txgraph: track amount of work done in linearization (preparation)

Author: sipa

src/bench/cluster_linearize.cpp

@@ -229,8 +229,8 @@ void BenchLinearizeOptimally(benchmark::Bench& bench, const std::array<uint8_t,
         reader >> Using<DepGraphFormatter>(depgraph);
         uint64_t rng_seed = 0;
         bench.run([&] {
-            auto res = Linearize(depgraph, /*max_iterations=*/10000000, rng_seed++);
-            assert(res.second);
+            auto [_lin, optimal, _cost] = Linearize(depgraph, /*max_iterations=*/10000000, rng_seed++);
+            assert(optimal);
         });
     };
 

src/cluster_linearize.h

@@ -1030,19 +1030,20 @@ class SearchCandidateFinder
  *                                linearize.
  * @param[in] old_linearization   An existing linearization for the cluster (which must be
  *                                topologically valid), or empty.
- * @return                        A pair of:
+ * @return                        A tuple of:
  *                                - The resulting linearization. It is guaranteed to be at least as
  *                                  good (in the feerate diagram sense) as old_linearization.
  *                                - A boolean indicating whether the result is guaranteed to be
  *                                  optimal.
+ *                                - How many optimization steps were actually performed.
  *
  * Complexity: possibly O(N * min(max_iterations + N, sqrt(2^N))) where N=depgraph.TxCount().
  */
 template<typename SetType>
-std::pair<std::vector<DepGraphIndex>, bool> Linearize(const DepGraph<SetType>& depgraph, uint64_t max_iterations, uint64_t rng_seed, std::span<const DepGraphIndex> old_linearization = {}) noexcept
+std::tuple<std::vector<DepGraphIndex>, bool, uint64_t> Linearize(const DepGraph<SetType>& depgraph, uint64_t max_iterations, uint64_t rng_seed, std::span<const DepGraphIndex> old_linearization = {}) noexcept
 {
     Assume(old_linearization.empty() || old_linearization.size() == depgraph.TxCount());
-    if (depgraph.TxCount() == 0) return {{}, true};
+    if (depgraph.TxCount() == 0) return {{}, true, 0};
 
     uint64_t iterations_left = max_iterations;
     std::vector<DepGraphIndex> linearization;
@@ -1113,7 +1114,7 @@ std::pair<std::vector<DepGraphIndex>, bool> Linearize(const DepGraph<SetType>& d
         }
     }
 
-    return {std::move(linearization), optimal};
+    return {std::move(linearization), optimal, max_iterations - iterations_left};
 }
 
 /** Improve a given linearization.

src/test/fuzz/cluster_linearize.cpp

@@ -1154,7 +1154,8 @@ FUZZ_TARGET(clusterlin_linearize)
 
     // Invoke Linearize().
     iter_count &= 0x7ffff;
-    auto [linearization, optimal] = Linearize(depgraph, iter_count, rng_seed, old_linearization);
+    auto [linearization, optimal, cost] = Linearize(depgraph, iter_count, rng_seed, old_linearization);
+    assert(cost <= iter_count);
     SanityCheck(depgraph, linearization);
     auto chunking = ChunkLinearization(depgraph, linearization);
 
@@ -1322,7 +1323,7 @@ FUZZ_TARGET(clusterlin_postlinearize_tree)
 
     // Try to find an even better linearization directly. This must not change the diagram for the
     // same reason.
-    auto [opt_linearization, _optimal] = Linearize(depgraph_tree, 100000, rng_seed, post_linearization);
+    auto [opt_linearization, _optimal, _cost] = Linearize(depgraph_tree, 100000, rng_seed, post_linearization);
     auto opt_chunking = ChunkLinearization(depgraph_tree, opt_linearization);
     auto cmp_opt = CompareChunks(opt_chunking, post_chunking);
     assert(cmp_opt == 0);

src/txgraph.cpp

@@ -189,8 +189,8 @@ class Cluster
     void Merge(TxGraphImpl& graph, Cluster& cluster) noexcept;
     /** Given a span of (parent, child) pairs that all belong to this Cluster, apply them. */
     void ApplyDependencies(TxGraphImpl& graph, std::span<std::pair<GraphIndex, GraphIndex>> to_apply) noexcept;
-    /** Improve the linearization of this Cluster. */
-    void Relinearize(TxGraphImpl& graph, uint64_t max_iters) noexcept;
+    /** Improve the linearization of this Cluster. Returns how much work was performed. */
+    uint64_t Relinearize(TxGraphImpl& graph, uint64_t max_iters) noexcept;
     /** For every chunk in the cluster, append its FeeFrac to ret. */
     void AppendChunkFeerates(std::vector<FeeFrac>& ret) const noexcept;
     /** Add a TrimTxData entry (filling m_chunk_feerate, m_index, m_tx_size) for every
@@ -1651,15 +1651,15 @@ void TxGraphImpl::ApplyDependencies(int level) noexcept
     clusterset.m_group_data = GroupData{};
 }
 
-void Cluster::Relinearize(TxGraphImpl& graph, uint64_t max_iters) noexcept
+uint64_t Cluster::Relinearize(TxGraphImpl& graph, uint64_t max_iters) noexcept
 {
     // We can only relinearize Clusters that do not need splitting.
     Assume(!NeedsSplitting());
     // No work is required for Clusters which are already optimally linearized.
-    if (IsOptimal()) return;
+    if (IsOptimal()) return 0;
     // Invoke the actual linearization algorithm (passing in the existing one).
     uint64_t rng_seed = graph.m_rng.rand64();
-    auto [linearization, optimal] = Linearize(m_depgraph, max_iters, rng_seed, m_linearization);
+    auto [linearization, optimal, cost] = Linearize(m_depgraph, max_iters, rng_seed, m_linearization);
     // Postlinearize if the result isn't optimal already. This guarantees (among other things)
     // that the chunks of the resulting linearization are all connected.
     if (!optimal) PostLinearize(m_depgraph, linearization);
@@ -1670,6 +1670,7 @@ void Cluster::Relinearize(TxGraphImpl& graph, uint64_t max_iters) noexcept
     graph.SetClusterQuality(m_level, m_quality, m_setindex, new_quality);
     // Update the Entry objects.
     Updated(graph);
+    return cost;
 }
 
 void TxGraphImpl::MakeAcceptable(Cluster& cluster) noexcept