llvm-project/lld/ELF/CallGraphSort.cpp

270 lines
8.8 KiB
C++

//===- CallGraphSort.cpp --------------------------------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
///
/// Implementation of Call-Chain Clustering from: Optimizing Function Placement
/// for Large-Scale Data-Center Applications
/// https://research.fb.com/wp-content/uploads/2017/01/cgo2017-hfsort-final1.pdf
///
/// The goal of this algorithm is to improve runtime performance of the final
/// executable by arranging code sections such that page table and i-cache
/// misses are minimized.
///
/// Definitions:
/// * Cluster
/// * An ordered list of input sections which are laid out as a unit. At the
/// beginning of the algorithm each input section has its own cluster and
/// the weight of the cluster is the sum of the weight of all incoming
/// edges.
/// * Call-Chain Clustering (C³) Heuristic
/// * Defines when and how clusters are combined. Pick the highest weighted
/// input section then add it to its most likely predecessor if it wouldn't
/// penalize it too much.
/// * Density
/// * The weight of the cluster divided by the size of the cluster. This is a
/// proxy for the amount of execution time spent per byte of the cluster.
///
/// It does so given a call graph profile by the following:
/// * Build a weighted call graph from the call graph profile
/// * Sort input sections by weight
/// * For each input section starting with the highest weight
/// * Find its most likely predecessor cluster
/// * Check if the combined cluster would be too large, or would have too low
/// a density.
/// * If not, then combine the clusters.
/// * Sort non-empty clusters by density
///
//===----------------------------------------------------------------------===//
#include "CallGraphSort.h"
#include "OutputSections.h"
#include "SymbolTable.h"
#include "Symbols.h"
#include <numeric>
using namespace llvm;
using namespace lld;
using namespace lld::elf;
namespace {
struct Edge {
int from;
uint64_t weight;
};
struct Cluster {
Cluster(int sec, size_t s) : next(sec), prev(sec), size(s) {}
double getDensity() const {
if (size == 0)
return 0;
return double(weight) / double(size);
}
int next;
int prev;
size_t size = 0;
uint64_t weight = 0;
uint64_t initialWeight = 0;
Edge bestPred = {-1, 0};
};
class CallGraphSort {
public:
CallGraphSort();
DenseMap<const InputSectionBase *, int> run();
private:
std::vector<Cluster> clusters;
std::vector<const InputSectionBase *> sections;
};
// Maximum amount the combined cluster density can be worse than the original
// cluster to consider merging.
constexpr int MAX_DENSITY_DEGRADATION = 8;
// Maximum cluster size in bytes.
constexpr uint64_t MAX_CLUSTER_SIZE = 1024 * 1024;
} // end anonymous namespace
using SectionPair =
std::pair<const InputSectionBase *, const InputSectionBase *>;
// Take the edge list in Config->CallGraphProfile, resolve symbol names to
// Symbols, and generate a graph between InputSections with the provided
// weights.
CallGraphSort::CallGraphSort() {
MapVector<SectionPair, uint64_t> &profile = config->callGraphProfile;
DenseMap<const InputSectionBase *, int> secToCluster;
auto getOrCreateNode = [&](const InputSectionBase *isec) -> int {
auto res = secToCluster.try_emplace(isec, clusters.size());
if (res.second) {
sections.push_back(isec);
clusters.emplace_back(clusters.size(), isec->getSize());
}
return res.first->second;
};
// Create the graph.
for (std::pair<SectionPair, uint64_t> &c : profile) {
const auto *fromSB = cast<InputSectionBase>(c.first.first->repl);
const auto *toSB = cast<InputSectionBase>(c.first.second->repl);
uint64_t weight = c.second;
// Ignore edges between input sections belonging to different output
// sections. This is done because otherwise we would end up with clusters
// containing input sections that can't actually be placed adjacently in the
// output. This messes with the cluster size and density calculations. We
// would also end up moving input sections in other output sections without
// moving them closer to what calls them.
if (fromSB->getOutputSection() != toSB->getOutputSection())
continue;
int from = getOrCreateNode(fromSB);
int to = getOrCreateNode(toSB);
clusters[to].weight += weight;
if (from == to)
continue;
// Remember the best edge.
Cluster &toC = clusters[to];
if (toC.bestPred.from == -1 || toC.bestPred.weight < weight) {
toC.bestPred.from = from;
toC.bestPred.weight = weight;
}
}
for (Cluster &c : clusters)
c.initialWeight = c.weight;
}
// It's bad to merge clusters which would degrade the density too much.
static bool isNewDensityBad(Cluster &a, Cluster &b) {
double newDensity = double(a.weight + b.weight) / double(a.size + b.size);
return newDensity < a.getDensity() / MAX_DENSITY_DEGRADATION;
}
// Find the leader of V's belonged cluster (represented as an equivalence
// class). We apply union-find path-halving technique (simple to implement) in
// the meantime as it decreases depths and the time complexity.
static int getLeader(std::vector<int> &leaders, int v) {
while (leaders[v] != v) {
leaders[v] = leaders[leaders[v]];
v = leaders[v];
}
return v;
}
static void mergeClusters(std::vector<Cluster> &cs, Cluster &into, int intoIdx,
Cluster &from, int fromIdx) {
int tail1 = into.prev, tail2 = from.prev;
into.prev = tail2;
cs[tail2].next = intoIdx;
from.prev = tail1;
cs[tail1].next = fromIdx;
into.size += from.size;
into.weight += from.weight;
from.size = 0;
from.weight = 0;
}
// Group InputSections into clusters using the Call-Chain Clustering heuristic
// then sort the clusters by density.
DenseMap<const InputSectionBase *, int> CallGraphSort::run() {
std::vector<int> sorted(clusters.size());
std::vector<int> leaders(clusters.size());
std::iota(leaders.begin(), leaders.end(), 0);
std::iota(sorted.begin(), sorted.end(), 0);
llvm::stable_sort(sorted, [&](int a, int b) {
return clusters[a].getDensity() > clusters[b].getDensity();
});
for (int l : sorted) {
// The cluster index is the same as the index of its leader here because
// clusters[L] has not been merged into another cluster yet.
Cluster &c = clusters[l];
// Don't consider merging if the edge is unlikely.
if (c.bestPred.from == -1 || c.bestPred.weight * 10 <= c.initialWeight)
continue;
int predL = getLeader(leaders, c.bestPred.from);
if (l == predL)
continue;
Cluster *predC = &clusters[predL];
if (c.size + predC->size > MAX_CLUSTER_SIZE)
continue;
if (isNewDensityBad(*predC, c))
continue;
leaders[l] = predL;
mergeClusters(clusters, *predC, predL, c, l);
}
// Sort remaining non-empty clusters by density.
sorted.clear();
for (int i = 0, e = (int)clusters.size(); i != e; ++i)
if (clusters[i].size > 0)
sorted.push_back(i);
llvm::stable_sort(sorted, [&](int a, int b) {
return clusters[a].getDensity() > clusters[b].getDensity();
});
DenseMap<const InputSectionBase *, int> orderMap;
int curOrder = 1;
for (int leader : sorted)
for (int i = leader;;) {
orderMap[sections[i]] = curOrder++;
i = clusters[i].next;
if (i == leader)
break;
}
if (!config->printSymbolOrder.empty()) {
std::error_code ec;
raw_fd_ostream os(config->printSymbolOrder, ec, sys::fs::OF_None);
if (ec) {
error("cannot open " + config->printSymbolOrder + ": " + ec.message());
return orderMap;
}
// Print the symbols ordered by C3, in the order of increasing curOrder
// Instead of sorting all the orderMap, just repeat the loops above.
for (int leader : sorted)
for (int i = leader;;) {
// Search all the symbols in the file of the section
// and find out a Defined symbol with name that is within the section.
for (Symbol *sym : sections[i]->file->getSymbols())
if (!sym->isSection()) // Filter out section-type symbols here.
if (auto *d = dyn_cast<Defined>(sym))
if (sections[i] == d->section)
os << sym->getName() << "\n";
i = clusters[i].next;
if (i == leader)
break;
}
}
return orderMap;
}
// Sort sections by the profile data provided by -callgraph-profile-file
//
// This first builds a call graph based on the profile data then merges sections
// according to the C³ heuristic. All clusters are then sorted by a density
// metric to further improve locality.
DenseMap<const InputSectionBase *, int> elf::computeCallGraphProfileOrder() {
return CallGraphSort().run();
}