LLVM 17.0.0git
BlockFrequencyInfoImpl.h
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1//==- BlockFrequencyInfoImpl.h - Block Frequency Implementation --*- C++ -*-==//
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// Shared implementation of BlockFrequency for IR and Machine Instructions.
10// See the documentation below for BlockFrequencyInfoImpl for details.
11//
12//===----------------------------------------------------------------------===//
13
14#ifndef LLVM_ANALYSIS_BLOCKFREQUENCYINFOIMPL_H
15#define LLVM_ANALYSIS_BLOCKFREQUENCYINFOIMPL_H
16
17#include "llvm/ADT/BitVector.h"
18#include "llvm/ADT/DenseMap.h"
19#include "llvm/ADT/DenseSet.h"
25#include "llvm/ADT/Twine.h"
27#include "llvm/IR/BasicBlock.h"
28#include "llvm/IR/ValueHandle.h"
33#include "llvm/Support/Debug.h"
34#include "llvm/Support/Format.h"
37#include <algorithm>
38#include <cassert>
39#include <cstddef>
40#include <cstdint>
41#include <deque>
42#include <iterator>
43#include <limits>
44#include <list>
45#include <optional>
46#include <queue>
47#include <string>
48#include <utility>
49#include <vector>
50
51#define DEBUG_TYPE "block-freq"
52
53namespace llvm {
55
59
60class BranchProbabilityInfo;
61class Function;
62class Loop;
63class LoopInfo;
64class MachineBasicBlock;
65class MachineBranchProbabilityInfo;
66class MachineFunction;
67class MachineLoop;
68class MachineLoopInfo;
69
70namespace bfi_detail {
71
72struct IrreducibleGraph;
73
74// This is part of a workaround for a GCC 4.7 crash on lambdas.
75template <class BT> struct BlockEdgesAdder;
76
77/// Mass of a block.
78///
79/// This class implements a sort of fixed-point fraction always between 0.0 and
80/// 1.0. getMass() == std::numeric_limits<uint64_t>::max() indicates a value of
81/// 1.0.
82///
83/// Masses can be added and subtracted. Simple saturation arithmetic is used,
84/// so arithmetic operations never overflow or underflow.
85///
86/// Masses can be multiplied. Multiplication treats full mass as 1.0 and uses
87/// an inexpensive floating-point algorithm that's off-by-one (almost, but not
88/// quite, maximum precision).
89///
90/// Masses can be scaled by \a BranchProbability at maximum precision.
91class BlockMass {
92 uint64_t Mass = 0;
93
94public:
95 BlockMass() = default;
96 explicit BlockMass(uint64_t Mass) : Mass(Mass) {}
97
98 static BlockMass getEmpty() { return BlockMass(); }
99
101 return BlockMass(std::numeric_limits<uint64_t>::max());
102 }
103
104 uint64_t getMass() const { return Mass; }
105
106 bool isFull() const { return Mass == std::numeric_limits<uint64_t>::max(); }
107 bool isEmpty() const { return !Mass; }
108
109 bool operator!() const { return isEmpty(); }
110
111 /// Add another mass.
112 ///
113 /// Adds another mass, saturating at \a isFull() rather than overflowing.
115 uint64_t Sum = Mass + X.Mass;
116 Mass = Sum < Mass ? std::numeric_limits<uint64_t>::max() : Sum;
117 return *this;
118 }
119
120 /// Subtract another mass.
121 ///
122 /// Subtracts another mass, saturating at \a isEmpty() rather than
123 /// undeflowing.
125 uint64_t Diff = Mass - X.Mass;
126 Mass = Diff > Mass ? 0 : Diff;
127 return *this;
128 }
129
131 Mass = P.scale(Mass);
132 return *this;
133 }
134
135 bool operator==(BlockMass X) const { return Mass == X.Mass; }
136 bool operator!=(BlockMass X) const { return Mass != X.Mass; }
137 bool operator<=(BlockMass X) const { return Mass <= X.Mass; }
138 bool operator>=(BlockMass X) const { return Mass >= X.Mass; }
139 bool operator<(BlockMass X) const { return Mass < X.Mass; }
140 bool operator>(BlockMass X) const { return Mass > X.Mass; }
141
142 /// Convert to scaled number.
143 ///
144 /// Convert to \a ScaledNumber. \a isFull() gives 1.0, while \a isEmpty()
145 /// gives slightly above 0.0.
147
148 void dump() const;
149 raw_ostream &print(raw_ostream &OS) const;
150};
151
153 return BlockMass(L) += R;
154}
156 return BlockMass(L) -= R;
157}
159 return BlockMass(L) *= R;
160}
162 return BlockMass(R) *= L;
163}
164
166 return X.print(OS);
167}
168
169} // end namespace bfi_detail
170
171/// Base class for BlockFrequencyInfoImpl
172///
173/// BlockFrequencyInfoImplBase has supporting data structures and some
174/// algorithms for BlockFrequencyInfoImplBase. Only algorithms that depend on
175/// the block type (or that call such algorithms) are skipped here.
176///
177/// Nevertheless, the majority of the overall algorithm documentation lives with
178/// BlockFrequencyInfoImpl. See there for details.
180public:
183
184 /// Representative of a block.
185 ///
186 /// This is a simple wrapper around an index into the reverse-post-order
187 /// traversal of the blocks.
188 ///
189 /// Unlike a block pointer, its order has meaning (location in the
190 /// topological sort) and it's class is the same regardless of block type.
191 struct BlockNode {
193
195
196 BlockNode() : Index(std::numeric_limits<uint32_t>::max()) {}
198
199 bool operator==(const BlockNode &X) const { return Index == X.Index; }
200 bool operator!=(const BlockNode &X) const { return Index != X.Index; }
201 bool operator<=(const BlockNode &X) const { return Index <= X.Index; }
202 bool operator>=(const BlockNode &X) const { return Index >= X.Index; }
203 bool operator<(const BlockNode &X) const { return Index < X.Index; }
204 bool operator>(const BlockNode &X) const { return Index > X.Index; }
205
206 bool isValid() const { return Index <= getMaxIndex(); }
207
208 static size_t getMaxIndex() {
209 return std::numeric_limits<uint32_t>::max() - 1;
210 }
211 };
212
213 /// Stats about a block itself.
217 };
218
219 /// Data about a loop.
220 ///
221 /// Contains the data necessary to represent a loop as a pseudo-node once it's
222 /// packaged.
223 struct LoopData {
227
228 LoopData *Parent; ///< The parent loop.
229 bool IsPackaged = false; ///< Whether this has been packaged.
230 uint32_t NumHeaders = 1; ///< Number of headers.
231 ExitMap Exits; ///< Successor edges (and weights).
232 NodeList Nodes; ///< Header and the members of the loop.
233 HeaderMassList BackedgeMass; ///< Mass returned to each loop header.
236
238 : Parent(Parent), Nodes(1, Header), BackedgeMass(1) {}
239
240 template <class It1, class It2>
241 LoopData(LoopData *Parent, It1 FirstHeader, It1 LastHeader, It2 FirstOther,
242 It2 LastOther)
243 : Parent(Parent), Nodes(FirstHeader, LastHeader) {
245 Nodes.insert(Nodes.end(), FirstOther, LastOther);
247 }
248
249 bool isHeader(const BlockNode &Node) const {
250 if (isIrreducible())
251 return std::binary_search(Nodes.begin(), Nodes.begin() + NumHeaders,
252 Node);
253 return Node == Nodes[0];
254 }
255
256 BlockNode getHeader() const { return Nodes[0]; }
257 bool isIrreducible() const { return NumHeaders > 1; }
258
260 assert(isHeader(B) && "this is only valid on loop header blocks");
261 if (isIrreducible())
262 return std::lower_bound(Nodes.begin(), Nodes.begin() + NumHeaders, B) -
263 Nodes.begin();
264 return 0;
265 }
266
268 return Nodes.begin() + NumHeaders;
269 }
270
274 }
275 };
276
277 /// Index of loop information.
278 struct WorkingData {
279 BlockNode Node; ///< This node.
280 LoopData *Loop = nullptr; ///< The loop this block is inside.
281 BlockMass Mass; ///< Mass distribution from the entry block.
282
284
285 bool isLoopHeader() const { return Loop && Loop->isHeader(Node); }
286
287 bool isDoubleLoopHeader() const {
288 return isLoopHeader() && Loop->Parent && Loop->Parent->isIrreducible() &&
289 Loop->Parent->isHeader(Node);
290 }
291
293 if (!isLoopHeader())
294 return Loop;
295 if (!isDoubleLoopHeader())
296 return Loop->Parent;
297 return Loop->Parent->Parent;
298 }
299
300 /// Resolve a node to its representative.
301 ///
302 /// Get the node currently representing Node, which could be a containing
303 /// loop.
304 ///
305 /// This function should only be called when distributing mass. As long as
306 /// there are no irreducible edges to Node, then it will have complexity
307 /// O(1) in this context.
308 ///
309 /// In general, the complexity is O(L), where L is the number of loop
310 /// headers Node has been packaged into. Since this method is called in
311 /// the context of distributing mass, L will be the number of loop headers
312 /// an early exit edge jumps out of.
314 auto L = getPackagedLoop();
315 return L ? L->getHeader() : Node;
316 }
317
319 if (!Loop || !Loop->IsPackaged)
320 return nullptr;
321 auto L = Loop;
322 while (L->Parent && L->Parent->IsPackaged)
323 L = L->Parent;
324 return L;
325 }
326
327 /// Get the appropriate mass for a node.
328 ///
329 /// Get appropriate mass for Node. If Node is a loop-header (whose loop
330 /// has been packaged), returns the mass of its pseudo-node. If it's a
331 /// node inside a packaged loop, it returns the loop's mass.
333 if (!isAPackage())
334 return Mass;
335 if (!isADoublePackage())
336 return Loop->Mass;
337 return Loop->Parent->Mass;
338 }
339
340 /// Has ContainingLoop been packaged up?
341 bool isPackaged() const { return getResolvedNode() != Node; }
342
343 /// Has Loop been packaged up?
344 bool isAPackage() const { return isLoopHeader() && Loop->IsPackaged; }
345
346 /// Has Loop been packaged up twice?
347 bool isADoublePackage() const {
348 return isDoubleLoopHeader() && Loop->Parent->IsPackaged;
349 }
350 };
351
352 /// Unscaled probability weight.
353 ///
354 /// Probability weight for an edge in the graph (including the
355 /// successor/target node).
356 ///
357 /// All edges in the original function are 32-bit. However, exit edges from
358 /// loop packages are taken from 64-bit exit masses, so we need 64-bits of
359 /// space in general.
360 ///
361 /// In addition to the raw weight amount, Weight stores the type of the edge
362 /// in the current context (i.e., the context of the loop being processed).
