With MergeLinearizations() gone and the LIMO-based Linearize() replaced by SFL, we do not
need a class (LinearizationChunking) that can maintain an incrementally-improving chunk
set anymore.
Replace it with a function (ChunkLinearizationInfo) that just computes the chunks as
SetInfos once, and returns them as a vector. This simplifies several call sites too.
This places equal-feerate chunks (with no dependencies between them) in random
order in the linearization output, hiding information about DepGraph insertion
order from the output. Likewise, it randomizes the order of transactions within
chunks for the same reason.
This introduces a local RNG inside the SFL state, which is used to randomize
various decisions inside the algorithm, in order to make it hard to create
pathological clusters which predictably have bad performance.
The decisions being randomized are:
* When deciding what chunk to attempt to split, the queue order is
randomized.
* When deciding which dependency to split on, a uniformly random one is
chosen among those with higher top feerate than bottom feerate within
the chosen chunk.
* When deciding which chunks to merge, a uniformly random one among those
with the higher feerate difference is picked.
* When merging two chunks, a uniformly random dependency between them is
now activated.
* When making the state topological, the queue of chunks to process is
randomized.
This introduces a queue of chunks that still need processing, in both
MakeTopological() and OptimizationStep(). This is simultaneously:
* A preparation for introducing randomization, by allowing permuting the
queue.
* An improvement to the fairness of suboptimal solutions, by distributing
the work more fairly over chunks.
* An optimization, by avoiding retrying chunks over and over again which
are already known to be optimal.
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.
This adds a data structure representing the optimization state for the spanning-forest
linearization algorithm (SFL), plus a fuzz test for its correctness.
This is preparation for switching over Linearize() to use this algorithm.
See https://delvingbitcoin.org/t/spanning-forest-cluster-linearization/1419 for
a description of the algorithm.
This can be reproduced according to the developer notes with something
like
( cd ./src/ && ../contrib/devtools/run-clang-tidy.py -p ../bld-cmake -fix -j $(nproc) )
Also, the header related changes were done manually.
This abstracts out the finding of the connected component that includes
a given element from FindConnectedComponent (which just finds any connected
component).
Use this in the txgraph fuzz test, which was effectively reimplementing this
logic. At the same time, improve its performance by replacing a vector with a
set.
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 function takes an existing ordering for transactions in a DepGraph, and
makes it a valid linearization for it (i.e., topological). Any topological
prefix of the input remains untouched.
This commits introduces support in DepGraph for the transaction positions to be
non-continuous. Specifically, it adds:
* DepGraph::RemoveTransactions which removes 0 or more positions from a DepGraph.
* DepGraph::Positions() to get a set of which positions are in use.
* DepGraph::PositionRange() to get the highest used position in a DepGraph + 1.
In addition, it extends the DepGraphFormatter format to support holes in a
compatible way (it serializes non-holey DepGraphs identically to the old code,
and deserializes them the same way)
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>
A fuzz test already relies on these operations, and a future commit will need
the same logic too. Therefore, abstract them out into proper member functions,
with proper testing.
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.
