Support serializing packed tables directly for the normal shuffle path #10
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--------- Signed-off-by: Firestarman <[email protected]>
Signed-off-by: Firestarman <[email protected]>
Signed-off-by: Firestarman <[email protected]>
Signed-off-by: Firestarman <[email protected]>
Signed-off-by: Firestarman <[email protected]>
…uffle-gpu-serde Signed-off-by: Firestarman <[email protected]>
Signed-off-by: Firestarman <[email protected]>
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This PR is trying to accelerate the normal shuffle path by partitioning and slicing tables on GPU.
The sliced table is already serializable so can be written to the Shuffle output stream directly, along with a lightweight metadata (a TableMeta) to rebuild the table on the Shuffle read side.
On the Shuffle read side, the new introduced PackedTableIterator will read the tables from the Shuffle input stream and rebuild them on GPU by leveraging the existing utils (MetaUtils, GpuCompressedColumnVector). Next, the existing GpuCoalesceBatches node is used to do the batch concatenation for the downstream operators, similar as what Rapids Shuffle does.
It led to some perf degression in NDS runs, so disable this feature by default. But we got about 2x speedup for a customer query. So we can still add in this feature and enable it explicitly for suitable cases.