Previously we just looked at the raw count of probed keys. Now, we compute the average and max of both the probe distance measured in the number of groups probed, and the number of probe compares measured in the compares required before finding the matching entry.
This lets us understand the relative impact of probe-distance vs. tag collisions on a given set of benchmark keys. Some of this is motivated by considering additional optimization techniques similar to those used in Boost's table and the F14 table from Facebook/Meta.
Previously we just looked at the raw count of probed keys. Now, we compute the average and max of both the probe distance measured in the number of groups probed, and the number of probe compares measured in the compares required before finding the matching entry.
This lets us understand the relative impact of probe-distance vs. tag collisions on a given set of benchmark keys. Some of this is motivated by considering additional optimization techniques similar to those used in Boost's table and the F14 table from Facebook/Meta.