363 /// Is this a local edge within the loop, an exit from the loop, or a
364 /// backedge to the loop header?
365 struct Weight {
370
371 Weight() = default;
374 };
375
376 /// Distribution of unscaled probability weight.
377 ///
378 /// Distribution of unscaled probability weight to a set of successors.
379 ///
380 /// This class collates the successor edge weights for later processing.
381 ///
382 /// \a DidOverflow indicates whether \a Total did overflow while adding to
383 /// the distribution. It should never overflow twice.
386
387 WeightList Weights; ///< Individual successor weights.
388 uint64_t Total = 0; ///< Sum of all weights.
389 bool DidOverflow = false; ///< Whether \a Total did overflow.
390
391 Distribution() = default;
392
393 void addLocal(const BlockNode &Node, uint64_t Amount) {
394 add(Node, Amount, Weight::Local);
395 }
396
397 void addExit(const BlockNode &Node, uint64_t Amount) {
398 add(Node, Amount, Weight::Exit);
399 }
400
401 void addBackedge(const BlockNode &Node, uint64_t Amount) {
402 add(Node, Amount, Weight::Backedge);
403 }
404
405 /// Normalize the distribution.
406 ///
407 /// Combines multiple edges to the same \a Weight::TargetNode and scales
408 /// down so that \a Total fits into 32-bits.
409 ///
410 /// This is linear in the size of \a Weights. For the vast majority of
411 /// cases, adjacent edge weights are combined by sorting WeightList and
412 /// combining adjacent weights. However, for very large edge lists an
413 /// auxiliary hash table is used.
414 void normalize();
415
416 private:
417 void add(const BlockNode &Node, uint64_t Amount, Weight::DistType Type);
418 };
419
420 /// Data about each block. This is used downstream.
421 std::vector<FrequencyData> Freqs;
422
423 /// Whether each block is an irreducible loop header.
424 /// This is used downstream.
426
427 /// Loop data: see initializeLoops().
428 std::vector<WorkingData> Working;
429
430 /// Indexed information about loops.
431 std::list<LoopData> Loops;
432
433 /// Virtual destructor.
434 ///
435 /// Need a virtual destructor to mask the compiler warning about
436 /// getBlockName().
437 virtual ~BlockFrequencyInfoImplBase() = default;
438
439 /// Add all edges out of a packaged loop to the distribution.
440 ///
441 /// Adds all edges from LocalLoopHead to Dist. Calls addToDist() to add each
442 /// successor edge.
443 ///
444 /// \return \c true unless there's an irreducible backedge.
445 bool addLoopSuccessorsToDist(const LoopData *OuterLoop, LoopData &Loop,
446 Distribution &Dist);
447
448 /// Add an edge to the distribution.
449 ///
450 /// Adds an edge to Succ to Dist. If \c LoopHead.isValid(), then whether the
451 /// edge is local/exit/backedge is in the context of LoopHead. Otherwise,
452 /// every edge should be a local edge (since all the loops are packaged up).
453 ///
454 /// \return \c true unless aborted due to an irreducible backedge.
455 bool addToDist(Distribution &Dist, const LoopData *OuterLoop,
456 const BlockNode &Pred, const BlockNode &Succ, uint64_t Weight);
457
458 /// Analyze irreducible SCCs.
459 ///
460 /// Separate irreducible SCCs from \c G, which is an explicit graph of \c
461 /// OuterLoop (or the top-level function, if \c OuterLoop is \c nullptr).
462 /// Insert them into \a Loops before \c Insert.
463 ///
464 /// \return the \c LoopData nodes representing the irreducible SCCs.
467 std::list<LoopData>::iterator Insert);
468
469 /// Update a loop after packaging irreducible SCCs inside of it.
470 ///
471 /// Update \c OuterLoop. Before finding irreducible control flow, it was
472 /// partway through \a computeMassInLoop(), so \a LoopData::Exits and \a
473 /// LoopData::BackedgeMass need to be reset. Also, nodes that were packaged
474 /// up need to be removed from \a OuterLoop::Nodes.
475 void updateLoopWithIrreducible(LoopData &OuterLoop);
476
477 /// Distribute mass according to a distribution.
478 ///
479 /// Distributes the mass in Source according to Dist. If LoopHead.isValid(),
480 /// backedges and exits are stored in its entry in Loops.
481 ///
482 /// Mass is distributed in parallel from two copies of the source mass.
483 void distributeMass(const BlockNode &Source, LoopData *OuterLoop,
484 Distribution &Dist);
485
486 /// Compute the loop scale for a loop.
488
489 /// Adjust the mass of all headers in an irreducible loop.
490 ///
491 /// Initially, irreducible loops are assumed to distribute their mass
492 /// equally among its headers. This can lead to wrong frequency estimates
493 /// since some headers may be executed more frequently than others.
494 ///
495 /// This adjusts header mass distribution so it matches the weights of
496 /// the backedges going into each of the loop headers.
498
500
501 /// Package up a loop.
503
504 /// Unwrap loops.
505 void unwrapLoops();
506
507 /// Finalize frequency metrics.
508 ///
509 /// Calculates final frequencies and cleans up no-longer-needed data
510 /// structures.
511 void finalizeMetrics();
512
513 /// Clear all memory.
514 void clear();
515
516 virtual std::string getBlockName(const BlockNode &Node) const;
517 std::string getLoopName(const LoopData &Loop) const;
518
519 virtual raw_ostream &print(raw_ostream &OS) const { return OS; }
520 void dump() const { print(dbgs()); }
521
522 Scaled64 getFloatingBlockFreq(const BlockNode &Node) const;
523
524 BlockFrequency getBlockFreq(const BlockNode &Node) const;
525 std::optional<uint64_t>
526 getBlockProfileCount(const Function &F, const BlockNode &Node,
527 bool AllowSynthetic = false) const;
528 std::optional<uint64_t>
530 bool AllowSynthetic = false) const;
531 bool isIrrLoopHeader(const BlockNode &Node);
532
533 void setBlockFreq(const BlockNode &Node, uint64_t Freq);
534
535 raw_ostream &printBlockFreq(raw_ostream &OS, const BlockNode &Node) const;
537 const BlockFrequency &Freq) const;
538
540 assert(!Freqs.empty());
541 return Freqs[0].Integer;
542 }
543};
544
545namespace bfi_detail {
546
547template <class BlockT> struct TypeMap {};
548template <> struct TypeMap<BasicBlock> {
553 using LoopT = Loop;
555};
556template <> struct TypeMap<MachineBasicBlock> {
563};
564
565template <class BlockT, class BFIImplT>
567
568/// Get the name of a MachineBasicBlock.
569///
570/// Get the name of a MachineBasicBlock. It's templated so that including from
571/// CodeGen is unnecessary (that would be a layering issue).
572///
573/// This is used mainly for debug output. The name is similar to
574/// MachineBasicBlock::getFullName(), but skips the name of the function.
575template <class BlockT> std::string getBlockName(const BlockT *BB) {
576 assert(BB && "Unexpected nullptr");
577 auto MachineName = "BB" + Twine(BB->getNumber());
578 if (BB->getBasicBlock())
579 return (MachineName + "[" + BB->getName() + "]").str();
580 return MachineName.str();
581}
582/// Get the name of a BasicBlock.
583template <> inline std::string getBlockName(const BasicBlock *BB) {
584 assert(BB && "Unexpected nullptr");
585 return BB->getName().str();
586}
587
588/// Graph of irreducible control flow.
589///
590/// This graph is used for determining the SCCs in a loop (or top-level
591/// function) that has irreducible control flow.
592///
593/// During the block frequency algorithm, the local graphs are defined in a
594/// light-weight way, deferring to the \a BasicBlock or \a MachineBasicBlock
595/// graphs for most edges, but getting others from \a LoopData::ExitMap. The
596/// latter only has successor information.
597///
598/// \a IrreducibleGraph makes this graph explicit. It's in a form that can use
599/// \a GraphTraits (so that \a analyzeIrreducible() can use \a scc_iterator),
600/// and it explicitly lists predecessors and successors. The initialization
601/// that relies on \c MachineBasicBlock is defined in the header.
604
606
608 struct IrrNode {
610 unsigned NumIn = 0;
611 std::deque<const IrrNode *> Edges;
612
614
615 using iterator = std::deque<const IrrNode *>::const_iterator;
616
617 iterator pred_begin() const { return Edges.begin(); }
618 iterator succ_begin() const { return Edges.begin() + NumIn; }
619 iterator pred_end() const { return succ_begin(); }
620 iterator succ_end() const { return Edges.end(); }
621 };
623 const IrrNode *StartIrr = nullptr;
624 std::vector<IrrNode> Nodes;
626
627 /// Construct an explicit graph containing irreducible control flow.
628 ///
629 /// Construct an explicit graph of the control flow in \c OuterLoop (or the
630 /// top-level function, if \c OuterLoop is \c nullptr). Uses \c
631 /// addBlockEdges to add block successors that have not been packaged into
632 /// loops.
633 ///
634 /// \a BlockFrequencyInfoImpl::computeIrreducibleMass() is the only expected
635 /// user of this.
636 template <class BlockEdgesAdder>
638 BlockEdgesAdder addBlockEdges) : BFI(BFI) {
639 initialize(OuterLoop, addBlockEdges);
640 }
641
642 template <class BlockEdgesAdder>
643 void initialize(const BFIBase::LoopData *OuterLoop,
644 BlockEdgesAdder addBlockEdges);
645 void addNodesInLoop(const BFIBase::LoopData &OuterLoop);
646 void addNodesInFunction();
647
648 void addNode(const BlockNode &Node) {
649 Nodes.emplace_back(Node);
650 BFI.Working[Node.Index].getMass() = BlockMass::getEmpty();
651 }
652
653 void indexNodes();
654 template <class BlockEdgesAdder>
655 void addEdges(const BlockNode &Node, const BFIBase::LoopData *OuterLoop,
656 BlockEdgesAdder addBlockEdges);
657 void addEdge(IrrNode &Irr, const BlockNode &Succ,
658 const BFIBase::LoopData *OuterLoop);
659};
660
661template <class BlockEdgesAdder>
663 BlockEdgesAdder addBlockEdges) {
664 if (OuterLoop) {
665 addNodesInLoop(*OuterLoop);
666 for (auto N : OuterLoop->Nodes)
667 addEdges(N, OuterLoop, addBlockEdges);
668 } else {
670 for (uint32_t Index = 0; Index < BFI.Working.size(); ++Index)
671 addEdges(Index, OuterLoop, addBlockEdges);
672 }
674}
675
676template <class BlockEdgesAdder>
678 const BFIBase::LoopData *OuterLoop,
679 BlockEdgesAdder addBlockEdges) {
680 auto L = Lookup.find(Node.Index);
681 if (L == Lookup.end())
682 return;
683 IrrNode &Irr = *L->second;
684 const auto &Working = BFI.Working[Node.Index];
685
686 if (Working.isAPackage())
687 for (const auto &I : Working.Loop->Exits)
688 addEdge(Irr, I.first, OuterLoop);
689 else
690 addBlockEdges(*this, Irr, OuterLoop);
691}
692
693} // end namespace bfi_detail
694
695/// Shared implementation for block frequency analysis.
