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>
Keep track of which transactions in the graph have an individual
feerate that is better than the best included set so far. Others do not
need to be added to the pot set, as they cannot possibly help beating
best.
In each work item, keep track of a conservative overestimate of the best
possible feerate that can be reached from it, and then use these to avoid
exploring hopeless work items.
Add a DepGraph(depgraph, reordering) function that constructs a new DepGraph
corresponding to an old one, but with its transactions is a modified order
(given as a vector from old to new positions).
Also use this reordering feature inside DepGraphFormatter::Unser, which needs
a small modification so that its reordering mapping is old-to-new (rather than
the new-to-old it used before).
Before this commit, the worst case for linearization involves clusters which
break apart in several smaller components after the first candidate is
included in the output linearization.
Address this by never considering work items that span multiple components
of what remains of the cluster.
When the transactions being marked done exactly match the first chunk of
what remains of the linearization, we can just remember to skip that
chunk instead of computing a full rechunking.
Further, chop off prefixes of the input linearization that are already done,
so they don't need to be reconsidered for further rechunkings.
It encapsulates a given linearization in chunked form, permitting arbitrary
subsets of transactions to be removed from the linearization. Its purpose
is adding the Intersect function, which is a crucial operation that will
be used in a further commit to make Linearize improve existing linearizations.
Switch to BFS exploration of the search tree in SearchCandidateFinder
instead of DFS exploration. This appears to behave better for real
world clusters.
As BFS has the downside of needing far larger search queues, switch
back to DFS temporarily when the queue grows too large.
This adds a first version of the overall linearization interface, which given
a DepGraph constructs a good linearization, by incrementally including good
candidate sets (found using AncestorCandidateFinder and SearchCandidateFinder).
This primarily adds the DepGraph class, which encapsulates precomputed
ancestor/descendant information for a given transaction cluster, with a
number of utility features (inspectors for set feerates, computing
reduced parents/children, adding transactions, adding dependencies), which
will become needed in future commits.
