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/Function.h"
29#include "llvm/IR/ValueHandle.h"
34#include "llvm/Support/Debug.h"
35#include "llvm/Support/Format.h"
38#include <algorithm>
39#include <cassert>
40#include <cstddef>
41#include <cstdint>
42#include <deque>
43#include <iterator>
44#include <limits>
45#include <list>
46#include <optional>
47#include <queue>
48#include <string>
49#include <utility>
50#include <vector>
51
52#define DEBUG_TYPE "block-freq"
53
54namespace llvm {
56
60
61class BranchProbabilityInfo;
62class Function;
63class Loop;
64class LoopInfo;
65class MachineBasicBlock;
66class MachineBranchProbabilityInfo;
67class MachineFunction;
68class MachineLoop;
69class MachineLoopInfo;
70
71namespace bfi_detail {
72
73struct IrreducibleGraph;
74
75// This is part of a workaround for a GCC 4.7 crash on lambdas.
76template <class BT> struct BlockEdgesAdder;
77
78/// Mass of a block.
79///
80/// This class implements a sort of fixed-point fraction always between 0.0 and
81/// 1.0. getMass() == std::numeric_limits<uint64_t>::max() indicates a value of
82/// 1.0.
83///
84/// Masses can be added and subtracted. Simple saturation arithmetic is used,
85/// so arithmetic operations never overflow or underflow.
86///
87/// Masses can be multiplied. Multiplication treats full mass as 1.0 and uses
88/// an inexpensive floating-point algorithm that's off-by-one (almost, but not
89/// quite, maximum precision).
90///
91/// Masses can be scaled by \a BranchProbability at maximum precision.
92class BlockMass {
93 uint64_t Mass = 0;
94
95public:
96 BlockMass() = default;
97 explicit BlockMass(uint64_t Mass) : Mass(Mass) {}
98
99 static BlockMass getEmpty() { return BlockMass(); }
100
102 return BlockMass(std::numeric_limits<uint64_t>::max());
103 }
104
105 uint64_t getMass() const { return Mass; }
106
107 bool isFull() const { return Mass == std::numeric_limits<uint64_t>::max(); }
108 bool isEmpty() const { return !Mass; }
109
110 bool operator!() const { return isEmpty(); }
111
112 /// Add another mass.
113 ///
114 /// Adds another mass, saturating at \a isFull() rather than overflowing.
116 uint64_t Sum = Mass + X.Mass;
117 Mass = Sum < Mass ? std::numeric_limits<uint64_t>::max() : Sum;
118 return *this;
119 }
120
121 /// Subtract another mass.
122 ///
123 /// Subtracts another mass, saturating at \a isEmpty() rather than
124 /// undeflowing.
126 uint64_t Diff = Mass - X.Mass;
127 Mass = Diff > Mass ? 0 : Diff;
128 return *this;
129 }
130
132 Mass = P.scale(Mass);
133 return *this;
134 }
135
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 bool operator>(BlockMass X) const { return Mass > X.Mass; }
142
143 /// Convert to scaled number.
144 ///
145 /// Convert to \a ScaledNumber. \a isFull() gives 1.0, while \a isEmpty()
146 /// gives slightly above 0.0.
148
149 void dump() const;
151};
152
154 return BlockMass(L) += R;
155}
157 return BlockMass(L) -= R;
158}
160 return BlockMass(L) *= R;
161}
163 return BlockMass(R) *= L;
164}
165
167 return X.print(OS);
168}
169
170} // end namespace bfi_detail
171
172/// Base class for BlockFrequencyInfoImpl
173///
174/// BlockFrequencyInfoImplBase has supporting data structures and some
175/// algorithms for BlockFrequencyInfoImplBase. Only algorithms that depend on
176/// the block type (or that call such algorithms) are skipped here.
177///
178/// Nevertheless, the majority of the overall algorithm documentation lives with
179/// BlockFrequencyInfoImpl. See there for details.
181public:
184
185 /// Representative of a block.
186 ///
187 /// This is a simple wrapper around an index into the reverse-post-order
188 /// traversal of the blocks.
189 ///
190 /// Unlike a block pointer, its order has meaning (location in the
191 /// topological sort) and it's class is the same regardless of block type.