696///
697/// This is a shared implementation of BlockFrequencyInfo and
698/// MachineBlockFrequencyInfo, and calculates the relative frequencies of
699/// blocks.
700///
701/// LoopInfo defines a loop as a "non-trivial" SCC dominated by a single block,
702/// which is called the header. A given loop, L, can have sub-loops, which are
703/// loops within the subgraph of L that exclude its header. (A "trivial" SCC
704/// consists of a single block that does not have a self-edge.)
705///
706/// In addition to loops, this algorithm has limited support for irreducible
707/// SCCs, which are SCCs with multiple entry blocks. Irreducible SCCs are
708/// discovered on the fly, and modelled as loops with multiple headers.
709///
710/// The headers of irreducible sub-SCCs consist of its entry blocks and all
711/// nodes that are targets of a backedge within it (excluding backedges within
712/// true sub-loops). Block frequency calculations act as if a block is
713/// inserted that intercepts all the edges to the headers. All backedges and
714/// entries point to this block. Its successors are the headers, which split
715/// the frequency evenly.
716///
717/// This algorithm leverages BlockMass and ScaledNumber to maintain precision,
718/// separates mass distribution from loop scaling, and dithers to eliminate
719/// probability mass loss.
720///
721/// The implementation is split between BlockFrequencyInfoImpl, which knows the
722/// type of graph being modelled (BasicBlock vs. MachineBasicBlock), and
723/// BlockFrequencyInfoImplBase, which doesn't. The base class uses \a
724/// BlockNode, a wrapper around a uint32_t. BlockNode is numbered from 0 in
725/// reverse-post order. This gives two advantages: it's easy to compare the
726/// relative ordering of two nodes, and maps keyed on BlockT can be represented
727/// by vectors.
728///
729/// This algorithm is O(V+E), unless there is irreducible control flow, in
730/// which case it's O(V*E) in the worst case.
731///
732/// These are the main stages:
733///
734/// 0. Reverse post-order traversal (\a initializeRPOT()).
735///
736/// Run a single post-order traversal and save it (in reverse) in RPOT.
737/// All other stages make use of this ordering. Save a lookup from BlockT
738/// to BlockNode (the index into RPOT) in Nodes.
739///
740/// 1. Loop initialization (\a initializeLoops()).
741///
742/// Translate LoopInfo/MachineLoopInfo into a form suitable for the rest of
743/// the algorithm. In particular, store the immediate members of each loop
744/// in reverse post-order.
745///
746/// 2. Calculate mass and scale in loops (\a computeMassInLoops()).
747///
748/// For each loop (bottom-up), distribute mass through the DAG resulting
749/// from ignoring backedges and treating sub-loops as a single pseudo-node.
750/// Track the backedge mass distributed to the loop header, and use it to
751/// calculate the loop scale (number of loop iterations). Immediate
752/// members that represent sub-loops will already have been visited and
753/// packaged into a pseudo-node.
754///
755/// Distributing mass in a loop is a reverse-post-order traversal through
756/// the loop. Start by assigning full mass to the Loop header. For each
757/// node in the loop:
758///
759/// - Fetch and categorize the weight distribution for its successors.
760/// If this is a packaged-subloop, the weight distribution is stored
761/// in \a LoopData::Exits. Otherwise, fetch it from
762/// BranchProbabilityInfo.
763///
764/// - Each successor is categorized as \a Weight::Local, a local edge
765/// within the current loop, \a Weight::Backedge, a backedge to the
766/// loop header, or \a Weight::Exit, any successor outside the loop.
767/// The weight, the successor, and its category are stored in \a
768/// Distribution. There can be multiple edges to each successor.
769///
770/// - If there's a backedge to a non-header, there's an irreducible SCC.
771/// The usual flow is temporarily aborted. \a
772/// computeIrreducibleMass() finds the irreducible SCCs within the
773/// loop, packages them up, and restarts the flow.
774///
775/// - Normalize the distribution: scale weights down so that their sum
776/// is 32-bits, and coalesce multiple edges to the same node.
777///
778/// - Distribute the mass accordingly, dithering to minimize mass loss,
779/// as described in \a distributeMass().
780///
781/// In the case of irreducible loops, instead of a single loop header,
782/// there will be several. The computation of backedge masses is similar
783/// but instead of having a single backedge mass, there will be one
784/// backedge per loop header. In these cases, each backedge will carry
785/// a mass proportional to the edge weights along the corresponding
786/// path.
787///
788/// At the end of propagation, the full mass assigned to the loop will be
789/// distributed among the loop headers proportionally according to the
790/// mass flowing through their backedges.
791///
792/// Finally, calculate the loop scale from the accumulated backedge mass.
793///
794/// 3. Distribute mass in the function (\a computeMassInFunction()).
795///
796/// Finally, distribute mass through the DAG resulting from packaging all
797/// loops in the function. This uses the same algorithm as distributing
798/// mass in a loop, except that there are no exit or backedge edges.
799///
800/// 4. Unpackage loops (\a unwrapLoops()).
801///
802/// Initialize each block's frequency to a floating point representation of
803/// its mass.
804///
805/// Visit loops top-down, scaling the frequencies of its immediate members
806/// by the loop's pseudo-node's frequency.
807///
808/// 5. Convert frequencies to a 64-bit range (\a finalizeMetrics()).
809///
810/// Using the min and max frequencies as a guide, translate floating point
811/// frequencies to an appropriate range in uint64_t.
812///
813/// It has some known flaws.
814///
815/// - The model of irreducible control flow is a rough approximation.
816///
817/// Modelling irreducible control flow exactly involves setting up and
818/// solving a group of infinite geometric series. Such precision is
819/// unlikely to be worthwhile, since most of our algorithms give up on
820/// irreducible control flow anyway.
821///
822/// Nevertheless, we might find that we need to get closer. Here's a sort
823/// of TODO list for the model with diminishing returns, to be completed as
824/// necessary.
825///
826/// - The headers for the \a LoopData representing an irreducible SCC
827/// include non-entry blocks. When these extra blocks exist, they
828/// indicate a self-contained irreducible sub-SCC. We could treat them
829/// as sub-loops, rather than arbitrarily shoving the problematic
830/// blocks into the headers of the main irreducible SCC.
831///
832/// - Entry frequencies are assumed to be evenly split between the
833/// headers of a given irreducible SCC, which is the only option if we
834/// need to compute mass in the SCC before its parent loop. Instead,
835/// we could partially compute mass in the parent loop, and stop when
836/// we get to the SCC. Here, we have the correct ratio of entry
837/// masses, which we can use to adjust their relative frequencies.
838/// Compute mass in the SCC, and then continue propagation in the
839/// parent.
840///
841/// - We can propagate mass iteratively through the SCC, for some fixed
842/// number of iterations. Each iteration starts by assigning the entry
843/// blocks their backedge mass from the prior iteration. The final
844/// mass for each block (and each exit, and the total backedge mass
845/// used for computing loop scale) is the sum of all iterations.
846/// (Running this until fixed point would "solve" the geometric
847/// series by simulation.)
849 // This is part of a workaround for a GCC 4.7 crash on lambdas.
850 friend struct bfi_detail::BlockEdgesAdder<BT>;
851
852 using BlockT = typename bfi_detail::TypeMap<BT>::BlockT;
853 using BlockKeyT = typename bfi_detail::TypeMap<BT>::BlockKeyT;
854 using FunctionT = typename bfi_detail::TypeMap<BT>::FunctionT;
855 using BranchProbabilityInfoT =
857 using LoopT = typename bfi_detail::TypeMap<BT>::LoopT;
858 using LoopInfoT = typename bfi_detail::TypeMap<BT>::LoopInfoT;
861 using BFICallbackVH =
863
864 const BranchProbabilityInfoT *BPI = nullptr;
865 const LoopInfoT *LI = nullptr;
866 const FunctionT *F = nullptr;
867
868 // All blocks in reverse postorder.
869 std::vector<const BlockT *> RPOT;
871
872 using rpot_iterator = typename std::vector<const BlockT *>::const_iterator;
873
874 rpot_iterator rpot_begin() const { return RPOT.begin(); }
875 rpot_iterator rpot_end() const { return RPOT.end(); }
876
877 size_t getIndex(const rpot_iterator &I) const { return I - rpot_begin(); }
878
879 BlockNode getNode(const rpot_iterator &I) const {
880 return BlockNode(getIndex(I));
881 }
882
883 BlockNode getNode(const BlockT *BB) const { return Nodes.lookup(BB).first; }
884
885 const BlockT *getBlock(const BlockNode &Node) const {
886 assert(Node.Index < RPOT.size());
887 return RPOT[Node.Index];
888 }
889
890 /// Run (and save) a post-order traversal.
891 ///
892 /// Saves a reverse post-order traversal of all the nodes in \a F.
893 void initializeRPOT();
894
895 /// Initialize loop data.
896 ///
897 /// Build up \a Loops using \a LoopInfo. \a LoopInfo gives us a mapping from
898 /// each block to the deepest loop it's in, but we need the inverse. For each
899 /// loop, we store in reverse post-order its "immediate" members, defined as
900 /// the header, the headers of immediate sub-loops, and all other blocks in
901 /// the loop that are not in sub-loops.
902 void initializeLoops();
903
904 /// Propagate to a block's successors.
905 ///
906 /// In the context of distributing mass through \c OuterLoop, divide the mass
907 /// currently assigned to \c Node between its successors.
908 ///
909 /// \return \c true unless there's an irreducible backedge.
910 bool propagateMassToSuccessors(LoopData *OuterLoop, const BlockNode &Node);
911
912 /// Compute mass in a particular loop.
913 ///
914 /// Assign mass to \c Loop's header, and then for each block in \c Loop in
915 /// reverse post-order, distribute mass to its successors. Only visits nodes
916 /// that have not been packaged into sub-loops.
917 ///
918 /// \pre \a computeMassInLoop() has been called for each subloop of \c Loop.
919 /// \return \c true unless there's an irreducible backedge.
920 bool computeMassInLoop(LoopData &Loop);
921
922 /// Try to compute mass in the top-level function.
923 ///
924 /// Assign mass to the entry block, and then for each block in reverse
925 /// post-order, distribute mass to its successors. Skips nodes that have
926 /// been packaged into loops.
