1 //===- CallGraphSort.cpp --------------------------------------------------===//
2 //
3 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
4 // See https://llvm.org/LICENSE.txt for license information.
5 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
6 //
7 //===----------------------------------------------------------------------===//
8 ///
9 /// This is based on the ELF port, see ELF/CallGraphSort.cpp for the details
10 /// about the algorithm.
11 ///
12 //===----------------------------------------------------------------------===//
13
14 #include "CallGraphSort.h"
15 #include "COFFLinkerContext.h"
16 #include "InputFiles.h"
17 #include "SymbolTable.h"
18 #include "Symbols.h"
19
20 #include <numeric>
21
22 using namespace llvm;
23 using namespace lld;
24 using namespace lld::coff;
25
26 namespace {
27 struct Edge {
28 int from;
29 uint64_t weight;
30 };
31
32 struct Cluster {
Cluster__anone43d6a6e0111::Cluster33 Cluster(int sec, size_t s) : next(sec), prev(sec), size(s) {}
34
getDensity__anone43d6a6e0111::Cluster35 double getDensity() const {
36 if (size == 0)
37 return 0;
38 return double(weight) / double(size);
39 }
40
41 int next;
42 int prev;
43 uint64_t size;
44 uint64_t weight = 0;
45 uint64_t initialWeight = 0;
46 Edge bestPred = {-1, 0};
47 };
48
49 class CallGraphSort {
50 public:
51 CallGraphSort(COFFLinkerContext &ctx);
52
53 DenseMap<const SectionChunk *, int> run();
54
55 private:
56 std::vector<Cluster> clusters;
57 std::vector<const SectionChunk *> sections;
58
59 COFFLinkerContext &ctx;
60 };
61
62 // Maximum amount the combined cluster density can be worse than the original
63 // cluster to consider merging.
64 constexpr int MAX_DENSITY_DEGRADATION = 8;
65
66 // Maximum cluster size in bytes.
67 constexpr uint64_t MAX_CLUSTER_SIZE = 1024 * 1024;
68 } // end anonymous namespace
69
70 using SectionPair = std::pair<const SectionChunk *, const SectionChunk *>;
71
72 // Take the edge list in Config->CallGraphProfile, resolve symbol names to
73 // Symbols, and generate a graph between InputSections with the provided
74 // weights.
CallGraphSort(COFFLinkerContext & ctx)75 CallGraphSort::CallGraphSort(COFFLinkerContext &ctx) : ctx(ctx) {
76 const MapVector<SectionPair, uint64_t> &profile = ctx.config.callGraphProfile;
77 DenseMap<const SectionChunk *, int> secToCluster;
78
79 auto getOrCreateNode = [&](const SectionChunk *isec) -> int {
80 auto res = secToCluster.try_emplace(isec, clusters.size());
81 if (res.second) {
82 sections.push_back(isec);
83 clusters.emplace_back(clusters.size(), isec->getSize());
84 }
85 return res.first->second;
86 };
87
88 // Create the graph.
89 for (const std::pair<SectionPair, uint64_t> &c : profile) {
90 const auto *fromSec = cast<SectionChunk>(c.first.first->repl);
91 const auto *toSec = cast<SectionChunk>(c.first.second->repl);
92 uint64_t weight = c.second;
93
94 // Ignore edges between input sections belonging to different output
95 // sections. This is done because otherwise we would end up with clusters
96 // containing input sections that can't actually be placed adjacently in the
97 // output. This messes with the cluster size and density calculations. We
98 // would also end up moving input sections in other output sections without
99 // moving them closer to what calls them.
100 if (ctx.getOutputSection(fromSec) != ctx.getOutputSection(toSec))
101 continue;
102
103 int from = getOrCreateNode(fromSec);
104 int to = getOrCreateNode(toSec);
105
106 clusters[to].weight += weight;
107
108 if (from == to)
109 continue;
110
111 // Remember the best edge.
112 Cluster &toC = clusters[to];
113 if (toC.bestPred.from == -1 || toC.bestPred.weight < weight) {
114 toC.bestPred.from = from;
115 toC.bestPred.weight = weight;
116 }
117 }
118 for (Cluster &c : clusters)
119 c.initialWeight = c.weight;
120 }
121
122 // It's bad to merge clusters which would degrade the density too much.
isNewDensityBad(Cluster & a,Cluster & b)123 static bool isNewDensityBad(Cluster &a, Cluster &b) {
124 double newDensity = double(a.weight + b.weight) / double(a.size + b.size);
125 return newDensity < a.getDensity() / MAX_DENSITY_DEGRADATION;
126 }
127
128 // Find the leader of V's belonged cluster (represented as an equivalence
129 // class). We apply union-find path-halving technique (simple to implement) in
130 // the meantime as it decreases depths and the time complexity.