192 struct BlockNode {
194
196
197 BlockNode() : Index(std::numeric_limits<uint32_t>::max()) {}
199
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 bool operator>(const BlockNode &X) const { return Index > X.Index; }
206
207 bool isValid() const { return Index <= getMaxIndex(); }
208
209 static size_t getMaxIndex() {
210 return std::numeric_limits<uint32_t>::max() - 1;
211 }
212 };
213
214 /// Stats about a block itself.
218 };
219
220 /// Data about a loop.
221 ///
222 /// Contains the data necessary to represent a loop as a pseudo-node once it's
223 /// packaged.
224 struct LoopData {
228
229 LoopData *Parent; ///< The parent loop.
230 bool IsPackaged = false; ///< Whether this has been packaged.
231 uint32_t NumHeaders = 1; ///< Number of headers.
232 ExitMap Exits; ///< Successor edges (and weights).
233 NodeList Nodes; ///< Header and the members of the loop.
234 HeaderMassList BackedgeMass; ///< Mass returned to each loop header.
237
239 : Parent(Parent), Nodes(1, Header), BackedgeMass(1) {}
240
241 template <class It1, class It2>
242 LoopData(LoopData *Parent, It1 FirstHeader, It1 LastHeader, It2 FirstOther,
243 It2 LastOther)
244 : Parent(Parent), Nodes(FirstHeader, LastHeader) {
246 Nodes.insert(Nodes.end(), FirstOther, LastOther);
248 }
249
250 bool isHeader(const BlockNode &Node) const {
251 if (isIrreducible())
252 return std::binary_search(Nodes.begin(), Nodes.begin() + NumHeaders,
253 Node);
254 return Node == Nodes[0];
255 }
256
257 BlockNode getHeader() const { return Nodes[0]; }
258 bool isIrreducible() const { return NumHeaders > 1; }
259
261 assert(isHeader(B) && "this is only valid on loop header blocks");
262 if (isIrreducible())
263 return std::lower_bound(Nodes.begin(), Nodes.begin() + NumHeaders, B) -
264 Nodes.begin();
265 return 0;
266 }
267
269 return Nodes.begin() + NumHeaders;
270 }
271
275 }
276 };
277
278 /// Index of loop information.
279 struct WorkingData {
280 BlockNode Node; ///< This node.
281 LoopData *Loop = nullptr; ///< The loop this block is inside.
282 BlockMass Mass; ///< Mass distribution from the entry block.
283
285
286 bool isLoopHeader() const { return Loop && Loop->isHeader(Node); }
287
288 bool isDoubleLoopHeader() const {
289 return isLoopHeader() && Loop->Parent && Loop->Parent->isIrreducible() &&
290 Loop->Parent->isHeader(Node);
291 }
292
294 if (!isLoopHeader())
295 return Loop;
296 if (!isDoubleLoopHeader())
297 return Loop->Parent;
298 return Loop->Parent->Parent;
299 }
300
301 /// Resolve a node to its representative.
302 ///
303 /// Get the node currently representing Node, which could be a containing
304 /// loop.
305 ///
306 /// This function should only be called when distributing mass. As long as
307 /// there are no irreducible edges to Node, then it will have complexity
308 /// O(1) in this context.
309 ///
310 /// In general, the complexity is O(L), where L is the number of loop
311 /// headers Node has been packaged into. Since this method is called in
312 /// the context of distributing mass, L will be the number of loop headers
313 /// an early exit edge jumps out of.
315 auto *L = getPackagedLoop();
316 return L ? L->getHeader() : Node;
317 }
318
320 if (!Loop || !Loop->IsPackaged)
321 return nullptr;
322 auto *L = Loop;
323 while (L->Parent && L->Parent->IsPackaged)
324 L = L->Parent;
325 return L;
326 }
327
328 /// Get the appropriate mass for a node.
329 ///
330 /// Get appropriate mass for Node. If Node is a loop-header (whose loop
331 /// has been packaged), returns the mass of its pseudo-node. If it's a
332 /// node inside a packaged loop, it returns the loop's mass.
334 if (!isAPackage())
335 return Mass;
336 if (!isADoublePackage())
337 return Loop->Mass;
338 return Loop->Parent->Mass;
339 }
340
341 /// Has ContainingLoop been packaged up?
342 bool isPackaged() const { return getResolvedNode() != Node; }
343
344 /// Has Loop been packaged up?
345 bool isAPackage() const { return isLoopHeader() && Loop->IsPackaged; }
346
347 /// Has Loop been packaged up twice?
348 bool isADoublePackage() const {
349 return isDoubleLoopHeader() && Loop->Parent->IsPackaged;
350 }
351 };
352
353 /// Unscaled probability weight.
354 ///
355 /// Probability weight for an edge in the graph (including the
356 /// successor/target node).
357 ///
358 /// All edges in the original function are 32-bit. However, exit edges from
359 /// loop packages are taken from 64-bit exit masses, so we need 64-bits of
360 /// space in general.
361 ///
362 /// In addition to the raw weight amount, Weight stores the type of the edge
363 /// in the current context (i.e., the context of the loop being processed).
364 /// Is this a local edge within the loop, an exit from the loop, or a
365 /// backedge to the loop header?
366 struct Weight {
371
372 Weight() = default;
375 };
376
377 /// Distribution of unscaled probability weight.
378 ///
379 /// Distribution of unscaled probability weight to a set of successors.
380 ///
381 /// This class collates the successor edge weights for later processing.
382 ///
383 /// \a DidOverflow indicates whether \a Total did overflow while adding to
384 /// the distribution. It should never overflow twice.
387
388 WeightList Weights; ///< Individual successor weights.
389 uint64_t Total = 0; ///< Sum of all weights.
390 bool DidOverflow = false; ///< Whether \a Total did overflow.
391
392 Distribution() = default;
393
394 void addLocal(const BlockNode &Node, uint64_t Amount) {
395 add(Node, Amount, Weight::Local);
396 }
397
398 void addExit(const BlockNode &Node, uint64_t Amount) {
399 add(Node, Amount, Weight::Exit);
400 }
401
402 void addBackedge(const BlockNode &Node, uint64_t Amount) {
403 add(Node, Amount, Weight::Backedge);
404 }
405
406 /// Normalize the distribution.
407 ///
408 /// Combines multiple edges to the same \a Weight::TargetNode and scales
409 /// down so that \a Total fits into 32-bits.
410 ///
411 /// This is linear in the size of \a Weights. For the vast majority of
412 /// cases, adjacent edge weights are combined by sorting WeightList and
413 /// combining adjacent weights. However, for very large edge lists an
414 /// auxiliary hash table is used.
415 void normalize();
416
417 private:
418 void add(const BlockNode &Node, uint64_t Amount, Weight::DistType Type);
419 };
420
421 /// Data about each block. This is used downstream.
422 std::vector<FrequencyData> Freqs;
423
424 /// Whether each block is an irreducible loop header.
425 /// This is used downstream.
427
428 /// Loop data: see initializeLoops().
429 std::vector<WorkingData> Working;
430
431 /// Indexed information about loops.
432 std::list<LoopData> Loops;
433
434 /// Virtual destructor.
435 ///
436 /// Need a virtual destructor to mask the compiler warning about
437 /// getBlockName().
438 virtual ~BlockFrequencyInfoImplBase() = default;
439
440 /// Add all edges out of a packaged loop to the distribution.
441 ///
442 /// Adds all edges from LocalLoopHead to Dist. Calls addToDist() to add each
443 /// successor edge.
444 ///
445 /// \return \c true unless there's an irreducible backedge.
446 bool addLoopSuccessorsToDist(const LoopData *OuterLoop, LoopData &Loop,
447 Distribution &Dist);
448
449 /// Add an edge to the distribution.
450 ///
451 /// Adds an edge to Succ to Dist. If \c LoopHead.isValid(), then whether the
452 /// edge is local/exit/backedge is in the context of LoopHead. Otherwise,
453 /// every edge should be a local edge (since all the loops are packaged up).
454 ///
455 /// \return \c true unless aborted due to an irreducible backedge.
456 bool addToDist(Distribution &Dist, const LoopData *OuterLoop,
457 const BlockNode &Pred, const BlockNode &Succ, uint64_t Weight);
458
459 /// Analyze irreducible SCCs.