927 ///
928 /// \pre \a computeMassInLoops() has been called.
929 /// \return \c true unless there's an irreducible backedge.
930 bool tryToComputeMassInFunction();
931
932 /// Compute mass in (and package up) irreducible SCCs.
933 ///
934 /// Find the irreducible SCCs in \c OuterLoop, add them to \a Loops (in front
935 /// of \c Insert), and call \a computeMassInLoop() on each of them.
936 ///
937 /// If \c OuterLoop is \c nullptr, it refers to the top-level function.
938 ///
939 /// \pre \a computeMassInLoop() has been called for each subloop of \c
940 /// OuterLoop.
941 /// \pre \c Insert points at the last loop successfully processed by \a
942 /// computeMassInLoop().
943 /// \pre \c OuterLoop has irreducible SCCs.
944 void computeIrreducibleMass(LoopData *OuterLoop,
945 std::list<LoopData>::iterator Insert);
946
947 /// Compute mass in all loops.
948 ///
949 /// For each loop bottom-up, call \a computeMassInLoop().
950 ///
951 /// \a computeMassInLoop() aborts (and returns \c false) on loops that
952 /// contain a irreducible sub-SCCs. Use \a computeIrreducibleMass() and then
953 /// re-enter \a computeMassInLoop().
954 ///
955 /// \post \a computeMassInLoop() has returned \c true for every loop.
956 void computeMassInLoops();
957
958 /// Compute mass in the top-level function.
959 ///
960 /// Uses \a tryToComputeMassInFunction() and \a computeIrreducibleMass() to
961 /// compute mass in the top-level function.
962 ///
963 /// \post \a tryToComputeMassInFunction() has returned \c true.
964 void computeMassInFunction();
965
966 std::string getBlockName(const BlockNode &Node) const override {
967 return bfi_detail::getBlockName(getBlock(Node));
968 }
969
970 /// The current implementation for computing relative block frequencies does
971 /// not handle correctly control-flow graphs containing irreducible loops. To
972 /// resolve the problem, we apply a post-processing step, which iteratively
973 /// updates block frequencies based on the frequencies of their predesessors.
974 /// This corresponds to finding the stationary point of the Markov chain by
975 /// an iterative method aka "PageRank computation".
976 /// The algorithm takes at most O(|E| * IterativeBFIMaxIterations) steps but
977 /// typically converges faster.
978 ///
979 /// Decide whether we want to apply iterative inference for a given function.
980 bool needIterativeInference() const;
981
982 /// Apply an iterative post-processing to infer correct counts for irr loops.
983 void applyIterativeInference();
984
985 using ProbMatrixType = std::vector<std::vector<std::pair<size_t, Scaled64>>>;
986
987 /// Run iterative inference for a probability matrix and initial frequencies.
988 void iterativeInference(const ProbMatrixType &ProbMatrix,
989 std::vector<Scaled64> &Freq) const;
990
991 /// Find all blocks to apply inference on, that is, reachable from the entry
992 /// and backward reachable from exists along edges with positive probability.
993 void findReachableBlocks(std::vector<const BlockT *> &Blocks) const;
994
995 /// Build a matrix of probabilities with transitions (edges) between the
996 /// blocks: ProbMatrix[I] holds pairs (J, P), where Pr[J -> I | J] = P
997 void initTransitionProbabilities(
998 const std::vector<const BlockT *> &Blocks,
999 const DenseMap<const BlockT *, size_t> &BlockIndex,
1000 ProbMatrixType &ProbMatrix) const;
1001
1002#ifndef NDEBUG
1003 /// Compute the discrepancy between current block frequencies and the
1004 /// probability matrix.
1005 Scaled64 discrepancy(const ProbMatrixType &ProbMatrix,
1006 const std::vector<Scaled64> &Freq) const;
1007#endif
1008
1009public:
1011
1012 const FunctionT *getFunction() const { return F; }
1013
1014 void calculate(const FunctionT &F, const BranchProbabilityInfoT &BPI,
1015 const LoopInfoT &LI);
1016
1018
1019 BlockFrequency getBlockFreq(const BlockT *BB) const {
1020 return BlockFrequencyInfoImplBase::getBlockFreq(getNode(BB));
1021 }
1022
1023 std::optional<uint64_t>
1024 getBlockProfileCount(const Function &F, const BlockT *BB,
1025 bool AllowSynthetic = false) const {
1027 AllowSynthetic);
1028 }
1029
1030 std::optional<uint64_t>
1032 bool AllowSynthetic = false) const {
1034 AllowSynthetic);
1035 }
1036
1037 bool isIrrLoopHeader(const BlockT *BB) {
1039 }
1040
1041 void setBlockFreq(const BlockT *BB, uint64_t Freq);
1042
1043 void forgetBlock(const BlockT *BB) {
1044 // We don't erase corresponding items from `Freqs`, `RPOT` and other to
1045 // avoid invalidating indices. Doing so would have saved some memory, but
1046 // it's not worth it.
1047 Nodes.erase(BB);
1048 }
1049
1050 Scaled64 getFloatingBlockFreq(const BlockT *BB) const {
1052 }
1053
1054 const BranchProbabilityInfoT &getBPI() const { return *BPI; }
1055
1056 /// Print the frequencies for the current function.
1057 ///
1058 /// Prints the frequencies for the blocks in the current function.
1059 ///
1060 /// Blocks are printed in the natural iteration order of the function, rather
1061 /// than reverse post-order. This provides two advantages: writing -analyze
1062 /// tests is easier (since blocks come out in source order), and even
1063 /// unreachable blocks are printed.
1064 ///
1065 /// \a BlockFrequencyInfoImplBase::print() only knows reverse post-order, so
1066 /// we need to override it here.
1067 raw_ostream &print(raw_ostream &OS) const override;
1068
1071
1072 raw_ostream &printBlockFreq(raw_ostream &OS, const BlockT *BB) const {
1073 return BlockFrequencyInfoImplBase::printBlockFreq(OS, getNode(BB));
1074 }
1075
1077};
1078
1079namespace bfi_detail {
1080
1081template <class BFIImplT>
1082class BFICallbackVH<BasicBlock, BFIImplT> : public CallbackVH {
1083 BFIImplT *BFIImpl;
1084
1085public:
1086 BFICallbackVH() = default;
1087
1088 BFICallbackVH(const BasicBlock *BB, BFIImplT *BFIImpl)
1089 : CallbackVH(BB), BFIImpl(BFIImpl) {}
1090
1091 virtual ~BFICallbackVH() = default;
1092
1093 void deleted() override {
1094 BFIImpl->forgetBlock(cast<BasicBlock>(getValPtr()));
1095 }
1096};
1097
1098/// Dummy implementation since MachineBasicBlocks aren't Values, so ValueHandles
1099/// don't apply to them.
1100template <class BFIImplT>
1102public:
1103 BFICallbackVH() = default;
1104 BFICallbackVH(const MachineBasicBlock *, BFIImplT *) {}
1105};
1106
1107} // end namespace bfi_detail
1108
1109template <class BT>
1111 const BranchProbabilityInfoT &BPI,
1112 const LoopInfoT &LI) {
1113 // Save the parameters.
1114 this->BPI = &BPI;
1115 this->LI = &LI;
1116 this->F = &F;
1117
1118 // Clean up left-over data structures.
1120 RPOT.clear();
1121 Nodes.clear();
1122
1123 // Initialize.
1124 LLVM_DEBUG(dbgs() << "\nblock-frequency: " << F.getName()
1125 << "\n================="
1126 << std::string(F.getName().size(), '=') << "\n");
1127 initializeRPOT();
1128 initializeLoops();
1129
1130 // Visit loops in post-order to find the local mass distribution, and then do
1131 // the full function.
1132 computeMassInLoops();
1133 computeMassInFunction();
1134 unwrapLoops();
1135 // Apply a post-processing step improving computed frequencies for functions
1136 // with irreducible loops.
1137 if (needIterativeInference())
1138 applyIterativeInference();
1139 finalizeMetrics();
1140
1142 // To detect BFI queries for unknown blocks, add entries for unreachable
1143 // blocks, if any. This is to distinguish between known/existing unreachable
1144 // blocks and unknown blocks.
1145 for (const BlockT &BB : F)
1146 if (!Nodes.count(&BB))
1147 setBlockFreq(&BB, 0);
1148 }
1149}
1150
1151template <class BT>
1153 if (Nodes.count(BB))
1155 else {
1156 // If BB is a newly added block after BFI is done, we need to create a new
1157 // BlockNode for it assigned with a new index. The index can be determined
1158 // by the size of Freqs.
1159 BlockNode NewNode(Freqs.size());
1160 Nodes[BB] = {NewNode, BFICallbackVH(BB, this)};
1161 Freqs.emplace_back();
1163 }
1164}
1165
1166template <class BT> void BlockFrequencyInfoImpl<BT>::initializeRPOT() {
1167 const BlockT *Entry = &F->front();
1168 RPOT.reserve(F->size());
1169 std::copy(po_begin(Entry), po_end(Entry), std::back_inserter(RPOT));
1170 std::reverse(RPOT.begin(), RPOT.end());
1171
1172 assert(RPOT.size() - 1 <= BlockNode::getMaxIndex() &&
1173 "More nodes in function than Block Frequency Info supports");
1174
1175 LLVM_DEBUG(dbgs() << "reverse-post-order-traversal\n");
1176 for (rpot_iterator I = rpot_begin(), E = rpot_end(); I != E; ++I) {
1177 BlockNode Node = getNode(I);
1178 LLVM_DEBUG(dbgs() << " - " << getIndex(I) << ": " << getBlockName(Node)
1179 << "\n");
1180 Nodes[*I] = {Node, BFICallbackVH(*I, this)};
1181 }
1182
1183 Working.reserve(RPOT.size());
1184 for (size_t Index = 0; Index < RPOT.size(); ++Index)
1185 Working.emplace_back(Index);
1186 Freqs.resize(RPOT.size());
1187}
1188
1189template <class BT> void BlockFrequencyInfoImpl<BT>::initializeLoops() {
1190 LLVM_DEBUG(dbgs() << "loop-detection\n");
1191 if (LI->empty())
1192 return;
1193
1194 // Visit loops top down and assign them an index.
1195 std::deque<std::pair<const LoopT *, LoopData *>> Q;
1196 for (const LoopT *L : *LI)
1197 Q.emplace_back(L, nullptr);
1198 while (!Q.empty()) {
1199 const LoopT *Loop = Q.front().first;
1200 LoopData *Parent = Q.front().second;
1201 Q.pop_front();
1202
1203 BlockNode Header = getNode(Loop->getHeader());
1204 assert(Header.isValid());
1205
1206 Loops.emplace_back(Parent, Header);
1207 Working[Header.Index].Loop = &Loops.back();
1208 LLVM_DEBUG(dbgs() << " - loop = " << getBlockName(Header) << "\n");
1209
1210 for (const LoopT *L : *Loop)
1211 Q.emplace_back(L, &Loops.back());
1212 }
1213
1214 // Visit nodes in reverse post-order and add them to their deepest containing
1215 // loop.
