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We need to do more investigation of this compatibility in params. Thus far, I just don't think we have analyzed the Paretos for enough datasets to be able to say one way or another how whether this conversion even makes sense.
The goal here is to say "I have params M and efConstruction for HNSW, what graph_degree and intermediate_graph_degree can I use to build a CAGRA graph that when converted to HNSW will yield a similar recall/throughput curve".
One thing we can (and should) try is taking a bunch of data for Pareto curves and testing the predictive capabilities by building a simple regression model to predict the CAGRA parameters and expected throughput on the Pareto when given HNSWlib parameters. We have done little exercises like this in past for finding things like optimal code paths for kernel launches given various shapes of data.
The text was updated successfully, but these errors were encountered:
We need to do more investigation of this compatibility in params. Thus far, I just don't think we have analyzed the Paretos for enough datasets to be able to say one way or another how whether this conversion even makes sense.
The goal here is to say "I have params M and efConstruction for HNSW, what graph_degree and intermediate_graph_degree can I use to build a CAGRA graph that when converted to HNSW will yield a similar recall/throughput curve".
One thing we can (and should) try is taking a bunch of data for Pareto curves and testing the predictive capabilities by building a simple regression model to predict the CAGRA parameters and expected throughput on the Pareto when given HNSWlib parameters. We have done little exercises like this in past for finding things like optimal code paths for kernel launches given various shapes of data.
The text was updated successfully, but these errors were encountered: