Use stream pool for gather/scatter. #14162
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Gather (scatter) summary:
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This is probably not going to do much for the listed issue. The issue here is the number of raw thrust and kernel calls stemming from nesting (think thousands of columns underneath the top level table). The fix for that is going to be smarter parallelization of what is currently the recursive cpu-side approach. We have some ideas here. |
abellina
reviewed
Sep 29, 2023
| // only a single column, the fork/join overhead should be avoided. | ||
| auto streams = std::vector<rmm::cuda_stream_view>{}; | ||
| if (num_columns > 1) { | ||
| streams = cudf::detail::fork_streams(stream, num_columns); |
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I am curious how this works for per-thread default stream. For spark, we build cuDF with PTDS. Will streams will be have number-of-columns vector of the PTDS stream?
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The stream passed in would be the PTDS stream. Then a stream pool (for that thread) would be created (or reused), the work would be executed across that stream pool, and then the join step would insert events for all the elements of streams to be synchronized with stream before new work on stream (the PTDS stream in Spark's case) would be runnable.
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Description
This PR uses the stream pool introduced in #13922 to gather/scatter each column in a table on a separate stream.
Related: #13509, which this might resolve (need to verify).
Checklist