460 ///
461 /// Separate irreducible SCCs from \c G, which is an explicit graph of \c
462 /// OuterLoop (or the top-level function, if \c OuterLoop is \c nullptr).
463 /// Insert them into \a Loops before \c Insert.
464 ///
465 /// \return the \c LoopData nodes representing the irreducible SCCs.
468 std::list<LoopData>::iterator Insert);
469
470 /// Update a loop after packaging irreducible SCCs inside of it.
471 ///
472 /// Update \c OuterLoop. Before finding irreducible control flow, it was
473 /// partway through \a computeMassInLoop(), so \a LoopData::Exits and \a
474 /// LoopData::BackedgeMass need to be reset. Also, nodes that were packaged
475 /// up need to be removed from \a OuterLoop::Nodes.
476 void updateLoopWithIrreducible(LoopData &OuterLoop);
477
478 /// Distribute mass according to a distribution.
479 ///
480 /// Distributes the mass in Source according to Dist. If LoopHead.isValid(),
481 /// backedges and exits are stored in its entry in Loops.
482 ///
483 /// Mass is distributed in parallel from two copies of the source mass.
484 void distributeMass(const BlockNode &Source, LoopData *OuterLoop,
485 Distribution &Dist);
486
487 /// Compute the loop scale for a loop.
489
490 /// Adjust the mass of all headers in an irreducible loop.
491 ///
492 /// Initially, irreducible loops are assumed to distribute their mass
493 /// equally among its headers. This can lead to wrong frequency estimates
494 /// since some headers may be executed more frequently than others.
495 ///
496 /// This adjusts header mass distribution so it matches the weights of
497 /// the backedges going into each of the loop headers.
499
501
502 /// Package up a loop.
504
505 /// Unwrap loops.
506 void unwrapLoops();
507
508 /// Finalize frequency metrics.
509 ///
510 /// Calculates final frequencies and cleans up no-longer-needed data
511 /// structures.
512 void finalizeMetrics();
513
514 /// Clear all memory.
515 void clear();
516
517 virtual std::string getBlockName(const BlockNode &Node) const;
518 std::string getLoopName(const LoopData &Loop) const;
519
520 virtual raw_ostream &print(raw_ostream &OS) const { return OS; }
521 void dump() const { print(dbgs()); }
522
523 Scaled64 getFloatingBlockFreq(const BlockNode &Node) const;
524
525 BlockFrequency getBlockFreq(const BlockNode &Node) const;
526 std::optional<uint64_t>
527 getBlockProfileCount(const Function &F, const BlockNode &Node,
528 bool AllowSynthetic = false) const;
529 std::optional<uint64_t>
531 bool AllowSynthetic = false) const;
532 bool isIrrLoopHeader(const BlockNode &Node);
533
534 void setBlockFreq(const BlockNode &Node, uint64_t Freq);
535
536 raw_ostream &printBlockFreq(raw_ostream &OS, const BlockNode &Node) const;
538 const BlockFrequency &Freq) const;
539
541 assert(!Freqs.empty());
542 return Freqs[0].Integer;
543 }
544};
545
546namespace bfi_detail {
547
548template <class BlockT> struct TypeMap {};
549template <> struct TypeMap<BasicBlock> {
554 using LoopT = Loop;
556};
557template <> struct TypeMap<MachineBasicBlock> {
564};
565
566template <class BlockT, class BFIImplT>
568
569/// Get the name of a MachineBasicBlock.
570///
571/// Get the name of a MachineBasicBlock. It's templated so that including from
572/// CodeGen is unnecessary (that would be a layering issue).
573///
574/// This is used mainly for debug output. The name is similar to
575/// MachineBasicBlock::getFullName(), but skips the name of the function.
576template <class BlockT> std::string getBlockName(const BlockT *BB) {
577 assert(BB && "Unexpected nullptr");
578 auto MachineName = "BB" + Twine(BB->getNumber());
579 if (BB->getBasicBlock())
580 return (MachineName + "[" + BB->getName() + "]").str();
581 return MachineName.str();
582}
583/// Get the name of a BasicBlock.
584template <> inline std::string getBlockName(const BasicBlock *BB) {
585 assert(BB && "Unexpected nullptr");
586 return BB->getName().str();
587}
588
589/// Graph of irreducible control flow.
590///
591/// This graph is used for determining the SCCs in a loop (or top-level
592/// function) that has irreducible control flow.
593///
594/// During the block frequency algorithm, the local graphs are defined in a
595/// light-weight way, deferring to the \a BasicBlock or \a MachineBasicBlock
596/// graphs for most edges, but getting others from \a LoopData::ExitMap. The
597/// latter only has successor information.
598///
599/// \a IrreducibleGraph makes this graph explicit. It's in a form that can use
600/// \a GraphTraits (so that \a analyzeIrreducible() can use \a scc_iterator),
601/// and it explicitly lists predecessors and successors. The initialization
602/// that relies on \c MachineBasicBlock is defined in the header.
605
607
609 struct IrrNode {
611 unsigned NumIn = 0;
612 std::deque<const IrrNode *> Edges;
613
615
616 using iterator = std::deque<const IrrNode *>::const_iterator;
617
618 iterator pred_begin() const { return Edges.begin(); }
619 iterator succ_begin() const { return Edges.begin() + NumIn; }
620 iterator pred_end() const { return succ_begin(); }
621 iterator succ_end() const { return Edges.end(); }
622 };
624 const IrrNode *StartIrr = nullptr;
625 std::vector<IrrNode> Nodes;
627
628 /// Construct an explicit graph containing irreducible control flow.
629 ///
630 /// Construct an explicit graph of the control flow in \c OuterLoop (or the
631 /// top-level function, if \c OuterLoop is \c nullptr). Uses \c
632 /// addBlockEdges to add block successors that have not been packaged into
633 /// loops.
634 ///
635 /// \a BlockFrequencyInfoImpl::computeIrreducibleMass() is the only expected
636 /// user of this.
637 template <class BlockEdgesAdder>
639 BlockEdgesAdder addBlockEdges) : BFI(BFI) {
640 initialize(OuterLoop, addBlockEdges);
641 }
642
643 template <class BlockEdgesAdder>
644 void initialize(const BFIBase::LoopData *OuterLoop,
645 BlockEdgesAdder addBlockEdges);
646 void addNodesInLoop(const BFIBase::LoopData &OuterLoop);
647 void addNodesInFunction();
648
649 void addNode(const BlockNode &Node) {
650 Nodes.emplace_back(Node);
651 BFI.Working[Node.Index].getMass() = BlockMass::getEmpty();
652 }
653
654 void indexNodes();
655 template <class BlockEdgesAdder>
656 void addEdges(const BlockNode &Node, const BFIBase::LoopData *OuterLoop,
657 BlockEdgesAdder addBlockEdges);
658 void addEdge(IrrNode &Irr, const BlockNode &Succ,
659 const BFIBase::LoopData *OuterLoop);
660};
661
662template <class BlockEdgesAdder>
664 BlockEdgesAdder addBlockEdges) {
665 if (OuterLoop) {
666 addNodesInLoop(*OuterLoop);
667 for (auto N : OuterLoop->Nodes)
668 addEdges(N, OuterLoop, addBlockEdges);
669 } else {
671 for (uint32_t Index = 0; Index < BFI.Working.size(); ++Index)
672 addEdges(Index, OuterLoop, addBlockEdges);
673 }
675}
676
677template <class BlockEdgesAdder>
679 const BFIBase::LoopData *OuterLoop,
680 BlockEdgesAdder addBlockEdges) {
681 auto L = Lookup.find(Node.Index);
682 if (L == Lookup.end())
683 return;
684 IrrNode &Irr = *L->second;
685 const auto &Working = BFI.Working[Node.Index];
686
687 if (Working.isAPackage())
688 for (const auto &I : Working.Loop->Exits)
689 addEdge(Irr, I.first, OuterLoop);
690 else
691 addBlockEdges(*this, Irr, OuterLoop);
692}
693
694} // end namespace bfi_detail
695
696/// Shared implementation for block frequency analysis.
697///
698/// This is a shared implementation of BlockFrequencyInfo and
699/// MachineBlockFrequencyInfo, and calculates the relative frequencies of
700/// blocks.
