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//===-- 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.
//
//===----------------------------------------------------------------------===//
#define DEBUG_TYPE "spillplacement"
#include "SpillPlacement.h"
#include "llvm/CodeGen/EdgeBundles.h"
#include "llvm/CodeGen/LiveIntervalAnalysis.h"
#include "llvm/CodeGen/MachineBasicBlock.h"
#include "llvm/CodeGen/MachineFunction.h"
#include "llvm/CodeGen/MachineLoopInfo.h"
#include "llvm/CodeGen/Passes.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/Format.h"
using namespace llvm;
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.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 {
/// Scale - Inverse block frequency feeding into[0] or out of[1] the bundle.
/// Ideally, these two numbers should be identical, but inaccuracies in the
/// block frequency estimates means that we need to normalize ingoing and
/// outgoing frequencies separately so they are commensurate.
float Scale[2];
/// Bias - Normalized contributions from non-transparent blocks.
/// A bundle connected to a MustSpill block has a huge negative bias,
/// otherwise it is a number in the range [-2;2].
float Bias;
/// Value - Output value of this node computed from the Bias and links.
/// This is always in the range [-1;1]. A positive number means the variable
/// should go in a register through this bundle.
float Value;
typedef SmallVector<std::pair<float, unsigned>, 4> LinkVector;
/// Links - (Weight, BundleNo) for all transparent blocks connecting to other
/// bundles. The weights are all positive and add up to at most 2, weights
/// from ingoing and outgoing nodes separately add up to a most 1. The weight
/// sum can be less than 2 when the variable is not live into / out of some
/// connected basic blocks.
LinkVector Links;
/// 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 {
// Actually, we must spill if Bias < sum(weights).
// It may be worth it to compute the weight sum here?
return Bias < -2.0f;
}
/// Node - Create a blank Node.
Node() {
Scale[0] = Scale[1] = 0;
}
/// clear - Reset per-query data, but preserve frequencies that only depend on
// the CFG.
void clear() {
Bias = Value = 0;
Links.clear();
}
/// addLink - Add a link to bundle b with weight w.
/// out=0 for an ingoing link, and 1 for an outgoing link.
void addLink(unsigned b, float w, bool out) {
// Normalize w relative to all connected blocks from that direction.
w *= Scale[out];
// 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 from an ingoing[0] or outgoing[1] link.
/// Return the change to the total number of positive biases.
void addBias(float w, bool out) {
// Normalize w relative to all connected blocks from that direction.
w *= Scale[out];
Bias += w;
}
/// update - Recompute Value from Bias and Links. Return true when node
/// preference changes.
bool update(const Node nodes[]) {
// Compute the weighted sum of inputs.
float Sum = Bias;
for (LinkVector::iterator I = Links.begin(), E = Links.end(); I != E; ++I)
Sum += I->first * nodes[I->second].Value;
// The weighted sum is going to be in the range [-2;2]. Ideally, we should
// simply set Value = sign(Sum), 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.
const float Thres = 1e-4f;
bool Before = preferReg();
if (Sum < -Thres)
Value = -1;
else if (Sum > Thres)
Value = 1;
else
Value = 0;
return Before != preferReg();
}
};
bool SpillPlacement::runOnMachineFunction(MachineFunction &mf) {
MF = &mf;
bundles = &getAnalysis<EdgeBundles>();
loops = &getAnalysis<MachineLoopInfo>();
assert(!nodes && "Leaking node array");
nodes = new Node[bundles->getNumBundles()];
// Compute total ingoing and outgoing block frequencies for all bundles.