1216 for (size_t Index = 0; Index < RPOT.size(); ++Index) {
1217 // Loop headers have already been mostly mapped.
1218 if (Working[Index].isLoopHeader()) {
1219 LoopData *ContainingLoop = Working[Index].getContainingLoop();
1220 if (ContainingLoop)
1221 ContainingLoop->Nodes.push_back(Index);
1222 continue;
1223 }
1224
1225 const LoopT *Loop = LI->getLoopFor(RPOT[Index]);
1226 if (!Loop)
1227 continue;
1228
1229 // Add this node to its containing loop's member list.
1230 BlockNode Header = getNode(Loop->getHeader());
1231 assert(Header.isValid());
1232 const auto &HeaderData = Working[Header.Index];
1233 assert(HeaderData.isLoopHeader());
1234
1235 Working[Index].Loop = HeaderData.Loop;
1236 HeaderData.Loop->Nodes.push_back(Index);
1237 LLVM_DEBUG(dbgs() << " - loop = " << getBlockName(Header)
1238 << ": member = " << getBlockName(Index) << "\n");
1239 }
1240}
1241
1242template <class BT> void BlockFrequencyInfoImpl<BT>::computeMassInLoops() {
1243 // Visit loops with the deepest first, and the top-level loops last.
1244 for (auto L = Loops.rbegin(), E = Loops.rend(); L != E; ++L) {
1245 if (computeMassInLoop(*L))
1246 continue;
1247 auto Next = std::next(L);
1248 computeIrreducibleMass(&*L, L.base());
1249 L = std::prev(Next);
1250 if (computeMassInLoop(*L))
1251 continue;
1252 llvm_unreachable("unhandled irreducible control flow");
1253 }
1254}
1255
1256template <class BT>
1257bool BlockFrequencyInfoImpl<BT>::computeMassInLoop(LoopData &Loop) {
1258 // Compute mass in loop.
1259 LLVM_DEBUG(dbgs() << "compute-mass-in-loop: " << getLoopName(Loop) << "\n");
1260
1261 if (Loop.isIrreducible()) {
1262 LLVM_DEBUG(dbgs() << "isIrreducible = true\n");
1263 Distribution Dist;
1264 unsigned NumHeadersWithWeight = 0;
1265 std::optional<uint64_t> MinHeaderWeight;
1266 DenseSet<uint32_t> HeadersWithoutWeight;
1267 HeadersWithoutWeight.reserve(Loop.NumHeaders);
1268 for (uint32_t H = 0; H < Loop.NumHeaders; ++H) {
1269 auto &HeaderNode = Loop.Nodes[H];
1270 const BlockT *Block = getBlock(HeaderNode);
1271 IsIrrLoopHeader.set(Loop.Nodes[H].Index);
1272 std::optional<uint64_t> HeaderWeight = Block->getIrrLoopHeaderWeight();
1273 if (!HeaderWeight) {
1274 LLVM_DEBUG(dbgs() << "Missing irr loop header metadata on "
1275 << getBlockName(HeaderNode) << "\n");
1276 HeadersWithoutWeight.insert(H);
1277 continue;
1278 }
1279 LLVM_DEBUG(dbgs() << getBlockName(HeaderNode)
1280 << " has irr loop header weight " << *HeaderWeight
1281 << "\n");
1282 NumHeadersWithWeight++;
1283 uint64_t HeaderWeightValue = *HeaderWeight;
1284 if (!MinHeaderWeight || HeaderWeightValue < MinHeaderWeight)
1285 MinHeaderWeight = HeaderWeightValue;
1286 if (HeaderWeightValue) {
1287 Dist.addLocal(HeaderNode, HeaderWeightValue);
1288 }
1289 }
1290 // As a heuristic, if some headers don't have a weight, give them the
1291 // minimum weight seen (not to disrupt the existing trends too much by
1292 // using a weight that's in the general range of the other headers' weights,
1293 // and the minimum seems to perform better than the average.)
1294 // FIXME: better update in the passes that drop the header weight.
1295 // If no headers have a weight, give them even weight (use weight 1).
1296 if (!MinHeaderWeight)
1297 MinHeaderWeight = 1;
1298 for (uint32_t H : HeadersWithoutWeight) {
1299 auto &HeaderNode = Loop.Nodes[H];
1300 assert(!getBlock(HeaderNode)->getIrrLoopHeaderWeight() &&
1301 "Shouldn't have a weight metadata");
1302 uint64_t MinWeight = *MinHeaderWeight;
1303 LLVM_DEBUG(dbgs() << "Giving weight " << MinWeight << " to "
1304 << getBlockName(HeaderNode) << "\n");
1305 if (MinWeight)
1306 Dist.addLocal(HeaderNode, MinWeight);
1307 }
1308 distributeIrrLoopHeaderMass(Dist);
1309 for (const BlockNode &M : Loop.Nodes)
1310 if (!propagateMassToSuccessors(&Loop, M))
1311 llvm_unreachable("unhandled irreducible control flow");
1312 if (NumHeadersWithWeight == 0)
1313 // No headers have a metadata. Adjust header mass.
1314 adjustLoopHeaderMass(Loop);
1315 } else {
1316 Working[Loop.getHeader().Index].getMass() = BlockMass::getFull();
1317 if (!propagateMassToSuccessors(&Loop, Loop.getHeader()))
1318 llvm_unreachable("irreducible control flow to loop header!?");
1319 for (const BlockNode &M : Loop.members())
1320 if (!propagateMassToSuccessors(&Loop, M))
1321 // Irreducible backedge.
1322 return false;
1323 }
1324
1325 computeLoopScale(Loop);
1326 packageLoop(Loop);
1327 return true;
1328}
1329
1330template <class BT>
1331bool BlockFrequencyInfoImpl<BT>::tryToComputeMassInFunction() {
1332 // Compute mass in function.
1333 LLVM_DEBUG(dbgs() << "compute-mass-in-function\n");
1334 assert(!Working.empty() && "no blocks in function");
1335 assert(!Working[0].isLoopHeader() && "entry block is a loop header");
1336
1337 Working[0].getMass() = BlockMass::getFull();
1338 for (rpot_iterator I = rpot_begin(), IE = rpot_end(); I != IE; ++I) {
1339 // Check for nodes that have been packaged.
1340 BlockNode Node = getNode(I);
1341 if (Working[Node.Index].isPackaged())
1342 continue;
1343
1344 if (!propagateMassToSuccessors(nullptr, Node))
1345 return false;
1346 }
1347 return true;
1348}
1349
1350template <class BT> void BlockFrequencyInfoImpl<BT>::computeMassInFunction() {
1351 if (tryToComputeMassInFunction())
1352 return;
1353 computeIrreducibleMass(nullptr, Loops.begin());
1354 if (tryToComputeMassInFunction())
1355 return;
1356 llvm_unreachable("unhandled irreducible control flow");
1357}
1358
1359template <class BT>
1360bool BlockFrequencyInfoImpl<BT>::needIterativeInference() const {
1362 return false;
1363 if (!F->getFunction().hasProfileData())
1364 return false;
1365 // Apply iterative inference only if the function contains irreducible loops;
1366 // otherwise, computed block frequencies are reasonably correct.
1367 for (auto L = Loops.rbegin(), E = Loops.rend(); L != E; ++L) {
1368 if (L->isIrreducible())
1369 return true;
1370 }
1371 return false;
1372}
1373
1374template <class BT> void BlockFrequencyInfoImpl<BT>::applyIterativeInference() {
1375 // Extract blocks for processing: a block is considered for inference iff it
1376 // can be reached from the entry by edges with a positive probability.
1377 // Non-processed blocks are assigned with the zero frequency and are ignored
1378 // in the computation
1379 std::vector<const BlockT *> ReachableBlocks;
1380 findReachableBlocks(ReachableBlocks);
1381 if (ReachableBlocks.empty())
1382 return;
1383
1384 // The map is used to to index successors/predecessors of reachable blocks in
1385 // the ReachableBlocks vector
1386 DenseMap<const BlockT *, size_t> BlockIndex;
1387 // Extract initial frequencies for the reachable blocks
1388 auto Freq = std::vector<Scaled64>(ReachableBlocks.size());
1389 Scaled64 SumFreq;
1390 for (size_t I = 0; I < ReachableBlocks.size(); I++) {
1391 const BlockT *BB = ReachableBlocks[I];
1392 BlockIndex[BB] = I;
1393 Freq[I] = getFloatingBlockFreq(BB);
1394 SumFreq += Freq[I];
1395 }
1396 assert(!SumFreq.isZero() && "empty initial block frequencies");
1397
1398 LLVM_DEBUG(dbgs() << "Applying iterative inference for " << F->getName()
1399 << " with " << ReachableBlocks.size() << " blocks\n");
1400
1401 // Normalizing frequencies so they sum up to 1.0
1402 for (auto &Value : Freq) {
1403 Value /= SumFreq;
1404 }
1405
1406 // Setting up edge probabilities using sparse matrix representation:
1407 // ProbMatrix[I] holds a vector of pairs (J, P) where Pr[J -> I | J] = P
1408 ProbMatrixType ProbMatrix;
1409 initTransitionProbabilities(ReachableBlocks, BlockIndex, ProbMatrix);
1410
1411 // Run the propagation
1412 iterativeInference(ProbMatrix, Freq);
1413
1414 // Assign computed frequency values
1415 for (const BlockT &BB : *F) {
1416 auto Node = getNode(&BB);
1417 if (!Node.isValid())
1418 continue;
1419 if (BlockIndex.count(&BB)) {
1420 Freqs[Node.Index].Scaled = Freq[BlockIndex[&BB]];
1421 } else {
1422 Freqs[Node.Index].Scaled = Scaled64::getZero();
1423 }
1424 }
1425}
1426
1427template <class BT>
1428void BlockFrequencyInfoImpl<BT>::iterativeInference(
1429 const ProbMatrixType &ProbMatrix, std::vector<Scaled64> &Freq) const {
1431 "incorrectly specified precision");
1432 // Convert double precision to Scaled64
1433 const auto Precision =
1434 Scaled64::getInverse(static_cast<uint64_t>(1.0 / IterativeBFIPrecision));
1435 const size_t MaxIterations = IterativeBFIMaxIterationsPerBlock * Freq.size();
1436
1437#ifndef NDEBUG
1438 LLVM_DEBUG(dbgs() << " Initial discrepancy = "
1439 << discrepancy(ProbMatrix, Freq).toString() << "\n");
1440#endif
1441
1442 // Successors[I] holds unique sucessors of the I-th block
1443 auto Successors = std::vector<std::vector<size_t>>(Freq.size());
1444 for (size_t I = 0; I < Freq.size(); I++) {
1445 for (const auto &Jump : ProbMatrix[I]) {
1446 Successors[Jump.first].push_back(I);
1447 }
1448 }
1449
1450 // To speedup computation, we maintain a set of "active" blocks whose
1451 // frequencies need to be updated based on the incoming edges.
1452 // The set is dynamic and changes after every update. Initially all blocks
1453 // with a positive frequency are active
1454 auto IsActive = BitVector(Freq.size(), false);
1455 std::queue<size_t> ActiveSet;
1456 for (size_t I = 0; I < Freq.size(); I++) {
1457 if (Freq[I] > 0) {
1458 ActiveSet.push(I);
1459 IsActive[I] = true;
1460 }
1461 }
1462
1463 // Iterate over the blocks propagating frequencies
1464 size_t It = 0;
1465 while (It++ < MaxIterations && !ActiveSet.empty()) {
1466 size_t I = ActiveSet.front();
1467 ActiveSet.pop();
1468 IsActive[I] = false;
1469
1470 // Compute a new frequency for the block: NewFreq := Freq \times ProbMatrix.
1471 // A special care is taken for self-edges that needs to be scaled by
1472 // (1.0 - SelfProb), where SelfProb is the sum of probabilities on the edges
1473 Scaled64 NewFreq;
1474 Scaled64 OneMinusSelfProb = Scaled64::getOne();
1475 for (const auto &Jump : ProbMatrix[I]) {
1476 if (Jump.first == I) {
1477 OneMinusSelfProb -= Jump.second;
1478 } else {
1479 NewFreq += Freq[Jump.first] * Jump.second;
1480 }
1481 }
1482 if (OneMinusSelfProb != Scaled64::getOne())
1483 NewFreq /= OneMinusSelfProb;
1484
1485 // If the block's frequency has changed enough, then
1486 // make sure the block and its successors are in the active set
1487 auto Change = Freq[I] >= NewFreq ? Freq[I] - NewFreq : NewFreq - Freq[I];
1488 if (Change > Precision) {
1489 ActiveSet.push(I);
1490 IsActive[I] = true;
1491 for (size_t Succ : Successors[I]) {
1492 if (!IsActive[Succ]) {
1493 ActiveSet.push(Succ);
1494 IsActive[Succ] = true;
1495 }
1496 }
1497 }
1498
1499 // Update the frequency for the block
1500 Freq[I] = NewFreq;
1501 }
1502
1503 LLVM_DEBUG(dbgs() << " Completed " << It << " inference iterations"
1504 << format(" (%0.0f per block)", double(It) / Freq.size())
1505 << "\n");
1506#ifndef NDEBUG
1507 LLVM_DEBUG(dbgs() << " Final discrepancy = "
1508 << discrepancy(ProbMatrix, Freq).toString() << "\n");
1509#endif
1510}
1511
1512template <class BT>
1513void BlockFrequencyInfoImpl<BT>::findReachableBlocks(
1514 std::vector<const BlockT *> &Blocks) const {
1515 // Find all blocks to apply inference on, that is, reachable from the entry
1516 // along edges with non-zero probablities
1517 std::queue<const BlockT *> Queue;
1518 SmallPtrSet<const BlockT *, 8> Reachable;
1519 const BlockT *Entry = &F->front();
1520 Queue.push(Entry);
1521 Reachable.insert(Entry);
1522 while (!Queue.empty()) {
1523 const BlockT *SrcBB = Queue.front();
1524 Queue.pop();
1525 for (const BlockT *DstBB : children<const BlockT *>(SrcBB)) {
1526 auto EP = BPI->getEdgeProbability(SrcBB, DstBB);
1527 if (EP.isZero())
1528 continue;
1529 if (Reachable.insert(DstBB).second)
1530 Queue.push(DstBB);
1531 }
1532 }
1533
1534 // Find all blocks to apply inference on, that is, backward reachable from
1535 // the entry along (backward) edges with non-zero probablities
1536 SmallPtrSet<const BlockT *, 8> InverseReachable;
1537 for (const BlockT &BB : *F) {
1538 // An exit block is a block without any successors
1539 bool HasSucc = GraphTraits<const BlockT *>::child_begin(&BB) !=
1540 GraphTraits<const BlockT *>::child_end(&BB);
1541 if (!HasSucc && Reachable.count(&BB)) {
1542 Queue.push(&BB);
1543 InverseReachable.insert(&BB);
1544 }
1545 }
1546 while (!Queue.empty()) {
1547 const BlockT *SrcBB = Queue.front();
1548 Queue.pop();
1549 for (const BlockT *DstBB : children<Inverse<const BlockT *>>(SrcBB)) {
1550 auto EP = BPI->getEdgeProbability(DstBB, SrcBB);
1551 if (EP.isZero())
1552 continue;
1553 if (InverseReachable.insert(DstBB).second)
1554 Queue.push(DstBB);
1555 }
1556 }
1557
1558 // Collect the result
1559 Blocks.reserve(F->size());
1560 for (const BlockT &BB : *F) {
1561 if (Reachable.count(&BB) && InverseReachable.count(&BB)) {
1562 Blocks.push_back(&BB);
1563 }
1564 }
1565}
1566
1567template <class BT>
1568void BlockFrequencyInfoImpl<BT>::initTransitionProbabilities(
1569 const std::vector<const BlockT *> &Blocks,
1570 const DenseMap<const BlockT *, size_t> &BlockIndex,
1571 ProbMatrixType &ProbMatrix) const {
1572 const size_t NumBlocks = Blocks.size();
1573 auto Succs = std::vector<std::vector<std::pair<size_t, Scaled64>>>(NumBlocks);
1574 auto SumProb = std::vector<Scaled64>(NumBlocks);
1575
1576 // Find unique successors and corresponding probabilities for every block
1577 for (size_t Src = 0; Src < NumBlocks; Src++) {
1578 const BlockT *BB = Blocks[Src];
1579 SmallPtrSet<const BlockT *, 2> UniqueSuccs;
1580 for (const auto SI : children<const BlockT *>(BB)) {
1581 // Ignore cold blocks
1582 if (BlockIndex.find(SI) == BlockIndex.end())
1583 continue;
1584 // Ignore parallel edges between BB and SI blocks
1585 if (!UniqueSuccs.insert(SI).second)
1586 continue;
1587 // Ignore jumps with zero probability
1588 auto EP = BPI->getEdgeProbability(BB, SI);
1589 if (EP.isZero())
1590 continue;
1591
1592 auto EdgeProb =
1593 Scaled64::getFraction(EP.getNumerator(), EP.getDenominator());
1594 size_t Dst = BlockIndex.find(SI)->second;
1595 Succs[Src].push_back(std::make_pair(Dst, EdgeProb));
1596 SumProb[Src] += EdgeProb;
1597 }
1598 }
1599
1600 // Add transitions for every jump with positive branch probability
1601 ProbMatrix = ProbMatrixType(NumBlocks);
1602 for (size_t Src = 0; Src < NumBlocks; Src++) {
1603 // Ignore blocks w/o successors
1604 if (Succs[Src].empty())
1605 continue;
1606
1607 assert(!SumProb[Src].isZero() && "Zero sum probability of non-exit block");
1608 for (auto &Jump : Succs[Src]) {
1609 size_t Dst = Jump.first;
1610 Scaled64 Prob = Jump.second;
1611 ProbMatrix[Dst].push_back(std::make_pair(Src, Prob / SumProb[Src]));
1612 }
1613 }
1614
1615 // Add transitions from sinks to the source
1616 size_t EntryIdx = BlockIndex.find(&F->front())->second;
1617 for (size_t Src = 0; Src < NumBlocks; Src++) {
1618 if (Succs[Src].empty()) {
1619 ProbMatrix[EntryIdx].push_back(std::make_pair(Src, Scaled64::getOne()));
1620 }
1621 }
1622}
1623
1624#ifndef NDEBUG
1625template <class BT>
1626BlockFrequencyInfoImplBase::Scaled64 BlockFrequencyInfoImpl<BT>::discrepancy(
1627 const ProbMatrixType &ProbMatrix, const std::vector<Scaled64> &Freq) const {
1628 assert(Freq[0] > 0 && "Incorrectly computed frequency of the entry block");
1629 Scaled64 Discrepancy;
1630 for (size_t I = 0; I < ProbMatrix.size(); I++) {
1631 Scaled64 Sum;
1632 for (const auto &Jump : ProbMatrix[I]) {
1633 Sum += Freq[Jump.first] * Jump.second;
1634 }
1635 Discrepancy += Freq[I] >= Sum ? Freq[I] - Sum : Sum - Freq[I];
1636 }
1637 // Normalizing by the frequency of the entry block
1638 return Discrepancy / Freq[0];
1639}
1640#endif
1641
1642/// \note This should be a lambda, but that crashes GCC 4.7.
1643namespace bfi_detail {
1644
1645template <class BT> struct BlockEdgesAdder {
1646 using BlockT = BT;
1649
1651
1653 : BFI(BFI) {}
1654
1656 const LoopData *OuterLoop) {
1657 const BlockT *BB = BFI.RPOT[Irr.Node.Index];
1658 for (const auto Succ : children<const BlockT *>(BB))
1659 G.addEdge(Irr, BFI.getNode(Succ), OuterLoop);
1660 }
1661};
1662
1663} // end namespace bfi_detail
1664
1665template <class BT>
1666void BlockFrequencyInfoImpl<BT>::computeIrreducibleMass(
1667 LoopData *OuterLoop, std::list<LoopData>::iterator Insert) {
1668 LLVM_DEBUG(dbgs() << "analyze-irreducible-in-";
1669 if (OuterLoop) dbgs()
1670 << "loop: " << getLoopName(*OuterLoop) << "\n";
1671 else dbgs() << "function\n");
1672
1673 using namespace bfi_detail;
1674
1675 // Ideally, addBlockEdges() would be declared here as a lambda, but that
1676 // crashes GCC 4.7.
1677 BlockEdgesAdder<BT> addBlockEdges(*this);
1678 IrreducibleGraph G(*this, OuterLoop, addBlockEdges);
1679
1680 for (auto &L : analyzeIrreducible(G, OuterLoop, Insert))
1681 computeMassInLoop(L);
1682
1683 if (!OuterLoop)
1684 return;
1685 updateLoopWithIrreducible(*OuterLoop);
1686}
1687
1688// A helper function that converts a branch probability into weight.
1690 return Prob.getNumerator();
1691}
1692
1693template <class BT>
1694bool
1695BlockFrequencyInfoImpl<BT>::propagateMassToSuccessors(LoopData *OuterLoop,
1696 const BlockNode &Node) {
1697 LLVM_DEBUG(dbgs() << " - node: " << getBlockName(Node) << "\n");
1698 // Calculate probability for successors.
1699 Distribution Dist;
1700 if (auto *Loop = Working[Node.Index].getPackagedLoop()) {
1701 assert(Loop != OuterLoop && "Cannot propagate mass in a packaged loop");
1702 if (!addLoopSuccessorsToDist(OuterLoop, *Loop, Dist))
1703 // Irreducible backedge.
1704 return false;
1705 } else {
1706 const BlockT *BB = getBlock(Node);
1707 for (auto SI = GraphTraits<const BlockT *>::child_begin(BB),
1708 SE = GraphTraits<const BlockT *>::child_end(BB);
1709 SI != SE; ++SI)
1710 if (!addToDist(
1711 Dist, OuterLoop, Node, getNode(*SI),
1712 getWeightFromBranchProb(BPI->getEdgeProbability(BB, SI))))
1713 // Irreducible backedge.
1714 return false;
1715 }
1716
1717 // Distribute mass to successors, saving exit and backedge data in the
1718 // loop header.
1719 distributeMass(Node, OuterLoop, Dist);
1720 return true;
1721}
1722
1723template <class BT>
1725 if (!F)
1726 return OS;
1727 OS << "block-frequency-info: " << F->getName() << "\n";
1728 for (const BlockT &BB : *F) {
1729 OS << " - " << bfi_detail::getBlockName(&BB) << ": float = ";
1730 getFloatingBlockFreq(&BB).print(OS, 5)
1731 << ", int = " << getBlockFreq(&BB).getFrequency();
1732 if (std::optional<uint64_t> ProfileCount =
1734 F->getFunction(), getNode(&BB)))
1735 OS << ", count = " << *ProfileCount;
1736 if (std::optional<uint64_t> IrrLoopHeaderWeight =
1737 BB.getIrrLoopHeaderWeight())
1738 OS << ", irr_loop_header_weight = " << *IrrLoopHeaderWeight;
1739 OS << "\n";
1740 }
1741
1742 // Add an extra newline for readability.
1743 OS << "\n";
1744 return OS;
1745}
1746
1747template <class BT>
1750 bool Match = true;
1753 for (auto &Entry : Nodes) {
1754 const BlockT *BB = Entry.first;
1755 if (BB) {
1756 ValidNodes[BB] = Entry.second.first;
1757 }
1758 }
1759 for (auto &Entry : Other.Nodes) {
1760 const BlockT *BB = Entry.first;
1761 if (BB) {
1762 OtherValidNodes[BB] = Entry.second.first;
1763 }
1764 }
1765 unsigned NumValidNodes = ValidNodes.size();
1766 unsigned NumOtherValidNodes = OtherValidNodes.size();
1767 if (NumValidNodes != NumOtherValidNodes) {
1768 Match = false;
1769 dbgs() << "Number of blocks mismatch: " << NumValidNodes << " vs "
1770 << NumOtherValidNodes << "\n";
1771 } else {
1772 for (auto &Entry : ValidNodes) {
1773 const BlockT *BB = Entry.first;
1774 BlockNode Node = Entry.second;
1775 if (OtherValidNodes.count(BB)) {
1776 BlockNode OtherNode = OtherValidNodes[BB];
1777 const auto &Freq = Freqs[Node.Index];
1778 const auto &OtherFreq = Other.Freqs[OtherNode.Index];
1779 if (Freq.Integer != OtherFreq.Integer) {
1780 Match = false;
1781 dbgs() << "Freq mismatch: " << bfi_detail::getBlockName(BB) << " "
1782 << Freq.Integer << " vs " << OtherFreq.Integer << "\n";
1783 }
1784 } else {
1785 Match = false;
1786 dbgs() << "Block " << bfi_detail::getBlockName(BB) << " index "
1787 << Node.Index << " does not exist in Other.\n";
1788 }
1789 }
1790 // If there's a valid node in OtherValidNodes that's not in ValidNodes,
1791 // either the above num check or the check on OtherValidNodes will fail.
1792 }
1793 if (!Match) {
1794 dbgs() << "This\n";
1795 print(dbgs());
1796 dbgs() << "Other\n";
1797 Other.print(dbgs());
1798 }
1799 assert(Match && "BFI mismatch");
1800}
1801
1802// Graph trait base class for block frequency information graph
1803// viewer.
1804
1806
1807template <class BlockFrequencyInfoT, class BranchProbabilityInfoT>
1810 using NodeRef = typename GTraits::NodeRef;
1811 using EdgeIter = typename GTraits::ChildIteratorType;
1812 using NodeIter = typename GTraits::nodes_iterator;
1813
1815
1816 explicit BFIDOTGraphTraitsBase(bool isSimple = false)
1818
1819 static StringRef getGraphName(const BlockFrequencyInfoT *G) {
1820 return G->getFunction()->getName();
1821 }
1822
1823 std::string getNodeAttributes(NodeRef Node, const BlockFrequencyInfoT *Graph,
1824 unsigned HotPercentThreshold = 0) {
1825 std::string Result;
1826 if (!HotPercentThreshold)
1827 return Result;
1828
1829 // Compute MaxFrequency on the fly:
1830 if (!MaxFrequency) {
1831 for (NodeIter I = GTraits::nodes_begin(Graph),
1832 E = GTraits::nodes_end(Graph);
1833 I != E; ++I) {
1834 NodeRef N = *I;
1835 MaxFrequency =
1836 std::max(MaxFrequency, Graph->getBlockFreq(N).getFrequency());
1837 }
1838 }
1839 BlockFrequency Freq = Graph->getBlockFreq(Node);
1840 BlockFrequency HotFreq =
1842 BranchProbability::getBranchProbability(HotPercentThreshold, 100));
1843
1844 if (Freq < HotFreq)
1845 return Result;
1846
1847 raw_string_ostream OS(Result);
1848 OS << "color=\"red\"";
1849 OS.flush();
1850 return Result;
1851 }
1852
1853 std::string getNodeLabel(NodeRef Node, const BlockFrequencyInfoT *Graph,
1854 GVDAGType GType, int layout_order = -1) {
1855 std::string Result;
1856 raw_string_ostream OS(Result);
1857
1858 if (layout_order != -1)
1859 OS << Node->getName() << "[" << layout_order << "] : ";
1860 else
1861 OS << Node->getName() << " : ";
1862 switch (GType) {
1863 case GVDT_Fraction:
1864 Graph->printBlockFreq(OS, Node);
1865 break;
1866 case GVDT_Integer:
1867 OS << Graph->getBlockFreq(Node).getFrequency();
1868 break;
1869 case GVDT_Count: {
1870 auto Count = Graph->getBlockProfileCount(Node);
1871 if (Count)
1872 OS << *Count;
1873 else
1874 OS << "Unknown";
1875 break;
1876 }
1877 case GVDT_None:
1878 llvm_unreachable("If we are not supposed to render a graph we should "
1879 "never reach this point.");
1880 }
1881 return Result;
1882 }
1883
1885 const BlockFrequencyInfoT *BFI,
1886 const BranchProbabilityInfoT *BPI,
1887 unsigned HotPercentThreshold = 0) {
1888 std::string Str;
1889 if (!BPI)
1890 return Str;
1891
1892 BranchProbability BP = BPI->getEdgeProbability(Node, EI);
1893 uint32_t N = BP.getNumerator();
1894 uint32_t D = BP.getDenominator();
1895 double Percent = 100.0 * N / D;
1896 raw_string_ostream OS(Str);
1897 OS << format("label=\"%.1f%%\"", Percent);
1898
1899 if (HotPercentThreshold) {
1900 BlockFrequency EFreq = BFI->getBlockFreq(Node) * BP;
1902 BranchProbability(HotPercentThreshold, 100);
1903
1904 if (EFreq >= HotFreq) {
1905 OS << ",color=\"red\"";
1906 }
1907 }
1908
1909 OS.flush();
1910 return Str;
1911 }
1912};
1913
1914} // end namespace llvm
1915
1916#undef DEBUG_TYPE
1917
1918#endif // LLVM_ANALYSIS_BLOCKFREQUENCYINFOIMPL_H
BitTracker BT
Definition: BitTracker.cpp:73
This file implements the BitVector class.
static GCRegistry::Add< OcamlGC > B("ocaml", "ocaml 3.10-compatible GC")
static GCRegistry::Add< StatepointGC > D("statepoint-example", "an example strategy for statepoint")
static GCRegistry::Add< CoreCLRGC > E("coreclr", "CoreCLR-compatible GC")
#define LLVM_DEBUG(X)
Definition: Debug.h:101
This file defines the DenseMap class.
This file defines the DenseSet and SmallDenseSet classes.
static GCMetadataPrinterRegistry::Add< ErlangGCPrinter > X("erlang", "erlang-compatible garbage collector")
This file defines the little GraphTraits<X> template class that should be specialized by classes that...
Hexagon Hardware Loops
static bool isZero(Value *V, const DataLayout &DL, DominatorTree *DT, AssumptionCache *AC)
Definition: Lint.cpp:524
#define F(x, y, z)
Definition: MD5.cpp:55
#define I(x, y, z)
Definition: MD5.cpp:58
#define G(x, y, z)
Definition: MD5.cpp:56
#define H(x, y, z)
Definition: MD5.cpp:57
Branch Probability Basic Block static false std::string getBlockName(const MachineBasicBlock *BB)
Helper to print the name of a MBB.
#define P(N)
This file builds on the ADT/GraphTraits.h file to build a generic graph post order iterator.
@ SI
assert(ImpDefSCC.getReg()==AMDGPU::SCC &&ImpDefSCC.isDef())
This file defines the SmallPtrSet class.
This file defines the SmallVector class.
This file defines the SparseBitVector class.
ScaledNumber< uint64_t > Scaled64
Value handle that asserts if the Value is deleted.
Definition: ValueHandle.h:264
LLVM Basic Block Representation.
Definition: BasicBlock.h:56
Base class for BlockFrequencyInfoImpl.
std::vector< WorkingData > Working
Loop data: see initializeLoops().
virtual ~BlockFrequencyInfoImplBase()=default
Virtual destructor.
std::list< LoopData > Loops
Indexed information about loops.
raw_ostream & printBlockFreq(raw_ostream &OS, const BlockNode &Node) const
bool addLoopSuccessorsToDist(const LoopData *OuterLoop, LoopData &Loop, Distribution &Dist)
Add all edges out of a packaged loop to the distribution.
std::string getLoopName(const LoopData &Loop) const
bool isIrrLoopHeader(const BlockNode &Node)
void computeLoopScale(LoopData &Loop)
Compute the loop scale for a loop.
void packageLoop(LoopData &Loop)
Package up a loop.
virtual raw_ostream & print(raw_ostream &OS) const
virtual std::string getBlockName(const BlockNode &Node) const
void finalizeMetrics()
Finalize frequency metrics.
void setBlockFreq(const BlockNode &Node, uint64_t Freq)
void updateLoopWithIrreducible(LoopData &OuterLoop)
Update a loop after packaging irreducible SCCs inside of it.
std::optional< uint64_t > getBlockProfileCount(const Function &F, const BlockNode &Node, bool AllowSynthetic=false) const
BlockFrequency getBlockFreq(const BlockNode &Node) const
void distributeIrrLoopHeaderMass(Distribution &Dist)
iterator_range< std::list< LoopData >::iterator > analyzeIrreducible(const bfi_detail::IrreducibleGraph &G, LoopData *OuterLoop, std::list< LoopData >::iterator Insert)
Analyze irreducible SCCs.
bool addToDist(Distribution &Dist, const LoopData *OuterLoop, const BlockNode &Pred, const BlockNode &Succ, uint64_t Weight)
Add an edge to the distribution.
Scaled64 getFloatingBlockFreq(const BlockNode &Node) const
void distributeMass(const BlockNode &Source, LoopData *OuterLoop, Distribution &Dist)
Distribute mass according to a distribution.
SparseBitVector IsIrrLoopHeader
Whether each block is an irreducible loop header.
std::vector< FrequencyData > Freqs
Data about each block. This is used downstream.
std::optional< uint64_t > getProfileCountFromFreq(const Function &F, uint64_t Freq, bool AllowSynthetic=false) const
void adjustLoopHeaderMass(LoopData &Loop)
Adjust the mass of all headers in an irreducible loop.
Shared implementation for block frequency analysis.
bool isIrrLoopHeader(const BlockT *BB)
std::optional< uint64_t > getBlockProfileCount(const Function &F, const BlockT *BB, bool AllowSynthetic=false) const
const BranchProbabilityInfoT & getBPI() const
const FunctionT * getFunction() const
std::optional< uint64_t > getProfileCountFromFreq(const Function &F, uint64_t Freq, bool AllowSynthetic=false) const
void verifyMatch(BlockFrequencyInfoImpl< BT > &Other) const
Scaled64 getFloatingBlockFreq(const BlockT *BB) const
void calculate(const FunctionT &F, const BranchProbabilityInfoT &BPI, const LoopInfoT &LI)
raw_ostream & printBlockFreq(raw_ostream &OS, const BlockT *BB) const
void setBlockFreq(const BlockT *BB, uint64_t Freq)
raw_ostream & print(raw_ostream &OS) const override
Print the frequencies for the current function.
BlockFrequency getBlockFreq(const BlockT *BB) const
Analysis providing branch probability information.
static BranchProbability getBranchProbability(uint64_t Numerator, uint64_t Denominator)
static uint32_t getDenominator()
uint32_t getNumerator() const
Value handle with callbacks on RAUW and destruction.
Definition: ValueHandle.h:383
ValueT lookup(const_arg_type_t< KeyT > Val) const
lookup - Return the entry for the specified key, or a default constructed value if no such entry exis...
Definition: DenseMap.h:197
bool erase(const KeyT &Val)
Definition: DenseMap.h:302
unsigned size() const
Definition: DenseMap.h:99
iterator begin()
Definition: DenseMap.h:75
size_type count(const_arg_type_t< KeyT > Val) const
Return 1 if the specified key is in the map, 0 otherwise.
Definition: DenseMap.h:145
Class to represent profile counts.
Definition: Function.h:252
Represents a single loop in the control flow graph.
Definition: LoopInfo.h:547
Simple representation of a scaled number.
Definition: ScaledNumber.h:493
size_t size() const
Definition: SmallVector.h:91
iterator insert(iterator I, T &&Elt)
Definition: SmallVector.h:809
void resize(size_type N)
Definition: SmallVector.h:642
StringRef - Represent a constant reference to a string, i.e.
Definition: StringRef.h:50
std::string str() const
str - Get the contents as an std::string.
Definition: StringRef.h:222
Twine - A lightweight data structure for efficiently representing the concatenation of temporary valu...
Definition: Twine.h:81
The instances of the Type class are immutable: once they are created, they are never changed.
Definition: Type.h:45
StringRef getName() const
Return a constant reference to the value's name.
Definition: Value.cpp:308
void deleted() override
Callback for Value destruction.
BFICallbackVH(const BasicBlock *BB, BFIImplT *BFIImpl)
bool operator<(BlockMass X) const
bool operator>(BlockMass X) const
raw_ostream & print(raw_ostream &OS) const
bool operator==(BlockMass X) const
BlockMass & operator-=(BlockMass X)
Subtract another mass.
bool operator<=(BlockMass X) const
BlockMass & operator*=(BranchProbability P)
bool operator!=(BlockMass X) const
BlockMass & operator+=(BlockMass X)
Add another mass.
bool operator>=(BlockMass X) const
ScaledNumber< uint64_t > toScaled() const
Convert to scaled number.
A range adaptor for a pair of iterators.
This class implements an extremely fast bulk output stream that can only output to a stream.
Definition: raw_ostream.h:52
A raw_ostream that writes to an std::string.
Definition: raw_ostream.h:642
This provides a very simple, boring adaptor for a begin and end iterator into a range type.
#define llvm_unreachable(msg)
Marks that the current location is not supposed to be reachable.
std::string getBlockName(const BlockT *BB)
Get the name of a MachineBasicBlock.
BlockMass operator*(BlockMass L, BranchProbability R)
BlockMass operator+(BlockMass L, BlockMass R)
raw_ostream & operator<<(raw_ostream &OS, BlockMass X)
BlockMass operator-(BlockMass L, BlockMass R)
std::optional< const char * > toString(const std::optional< DWARFFormValue > &V)
Take an optional DWARFFormValue and try to extract a string value from it.
This is an optimization pass for GlobalISel generic memory operations.
Definition: AddressRanges.h:18
uint32_t getWeightFromBranchProb(const BranchProbability Prob)
Function::ProfileCount ProfileCount
iterator_range< T > make_range(T x, T y)
Convenience function for iterating over sub-ranges.
llvm::cl::opt< unsigned > IterativeBFIMaxIterationsPerBlock
po_iterator< T > po_begin(const T &G)
Printable print(const GCNRegPressure &RP, const GCNSubtarget *ST=nullptr)
llvm::cl::opt< bool > UseIterativeBFIInference
raw_ostream & dbgs()
dbgs() - This returns a reference to a raw_ostream for debugging messages.
Definition: Debug.cpp:163
format_object< Ts... > format(const char *Fmt, const Ts &... Vals)
These are helper functions used to produce formatted output.
Definition: Format.h:124
llvm::cl::opt< bool > CheckBFIUnknownBlockQueries
Expected< ExpressionValue > max(const ExpressionValue &Lhs, const ExpressionValue &Rhs)
Definition: FileCheck.cpp:337
iterator_range< typename GraphTraits< GraphType >::ChildIteratorType > children(const typename GraphTraits< GraphType >::NodeRef &G)
Definition: GraphTraits.h:123
po_iterator< T > po_end(const T &G)
llvm::cl::opt< double > IterativeBFIPrecision
Definition: BitVector.h:851
#define N
std::string getNodeAttributes(NodeRef Node, const BlockFrequencyInfoT *Graph, unsigned HotPercentThreshold=0)
typename GTraits::nodes_iterator NodeIter
typename GTraits::NodeRef NodeRef
typename GTraits::ChildIteratorType EdgeIter
std::string getNodeLabel(NodeRef Node, const BlockFrequencyInfoT *Graph, GVDAGType GType, int layout_order=-1)
std::string getEdgeAttributes(NodeRef Node, EdgeIter EI, const BlockFrequencyInfoT *BFI, const BranchProbabilityInfoT *BPI, unsigned HotPercentThreshold=0)
BFIDOTGraphTraitsBase(bool isSimple=false)
static StringRef getGraphName(const BlockFrequencyInfoT *G)
Distribution of unscaled probability weight.
void addBackedge(const BlockNode &Node, uint64_t Amount)
WeightList Weights
Individual successor weights.
void addExit(const BlockNode &Node, uint64_t Amount)
void addLocal(const BlockNode &Node, uint64_t Amount)
bool isHeader(const BlockNode &Node) const
LoopData(LoopData *Parent, It1 FirstHeader, It1 LastHeader, It2 FirstOther, It2 LastOther)
ExitMap Exits
Successor edges (and weights).
bool IsPackaged
Whether this has been packaged.
LoopData(LoopData *Parent, const BlockNode &Header)
NodeList::const_iterator members_end() const
NodeList::const_iterator members_begin() const
NodeList Nodes
Header and the members of the loop.
HeaderMassList BackedgeMass
Mass returned to each loop header.
HeaderMassList::difference_type getHeaderIndex(const BlockNode &B)
iterator_range< NodeList::const_iterator > members() const
Weight(DistType Type, BlockNode TargetNode, uint64_t Amount)
bool isPackaged() const
Has ContainingLoop been packaged up?
BlockMass Mass
Mass distribution from the entry block.
BlockMass & getMass()
Get the appropriate mass for a node.
bool isAPackage() const
Has Loop been packaged up?
LoopData * Loop
The loop this block is inside.
BlockNode getResolvedNode() const
Resolve a node to its representative.
bool isADoublePackage() const
Has Loop been packaged up twice?
DefaultDOTGraphTraits - This class provides the default implementations of all of the DOTGraphTraits ...
typename GraphType::UnknownGraphTypeError NodeRef
Definition: GraphTraits.h:80
BlockEdgesAdder(const BlockFrequencyInfoImpl< BT > &BFI)
const BlockFrequencyInfoImpl< BT > & BFI
void operator()(IrreducibleGraph &G, IrreducibleGraph::IrrNode &Irr, const LoopData *OuterLoop)
std::deque< const IrrNode * >::const_iterator iterator
Graph of irreducible control flow.
IrreducibleGraph(BFIBase &BFI, const BFIBase::LoopData *OuterLoop, BlockEdgesAdder addBlockEdges)
Construct an explicit graph containing irreducible control flow.
void addEdge(IrrNode &Irr, const BlockNode &Succ, const BFIBase::LoopData *OuterLoop)
void addEdges(const BlockNode &Node, const BFIBase::LoopData *OuterLoop, BlockEdgesAdder addBlockEdges)
SmallDenseMap< uint32_t, IrrNode *, 4 > Lookup
void initialize(const BFIBase::LoopData *OuterLoop, BlockEdgesAdder addBlockEdges)
void addNodesInLoop(const BFIBase::LoopData &OuterLoop)