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The original probe sequence, taken from Python's hash table code, is
exponential, jumping around in the hash table with poor memory locality.
This replaces the probe algorithm with the more conventional quadratic
probing.
This also adds tests to the benchmarking code using AnyRef keys, which
have pseudorandom hash codes (unlike Ints, whose hash code is simply
the Int itself). The intensity of the benchmarking is reduced to make
the tests complete within 9 hours, by removing unnecessary sampling.
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Besides tweaks to the documentation, this tests smaller (25-element)
maps, and rewrites OpenHashMapRunner in more idiomatic Scala.
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For the warm-up invocations, suppress setup and teardown that is only
needed for the measurement iterations. Reduce the number of forks.
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Also add sbteclipse to the benchmark project.
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In order to get a better exploration of the variance of tests in a
limited time, I've reduced the number of measurement iterations and
increased the number of forks. By sight, the measurement iterations
seemed pretty consistent within a trial, whereas they would vary
widely on occasional forks.
I extended testing down to 50-entry maps, to explore the rise in
service times that I was seeing at small scale. This is probably a
timing artifact, from too-short invocations, since I'm using
@Level.Invocation in the put() tests. To fix that, I enlarged the
unit of testing, by creating multiple, sometimes thousands, of maps
for the invocation to fill. This has also changed the test from
filling a previously-filled map, to filling a new, but sufficiently
sized map. The put()/remove() test now performs much worse (on a
more realistic scenario).
This also adds a couple tests for calling get() against a map that's
been filled only with put()s, or with a mix of put() and remove().
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Add an example benchmark for OpenHashMap.
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