BlockFrequency.resize(mf.getNumBlockIDs());
for (MachineFunction::iterator I = mf.begin(), E = mf.end(); I != E; ++I) {
float Freq = LiveIntervals::getSpillWeight(true, false,
loops->getLoopDepth(I));
unsigned Num = I->getNumber();
BlockFrequency[Num] = Freq;
nodes[bundles->getBundle(Num, 1)].Scale[0] += Freq;
nodes[bundles->getBundle(Num, 0)].Scale[1] += Freq;
}
// Scales are reciprocal frequencies.
for (unsigned i = 0, e = bundles->getNumBundles(); i != e; ++i)
for (unsigned d = 0; d != 2; ++d)
if (nodes[i].Scale[d] > 0)
nodes[i].Scale[d] = 1 / nodes[i].Scale[d];
// We never change the function.
return false;
}
void SpillPlacement::releaseMemory() {
delete[] nodes;
nodes = 0;
}
/// activate - mark node n as active if it wasn't already.
void SpillPlacement::activate(unsigned n) {
if (ActiveNodes->test(n))
return;
ActiveNodes->set(n);
nodes[n].clear();
// 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 1/32 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].Bias = -0.0625f;
}
/// 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) {
float Freq = getBlockFrequency(I->Number);
const float Bias[] = {
0, // DontCare,
1, // PrefReg,
-1, // PrefSpill
0, // PrefBoth
-HUGE_VALF // MustSpill
};
// Live-in to block?
if (I->Entry != DontCare) {
unsigned ib = bundles->getBundle(I->Number, 0);
activate(ib);
nodes[ib].addBias(Freq * Bias[I->Entry], 1);
}
// Live-out from block?
if (I->Exit != DontCare) {
unsigned ob = bundles->getBundle(I->Number, 1);
activate(ob);
nodes[ob].addBias(Freq * Bias[I->Exit], 0);
}
}
}
/// 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) {
float Freq = getBlockFrequency(*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, 1);
nodes[ob].addBias(-Freq, 0);
}
}
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);
if (nodes[ib].Links.empty() && !nodes[ib].mustSpill())
Linked.push_back(ib);
if (nodes[ob].Links.empty() && !nodes[ob].mustSpill())
Linked.push_back(ob);
float Freq = getBlockFrequency(Number);
nodes[ib].addLink(ob, Freq, 1);
nodes[ob].addLink(ib, Freq, 0);
}
}
bool SpillPlacement::scanActiveBundles() {
Linked.clear();
RecentPositive.clear();
for (int n = ActiveNodes->find_first(); n>=0; n = ActiveNodes->find_next(n)) {
nodes[n].update(nodes);
// 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].Links.empty())
Linked.push_back(n);
if (nodes[n].preferReg())
RecentPositive.push_back(n);
}
return !RecentPositive.empty();
}
/// iterate - Repeatedly update the Hopfield nodes until stability or the
/// maximum number of iterations is reached.
/// @param Linked - Numbers of linked nodes that need updating.
void SpillPlacement::iterate() {
// First update the recently positive nodes. They have likely received new
// negative bias that will turn them off.
while (!RecentPositive.empty())
nodes[RecentPositive.pop_back_val()].update(nodes);
if (Linked.empty())
return;
// Run up to 10 iterations. The edge bundle numbering is closely related to
// basic block numbering, so there is a strong tendency towards chains of
// linked nodes with sequential numbers. By scanning the linked nodes
// backwards and forwards, we make it very likely that a single node can
// affect the entire network in a single iteration. That means very fast
// convergence, usually in a single iteration.
for (unsigned iteration = 0; iteration != 10; ++iteration) {
// Scan backwards, skipping the last node which was just updated.
bool Changed = false;
for (SmallVectorImpl<unsigned>::const_reverse_iterator I =
llvm::next(Linked.rbegin()), E = Linked.rend(); I != E; ++I) {
unsigned n = *I;
if (nodes[n].update(nodes)) {
Changed = true;
if (nodes[n].preferReg())
RecentPositive.push_back(n);
}
}
if (!Changed || !RecentPositive.empty())
return;
// Scan forwards, skipping the first node which was just updated.
Changed = false;
for (SmallVectorImpl<unsigned>::const_iterator I =
llvm::next(Linked.begin()), E = Linked.end(); I != E; ++I) {
unsigned n = *I;
if (nodes[n].update(nodes)) {
Changed = true;
if (nodes[n].preferReg())
RecentPositive.push_back(n);
}
}
if (!Changed || !RecentPositive.empty())
return;
}
}
void SpillPlacement::prepare(BitVector &RegBundles) {
Linked.clear();
RecentPositive.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 = 0;
return Perfect;
}