block
sizes, and allows for a smaller library side by moving some logic
into compiled in functions.
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.
Redesigns the TBB and Serial backends and the vtkm::exec::Task concept so that
we can re-use the same launching logic for all Worklets, instead of generating
per worlet code. To keep the performance the same the TilingTask now is past
a range of indices to work on, rather than a single index.
Binary size reduction:
WorkletTests_SERIAL old - 19MB
WorkletTests_SERIAL new - 18MB
WorkletTests_TBB old - 39MB
WorkletTests_TBB new - 18MB
libvtkAcceleratorsVTKm old - 48MB
libvtkAcceleratorsVTKm new - 19MB
Most functions in the CUDA runtime API return an error code that must be
checked to determine whether the operation completed successfully. Most
operations in VTK-m just called the function and assumed it completed
correctly, which could lead to further errors. This change wraps most
CUDA calls in a VTKM_CUDA_CALL macro that checks the error code and
throws an exception if the call fails.
Change the VTKM_CONT_EXPORT to VTKM_CONT. (Likewise for EXEC and
EXEC_CONT.) Remove the inline from these macros so that they can be
applied to everything, including implementations in a library.
Because inline is not declared in these modifies, you have to add the
keyword to functions and methods where the implementation is not inlined
in the class.
