This replaces the existing LIMO linearization algorithm (which internally uses
ancestor set finding and candidate set finding) with the much more performant
spanning-forest linearization algorithm.
This removes the old candidate-set search algorithm, and several of its tests,
benchmarks, and needed utility code.
The worst case time per cost is similar to the previous algorithm, so
ACCEPTABLE_ITERS is unchanged.
Since cluster_linearize.h does not actually have a Cluster type anymore, it is more
appropriate to rename the index type to DepGraphIndex.
-BEGIN VERIFY SCRIPT-
sed -i 's/Data type to represent transaction indices in clusters./Data type to represent transaction indices in DepGraphs and the clusters they represent./' $(git grep -l 'using ClusterIndex')
sed -i 's|\<ClusterIndex\>|DepGraphIndex|g' $(git grep -l 'ClusterIndex')
-END VERIFY SCRIPT-
This changes DepGraph::AddDependency into DepGraph::AddDependencies, which takes
in a single child, but a set of parent transactions, making them all dependencies
at once.
This is important for performance. N transactions can have O(N^2) parents combined,
so constructing a full DepGraph using just AddDependency (which is O(N) on its own)
could take O(N^3) time, while doing the same with AddDependencies (also O(N) on its
own) only takes O(N^2).
Notably, this matters for DepGraphFormatter::Unser, which goes from O(N^3) to O(N^2).
Co-Authored-By: Greg Sanders <gsanders87@gmail.com>
Empirically, this approach seems to be more efficient in common real-life
clusters, and does not change the worst case.
Co-Authored-By: Suhas Daftuar <sdaftuar@gmail.com>
Automatically add topologically-valid subsets of the potential set pot
to inc. It can be proven that these must be part of the best reachable
topologically-valid set from that work item.
This is a crucial optimization that (apparently) reduces the maximum
number of iterations from ~2^(N-1) to ~sqrt(2^N).
Co-Authored-By: Suhas Daftuar <sdaftuar@gmail.com>
Add benchmarks for known bad graphs for the purpose of search (as
an upper bound on work per search iterations) and ancestor sorting
(as an upper bound on linearization work with no search iterations).
