WIP: Add PARTED kernels #382
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This does the same thing as TRIAD but breaks it into multiple for loops over the data instead of a single for loop over the data.
Leave in comments of other dispatch options.
This makes each partition a multiple of the size of the prevoius partition
This tuning provides a best case scenario where the overhead of capturing the state and synchronizing per rep is removed.
The new gpu tuning is a AOS version using triad_holder. This is now in addition to the SOA tuning.
This copies the basic mempool from RAJA and adds a capability to synchronize as necessary to avoid host device race conditions when memory is needed on the host and but all the memory has been used on the device.
Default is on so the sizes of partitions are not always in non-decreasing order.
This uses a scan and binary search to schedule work to blocks instead of a 2d grid. Thus it avoids blocks with no work.
This is faster for cuda but slower for hip.
This has a minimal effect
with triad parted fused This has a large effect and makes a block size of 256 as good or better than 1024
always use binary search code
reorder TRIAD_PARTED gpu tuning declarations
These tuning use events to "fork-join" the streams as would be required in more realistic code. Though it would not always have to be done as frequently.
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WIP: Add PARTED kernels
These parted or partitioned kernels do the same computation over the same data as the Stream TRIAD kernel, but instead of a single loop over all of the data they use multiple kernels over parts of the data. So the same work is ultimately done to the same data just broken up into multiple partitions.
The idea is to look at ways of improving performance when running the same kernel over different data like when running over subdomains or tiles in a block structured code or with AMR.
Ideas:
add kernels listed in Add kernels broken up over multiple loops #381
add a folder to keep this performance study in a single place
split up data unevenly (currently each part gets size/num_parts data)
This PR is a feature
It does the following: