llvm-project/llvm/lib/CodeGen/SpillPlacement.cpp

376 lines
12 KiB
C++

//===-- SpillPlacement.cpp - Optimal Spill Code Placement -----------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
//
// This file implements the spill code placement analysis.
//
// Each edge bundle corresponds to a node in a Hopfield network. Constraints on
// basic blocks are weighted by the block frequency and added to become the node
// bias.
//
// Transparent basic blocks have the variable live through, but don't care if it
// is spilled or in a register. These blocks become connections in the Hopfield
// network, again weighted by block frequency.
//
// The Hopfield network minimizes (possibly locally) its energy function:
//
// E = -sum_n V_n * ( B_n + sum_{n, m linked by b} V_m * F_b )
//
// The energy function represents the expected spill code execution frequency,
// or the cost of spilling. This is a Lyapunov function which never increases
// when a node is updated. It is guaranteed to converge to a local minimum.
//
//===----------------------------------------------------------------------===//
#include "SpillPlacement.h"
#include "llvm/ADT/BitVector.h"
#include "llvm/CodeGen/EdgeBundles.h"
#include "llvm/CodeGen/MachineBasicBlock.h"
#include "llvm/CodeGen/MachineBlockFrequencyInfo.h"
#include "llvm/CodeGen/MachineFunction.h"
#include "llvm/CodeGen/MachineLoopInfo.h"
#include "llvm/CodeGen/Passes.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/ManagedStatic.h"
using namespace llvm;
#define DEBUG_TYPE "spillplacement"
char SpillPlacement::ID = 0;
INITIALIZE_PASS_BEGIN(SpillPlacement, "spill-code-placement",
"Spill Code Placement Analysis", true, true)
INITIALIZE_PASS_DEPENDENCY(EdgeBundles)
INITIALIZE_PASS_DEPENDENCY(MachineLoopInfo)
INITIALIZE_PASS_END(SpillPlacement, "spill-code-placement",
"Spill Code Placement Analysis", true, true)
char &llvm::SpillPlacementID = SpillPlacement::ID;
void SpillPlacement::getAnalysisUsage(AnalysisUsage &AU) const {
AU.setPreservesAll();
AU.addRequired<MachineBlockFrequencyInfo>();
AU.addRequiredTransitive<EdgeBundles>();
AU.addRequiredTransitive<MachineLoopInfo>();
MachineFunctionPass::getAnalysisUsage(AU);
}
/// Node - Each edge bundle corresponds to a Hopfield node.
///
/// The node contains precomputed frequency data that only depends on the CFG,
/// but Bias and Links are computed each time placeSpills is called.
///
/// The node Value is positive when the variable should be in a register. The
/// value can change when linked nodes change, but convergence is very fast
/// because all weights are positive.
///
struct SpillPlacement::Node {
/// BiasN - Sum of blocks that prefer a spill.
BlockFrequency BiasN;
/// BiasP - Sum of blocks that prefer a register.
BlockFrequency BiasP;
/// Value - Output value of this node computed from the Bias and links.
/// This is always on of the values {-1, 0, 1}. A positive number means the
/// variable should go in a register through this bundle.
int Value;
typedef SmallVector<std::pair<BlockFrequency, unsigned>, 4> LinkVector;
/// Links - (Weight, BundleNo) for all transparent blocks connecting to other
/// bundles. The weights are all positive block frequencies.
LinkVector Links;
/// SumLinkWeights - Cached sum of the weights of all links + ThresHold.
BlockFrequency SumLinkWeights;
/// preferReg - Return true when this node prefers to be in a register.
bool preferReg() const {
// Undecided nodes (Value==0) go on the stack.
return Value > 0;
}
/// mustSpill - Return True if this node is so biased that it must spill.
bool mustSpill() const {
// We must spill if Bias < -sum(weights) or the MustSpill flag was set.
// BiasN is saturated when MustSpill is set, make sure this still returns
// true when the RHS saturates. Note that SumLinkWeights includes Threshold.
return BiasN >= BiasP + SumLinkWeights;
}
/// clear - Reset per-query data, but preserve frequencies that only depend on
// the CFG.
void clear(const BlockFrequency &Threshold) {
BiasN = BiasP = Value = 0;
SumLinkWeights = Threshold;
Links.clear();
}
/// addLink - Add a link to bundle b with weight w.
void addLink(unsigned b, BlockFrequency w) {
// Update cached sum.
SumLinkWeights += w;
// There can be multiple links to the same bundle, add them up.
for (LinkVector::iterator I = Links.begin(), E = Links.end(); I != E; ++I)
if (I->second == b) {
I->first += w;
return;
}
// This must be the first link to b.
Links.push_back(std::make_pair(w, b));
}
/// addBias - Bias this node.
void addBias(BlockFrequency freq, BorderConstraint direction) {
switch (direction) {
default:
break;
case PrefReg:
BiasP += freq;
break;
case PrefSpill:
BiasN += freq;
break;
case MustSpill:
BiasN = BlockFrequency::getMaxFrequency();
break;
}
}
/// update - Recompute Value from Bias and Links. Return true when node
/// preference changes.
bool update(const Node nodes[], const BlockFrequency &Threshold) {
// Compute the weighted sum of inputs.
BlockFrequency SumN = BiasN;
BlockFrequency SumP = BiasP;
for (LinkVector::iterator I = Links.begin(), E = Links.end(); I != E; ++I) {
if (nodes[I->second].Value == -1)
SumN += I->first;
else if (nodes[I->second].Value == 1)
SumP += I->first;
}
// Each weighted sum is going to be less than the total frequency of the
// bundle. Ideally, we should simply set Value = sign(SumP - SumN), but we
// will add a dead zone around 0 for two reasons:
//
// 1. It avoids arbitrary bias when all links are 0 as is possible during
// initial iterations.
// 2. It helps tame rounding errors when the links nominally sum to 0.
//
bool Before = preferReg();
if (SumN >= SumP + Threshold)
Value = -1;
else if (SumP >= SumN + Threshold)
Value = 1;
else
Value = 0;
return Before != preferReg();
}
void getDissentingNeighbors(SparseSet<unsigned> &List,
const Node nodes[]) const {
for (const auto &Elt : Links) {
unsigned n = Elt.second;
// Neighbors that already have the same value are not going to
// change because of this node changing.
if (Value != nodes[n].Value)
List.insert(n);
}
}
};
bool SpillPlacement::runOnMachineFunction(MachineFunction &mf) {
MF = &mf;
bundles = &getAnalysis<EdgeBundles>();
loops = &getAnalysis<MachineLoopInfo>();
assert(!nodes && "Leaking node array");
nodes = new Node[bundles->getNumBundles()];
TodoList.clear();
TodoList.setUniverse(bundles->getNumBundles());
// Compute total ingoing and outgoing block frequencies for all bundles.
BlockFrequencies.resize(mf.getNumBlockIDs());
MBFI = &getAnalysis<MachineBlockFrequencyInfo>();
setThreshold(MBFI->getEntryFreq());
for (auto &I : mf) {
unsigned Num = I.getNumber();
BlockFrequencies[Num] = MBFI->getBlockFreq(&I);
}
// We never change the function.
return false;
}
void SpillPlacement::releaseMemory() {
delete[] nodes;
nodes = nullptr;
TodoList.clear();
}
/// activate - mark node n as active if it wasn't already.
void SpillPlacement::activate(unsigned n) {
TodoList.insert(n);
if (ActiveNodes->test(n))
return;
ActiveNodes->set(n);
nodes[n].clear(Threshold);
// Very large bundles usually come from big switches, indirect branches,
// landing pads, or loops with many 'continue' statements. It is difficult to
// allocate registers when so many different blocks are involved.
//
// Give a small negative bias to large bundles such that a substantial
// fraction of the connected blocks need to be interested before we consider
// expanding the region through the bundle. This helps compile time by
// limiting the number of blocks visited and the number of links in the
// Hopfield network.
if (bundles->getBlocks(n).size() > 100) {
nodes[n].BiasP = 0;
nodes[n].BiasN = (MBFI->getEntryFreq() / 16);
}
}
/// \brief Set the threshold for a given entry frequency.
///
/// Set the threshold relative to \c Entry. Since the threshold is used as a
/// bound on the open interval (-Threshold;Threshold), 1 is the minimum
/// threshold.
void SpillPlacement::setThreshold(const BlockFrequency &Entry) {
// Apparently 2 is a good threshold when Entry==2^14, but we need to scale
// it. Divide by 2^13, rounding as appropriate.
uint64_t Freq = Entry.getFrequency();
uint64_t Scaled = (Freq >> 13) + bool(Freq & (1 << 12));
Threshold = std::max(UINT64_C(1), Scaled);
}
/// addConstraints - Compute node biases and weights from a set of constraints.
/// Set a bit in NodeMask for each active node.
void SpillPlacement::addConstraints(ArrayRef<BlockConstraint> LiveBlocks) {
for (ArrayRef<BlockConstraint>::iterator I = LiveBlocks.begin(),
E = LiveBlocks.end(); I != E; ++I) {
BlockFrequency Freq = BlockFrequencies[I->Number];
// Live-in to block?
if (I->Entry != DontCare) {
unsigned ib = bundles->getBundle(I->Number, 0);
activate(ib);
nodes[ib].addBias(Freq, I->Entry);
}
// Live-out from block?
if (I->Exit != DontCare) {
unsigned ob = bundles->getBundle(I->Number, 1);
activate(ob);
nodes[ob].addBias(Freq, I->Exit);
}
}
}
/// addPrefSpill - Same as addConstraints(PrefSpill)
void SpillPlacement::addPrefSpill(ArrayRef<unsigned> Blocks, bool Strong) {
for (ArrayRef<unsigned>::iterator I = Blocks.begin(), E = Blocks.end();
I != E; ++I) {
BlockFrequency Freq = BlockFrequencies[*I];
if (Strong)
Freq += Freq;
unsigned ib = bundles->getBundle(*I, 0);
unsigned ob = bundles->getBundle(*I, 1);
activate(ib);
activate(ob);
nodes[ib].addBias(Freq, PrefSpill);
nodes[ob].addBias(Freq, PrefSpill);
}
}
void SpillPlacement::addLinks(ArrayRef<unsigned> Links) {
for (ArrayRef<unsigned>::iterator I = Links.begin(), E = Links.end(); I != E;
++I) {
unsigned Number = *I;
unsigned ib = bundles->getBundle(Number, 0);
unsigned ob = bundles->getBundle(Number, 1);
// Ignore self-loops.
if (ib == ob)
continue;
activate(ib);
activate(ob);
BlockFrequency Freq = BlockFrequencies[Number];
nodes[ib].addLink(ob, Freq);
nodes[ob].addLink(ib, Freq);
}
}
bool SpillPlacement::scanActiveBundles() {
RecentPositive.clear();
for (int n = ActiveNodes->find_first(); n>=0; n = ActiveNodes->find_next(n)) {
update(n);
// A node that must spill, or a node without any links is not going to
// change its value ever again, so exclude it from iterations.
if (nodes[n].mustSpill())
continue;
if (nodes[n].preferReg())
RecentPositive.push_back(n);
}
return !RecentPositive.empty();
}
bool SpillPlacement::update(unsigned n) {
if (!nodes[n].update(nodes, Threshold))
return false;
nodes[n].getDissentingNeighbors(TodoList, nodes);
return true;
}
/// iterate - Repeatedly update the Hopfield nodes until stability or the
/// maximum number of iterations is reached.
void SpillPlacement::iterate() {
// We do not need to push those node in the todolist.
// They are already been proceeded as part of the previous iteration.
RecentPositive.clear();
// Since the last iteration, the todolist have been augmented by calls
// to addConstraints, addLinks, and co.
// Update the network energy starting at this new frontier.
// The call to ::update will add the nodes that changed into the todolist.
unsigned Limit = bundles->getNumBundles() * 10;
while(Limit-- > 0 && !TodoList.empty()) {
unsigned n = TodoList.pop_back_val();
if (!update(n))
continue;
if (nodes[n].preferReg())
RecentPositive.push_back(n);
}
}
void SpillPlacement::prepare(BitVector &RegBundles) {
RecentPositive.clear();
TodoList.clear();
// Reuse RegBundles as our ActiveNodes vector.
ActiveNodes = &RegBundles;
ActiveNodes->clear();
ActiveNodes->resize(bundles->getNumBundles());
}
bool
SpillPlacement::finish() {
assert(ActiveNodes && "Call prepare() first");
// Write preferences back to ActiveNodes.
bool Perfect = true;
for (int n = ActiveNodes->find_first(); n>=0; n = ActiveNodes->find_next(n))
if (!nodes[n].preferReg()) {
ActiveNodes->reset(n);
Perfect = false;
}
ActiveNodes = nullptr;
return Perfect;
}