New kernel reduction interface

[mdavis36]

New kernel reduction interface

[mdavis36]

@rchen20 Updated the example above, the lambda argument itself is m_worksum, the target for the final reduction result is worksum. These should be different. m_worksum is the thread local value to be used before the actual reduction work is done later.

[rchen20]

Hey @mdavis36, in the implicit lambda case, are there typos where data_t m_red ought to be data_t & worksum? If so, is this implying that we need to pass the reduced data to each lambda, regardless of whether that lambda actually performs a reduction?

[rcarson3]

@mdavis36 if I'm reading the above would this essentially collapse all the various different reduction types (e.g. `RAJA::ReduceSum<RAJA::seq_reduce, int> RAJA::ReduceSum<RAJA::omp_reduce_ordered, int> RAJA::ReduceSum<RAJA::cuda_reduce, int>, etc...) down to one single type ? So, you would only need 1 data type for all your different execution policies?

If so I'd just like to say that I'd be very much for such a feature as forall loops of mine that have those operations are the only ones I can't abstract away to a single forall abstraction using something like raja::expt::dynamic_forall feature for all the execution policies I support in my libraries/apps (cpu, openmp, cuda, hip, etc...).

Unfortunately, things like std::variant or std::visit still are not supported on the device, at least to my current knowledge of things, which would have allowed a simple-ish solution to the above.