getLeader(std::vector<int> & leaders,int v)131 static int getLeader(std::vector<int> &leaders, int v) {
132 while (leaders[v] != v) {
133 leaders[v] = leaders[leaders[v]];
134 v = leaders[v];
135 }
136 return v;
137 }
138
mergeClusters(std::vector<Cluster> & cs,Cluster & into,int intoIdx,Cluster & from,int fromIdx)139 static void mergeClusters(std::vector<Cluster> &cs, Cluster &into, int intoIdx,
140 Cluster &from, int fromIdx) {
141 int tail1 = into.prev, tail2 = from.prev;
142 into.prev = tail2;
143 cs[tail2].next = intoIdx;
144 from.prev = tail1;
145 cs[tail1].next = fromIdx;
146 into.size += from.size;
147 into.weight += from.weight;
148 from.size = 0;
149 from.weight = 0;
150 }
151
152 // Group InputSections into clusters using the Call-Chain Clustering heuristic
153 // then sort the clusters by density.
run()154 DenseMap<const SectionChunk *, int> CallGraphSort::run() {
155 std::vector<int> sorted(clusters.size());
156 std::vector<int> leaders(clusters.size());
157
158 std::iota(leaders.begin(), leaders.end(), 0);
159 std::iota(sorted.begin(), sorted.end(), 0);
160 llvm::stable_sort(sorted, [&](int a, int b) {
161 return clusters[a].getDensity() > clusters[b].getDensity();
162 });
163
164 for (int l : sorted) {
165 // The cluster index is the same as the index of its leader here because
166 // clusters[L] has not been merged into another cluster yet.
167 Cluster &c = clusters[l];
168
169 // Don't consider merging if the edge is unlikely.
170 if (c.bestPred.from == -1 || c.bestPred.weight * 10 <= c.initialWeight)
171 continue;
172
173 int predL = getLeader(leaders, c.bestPred.from);
174 if (l == predL)
175 continue;
176
177 Cluster *predC = &clusters[predL];
178 if (c.size + predC->size > MAX_CLUSTER_SIZE)
179 continue;
180
181 if (isNewDensityBad(*predC, c))
182 continue;
183
184 leaders[l] = predL;
185 mergeClusters(clusters, *predC, predL, c, l);
186 }
187
188 // Sort remaining non-empty clusters by density.
189 sorted.clear();
190 for (int i = 0, e = (int)clusters.size(); i != e; ++i)
191 if (clusters[i].size > 0)
192 sorted.push_back(i);
193 llvm::stable_sort(sorted, [&](int a, int b) {
194 return clusters[a].getDensity() > clusters[b].getDensity();
195 });
196
197 DenseMap<const SectionChunk *, int> orderMap;
198 // Sections will be sorted by increasing order. Absent sections will have
199 // priority 0 and be placed at the end of sections.
200 int curOrder = INT_MIN;
201 for (int leader : sorted) {
202 for (int i = leader;;) {
203 orderMap[sections[i]] = curOrder++;
204 i = clusters[i].next;
205 if (i == leader)
206 break;
207 }
208 }
209 if (!ctx.config.printSymbolOrder.empty()) {
210 std::error_code ec;
211 raw_fd_ostream os(ctx.config.printSymbolOrder, ec, sys::fs::OF_None);
212 if (ec) {
213 Err(ctx) << "cannot open " << ctx.config.printSymbolOrder << ": "
214 << ec.message();
215 return orderMap;
216 }
217 // Print the symbols ordered by C3, in the order of increasing curOrder
218 // Instead of sorting all the orderMap, just repeat the loops above.
219 for (int leader : sorted)
220 for (int i = leader;;) {
221 const SectionChunk *sc = sections[i];
222
223 // Search all the symbols in the file of the section
224 // and find out a DefinedCOFF symbol with name that is within the
225 // section.
226 for (Symbol *sym : sc->file->getSymbols())
227 if (auto *d = dyn_cast_or_null<DefinedCOFF>(sym))
228 // Filter out non-COMDAT symbols and section symbols.
229 if (d->isCOMDAT && !d->getCOFFSymbol().isSection() &&
230 sc == d->getChunk())
231 os << sym->getName() << "\n";
232 i = clusters[i].next;
233 if (i == leader)
234 break;
235 }
236 }
237
238 return orderMap;
239 }
240
241 // Sort sections by the profile data provided by /call-graph-ordering-file
242 //
243 // This first builds a call graph based on the profile data then merges sections
244 // according to the C³ heuristic. All clusters are then sorted by a density
245 // metric to further improve locality.
246 DenseMap<const SectionChunk *, int>
computeCallGraphProfileOrder(COFFLinkerContext & ctx)247 coff::computeCallGraphProfileOrder(COFFLinkerContext &ctx) {
248 return CallGraphSort(ctx).run();
249 }
250