701///
702/// LoopInfo defines a loop as a "non-trivial" SCC dominated by a single block,
703/// which is called the header. A given loop, L, can have sub-loops, which are
704/// loops within the subgraph of L that exclude its header. (A "trivial" SCC
705/// consists of a single block that does not have a self-edge.)
706///
707/// In addition to loops, this algorithm has limited support for irreducible
708/// SCCs, which are SCCs with multiple entry blocks. Irreducible SCCs are
709/// discovered on the fly, and modelled as loops with multiple headers.
710///
711/// The headers of irreducible sub-SCCs consist of its entry blocks and all
712/// nodes that are targets of a backedge within it (excluding backedges within
713/// true sub-loops). Block frequency calculations act as if a block is
714/// inserted that intercepts all the edges to the headers. All backedges and
715/// entries point to this block. Its successors are the headers, which split
716/// the frequency evenly.
717///
718/// This algorithm leverages BlockMass and ScaledNumber to maintain precision,
719/// separates mass distribution from loop scaling, and dithers to eliminate
720/// probability mass loss.
721///
722/// The implementation is split between BlockFrequencyInfoImpl, which knows the
723/// type of graph being modelled (BasicBlock vs. MachineBasicBlock), and
724/// BlockFrequencyInfoImplBase, which doesn't. The base class uses \a
725/// BlockNode, a wrapper around a uint32_t. BlockNode is numbered from 0 in
726/// reverse-post order. This gives two advantages: it's easy to compare the
727/// relative ordering of two nodes, and maps keyed on BlockT can be represented
728/// by vectors.
729///
730/// This algorithm is O(V+E), unless there is irreducible control flow, in
731/// which case it's O(V*E) in the worst case.
732///
733/// These are the main stages:
734///
735/// 0. Reverse post-order traversal (\a initializeRPOT()).
736///
737/// Run a single post-order traversal and save it (in reverse) in RPOT.
738/// All other stages make use of this ordering. Save a lookup from BlockT
739/// to BlockNode (the index into RPOT) in Nodes.
740///
741/// 1. Loop initialization (\a initializeLoops()).
742///
743/// Translate LoopInfo/MachineLoopInfo into a form suitable for the rest of
744/// the algorithm. In particular, store the immediate members of each loop
745/// in reverse post-order.
746///
747/// 2. Calculate mass and scale in loops (\a computeMassInLoops()).
748///
749/// For each loop (bottom-up), distribute mass through the DAG resulting
750/// from ignoring backedges and treating sub-loops as a single pseudo-node.
751/// Track the backedge mass distributed to the loop header, and use it to
752/// calculate the loop scale (number of loop iterations). Immediate
753/// members that represent sub-loops will already have been visited and
754/// packaged into a pseudo-node.
755///
756/// Distributing mass in a loop is a reverse-post-order traversal through
757/// the loop. Start by assigning full mass to the Loop header. For each
758/// node in the loop:
759///
760/// - Fetch and categorize the weight distribution for its successors.
761/// If this is a packaged-subloop, the weight distribution is stored
762/// in \a LoopData::Exits. Otherwise, fetch it from
763/// BranchProbabilityInfo.
764///
765/// - Each successor is categorized as \a Weight::Local, a local edge
766/// within the current loop, \a Weight::Backedge, a backedge to the
767/// loop header, or \a Weight::Exit, any successor outside the loop.
768/// The weight, the successor, and its category are stored in \a
769/// Distribution. There can be multiple edges to each successor.
770///
771/// - If there's a backedge to a non-header, there's an irreducible SCC.
772/// The usual flow is temporarily aborted. \a
773/// computeIrreducibleMass() finds the irreducible SCCs within the
774/// loop, packages them up, and restarts the flow.
775///
776/// - Normalize the distribution: scale weights down so that their sum
777/// is 32-bits, and coalesce multiple edges to the same node.
778///
779/// - Distribute the mass accordingly, dithering to minimize mass loss,
780/// as described in \a distributeMass().
781///
782/// In the case of irreducible loops, instead of a single loop header,
783/// there will be several. The computation of backedge masses is similar
784/// but instead of having a single backedge mass, there will be one
785/// backedge per loop header. In these cases, each backedge will carry
786/// a mass proportional to the edge weights along the corresponding
787/// path.
788///
789/// At the end of propagation, the full mass assigned to the loop will be
790/// distributed among the loop headers proportionally according to the
791/// mass flowing through their backedges.
792///
793/// Finally, calculate the loop scale from the accumulated backedge mass.
794///
795/// 3. Distribute mass in the function (\a computeMassInFunction()).
796///
797/// Finally, distribute mass through the DAG resulting from packaging all
798/// loops in the function. This uses the same algorithm as distributing
799/// mass in a loop, except that there are no exit or backedge edges.
800///
801/// 4. Unpackage loops (\a unwrapLoops()).
802///
803/// Initialize each block's frequency to a floating point representation of
804/// its mass.
805///
806/// Visit loops top-down, scaling the frequencies of its immediate members
807/// by the loop's pseudo-node's frequency.
808///
809/// 5. Convert frequencies to a 64-bit range (\a finalizeMetrics()).
810///
811/// Using the min and max frequencies as a guide, translate floating point
812/// frequencies to an appropriate range in uint64_t.
813///
814/// It has some known flaws.
815///
816/// - The model of irreducible control flow is a rough approximation.
817///
818/// Modelling irreducible control flow exactly involves setting up and
819/// solving a group of infinite geometric series. Such precision is
820/// unlikely to be worthwhile, since most of our algorithms give up on
821/// irreducible control flow anyway.
822///
823/// Nevertheless, we might find that we need to get closer. Here's a sort
824/// of TODO list for the model with diminishing returns, to be completed as
825/// necessary.
826///
827/// - The headers for the \a LoopData representing an irreducible SCC
828/// include non-entry blocks. When these extra blocks exist, they
829/// indicate a self-contained irreducible sub-SCC. We could treat them
830/// as sub-loops, rather than arbitrarily shoving the problematic
831/// blocks into the headers of the main irreducible SCC.
832///
833/// - Entry frequencies are assumed to be evenly split between the
834/// headers of a given irreducible SCC, which is the only option if we
835/// need to compute mass in the SCC before its parent loop. Instead,
836/// we could partially compute mass in the parent loop, and stop when
837/// we get to the SCC. Here, we have the correct ratio of entry
838/// masses, which we can use to adjust their relative frequencies.
839/// Compute mass in the SCC, and then continue propagation in the
840/// parent.
841///
842/// - We can propagate mass iteratively through the SCC, for some fixed
843/// number of iterations. Each iteration starts by assigning the entry
844/// blocks their backedge mass from the prior iteration. The final
845/// mass for each block (and each exit, and the total backedge mass
846/// used for computing loop scale) is the sum of all iterations.
847/// (Running this until fixed point would "solve" the geometric
848/// series by simulation.)
850 // This is part of a workaround for a GCC 4.7 crash on lambdas.
851 friend struct bfi_detail::BlockEdgesAdder<BT>;
852
853 using BlockT = typename bfi_detail::TypeMap<BT>::BlockT;
854 using BlockKeyT = typename bfi_detail::TypeMap<BT>::BlockKeyT;
855 using FunctionT = typename bfi_detail::TypeMap<BT>::FunctionT;
856 using BranchProbabilityInfoT =
858 using LoopT = typename bfi_detail::TypeMap<BT>::LoopT;
859 using LoopInfoT = typename bfi_detail::TypeMap<BT>::LoopInfoT;
862 using BFICallbackVH =
864
865 const BranchProbabilityInfoT *BPI = nullptr;
866 const LoopInfoT *LI = nullptr;
867 const FunctionT *F = nullptr;
868
869 // All blocks in reverse postorder.
870 std::vector<const BlockT *> RPOT;
872
873 using rpot_iterator = typename std::vector<const BlockT *>::const_iterator;
874
875 rpot_iterator rpot_begin() const { return RPOT.begin(); }
876 rpot_iterator rpot_end() const { return RPOT.end(); }
877
878 size_t getIndex(const rpot_iterator &I) const { return I - rpot_begin(); }
879
880 BlockNode getNode(const rpot_iterator &I) const {
881 return BlockNode(getIndex(I));
882 }
883
884 BlockNode getNode(const BlockT *BB) const { return Nodes.lookup(BB).first; }
885
886 const BlockT *getBlock(const BlockNode &Node) const {
887 assert(Node.Index < RPOT.size());
888 return RPOT[Node.Index];
889 }
890
891 /// Run (and save) a post-order traversal.
892 ///
893 /// Saves a reverse post-order traversal of all the nodes in \a F.
894 void initializeRPOT();
895
896 /// Initialize loop data.
897 ///
898 /// Build up \a Loops using \a LoopInfo. \a LoopInfo gives us a mapping from
899 /// each block to the deepest loop it's in, but we need the inverse. For each
900 /// loop, we store in reverse post-order its "immediate" members, defined as
901 /// the header, the headers of immediate sub-loops, and all other blocks in
902 /// the loop that are not in sub-loops.
903 void initializeLoops();
904
905 /// Propagate to a block's successors.
906 ///
907 /// In the context of distributing mass through \c OuterLoop, divide the mass
908 /// currently assigned to \c Node between its successors.
909 ///
910 /// \return \c true unless there's an irreducible backedge.
911 bool propagateMassToSuccessors(LoopData *OuterLoop, const BlockNode &Node);
912
913 /// Compute mass in a particular loop.
914 ///
915 /// Assign mass to \c Loop's header, and then for each block in \c Loop in
916 /// reverse post-order, distribute mass to its successors. Only visits nodes
917 /// that have not been packaged into sub-loops.
918 ///
919 /// \pre \a computeMassInLoop() has been called for each subloop of \c Loop.
920 /// \return \c true unless there's an irreducible backedge.
921 bool computeMassInLoop(LoopData &Loop);
922
923 /// Try to compute mass in the top-level function.
924 ///
925 /// Assign mass to the entry block, and then for each block in reverse
926 /// post-order, distribute mass to its successors. Skips nodes that have
927 /// been packaged into loops.
928 ///
929 /// \pre \a computeMassInLoops() has been called.
930 /// \return \c true unless there's an irreducible backedge.
931 bool tryToComputeMassInFunction();
932
933 /// Compute mass in (and package up) irreducible SCCs.
934 ///
935 /// Find the irreducible SCCs in \c OuterLoop, add them to \a Loops (in front
936 /// of \c Insert), and call \a computeMassInLoop() on each of them.
937 ///
938 /// If \c OuterLoop is \c nullptr, it refers to the top-level function.
939 ///
940 /// \pre \a computeMassInLoop() has been called for each subloop of \c
941 /// OuterLoop.
942 /// \pre \c Insert points at the last loop successfully processed by \a
943 /// computeMassInLoop().
944 /// \pre \c OuterLoop has irreducible SCCs.
945 void computeIrreducibleMass(LoopData *OuterLoop,
946 std::list<LoopData>::iterator Insert);
947
948 /// Compute mass in all loops.
949 ///
950 /// For each loop bottom-up, call \a computeMassInLoop().
951 ///
952 /// \a computeMassInLoop() aborts (and returns \c false) on loops that
953 /// contain a irreducible sub-SCCs. Use \a computeIrreducibleMass() and then
954 /// re-enter \a computeMassInLoop().
955 ///
956 /// \post \a computeMassInLoop() has returned \c true for every loop.
957 void computeMassInLoops();
958
959 /// Compute mass in the top-level function.
960 ///
961 /// Uses \a tryToComputeMassInFunction() and \a computeIrreducibleMass() to
962 /// compute mass in the top-level function.
963 ///
964 /// \post \a tryToComputeMassInFunction() has returned \c true.
965 void computeMassInFunction();
966
967 std::string getBlockName(const BlockNode &Node) const override {
968 return bfi_detail::getBlockName(getBlock(Node));
969 }
970
971 /// The current implementation for computing relative block frequencies does
972 /// not handle correctly control-flow graphs containing irreducible loops. To
973 /// resolve the problem, we apply a post-processing step, which iteratively
974 /// updates block frequencies based on the frequencies of their predesessors.
975 /// This corresponds to finding the stationary point of the Markov chain by
976 /// an iterative method aka "PageRank computation".
977 /// The algorithm takes at most O(|E| * IterativeBFIMaxIterations) steps but
978 /// typically converges faster.
979 ///
980 /// Decide whether we want to apply iterative inference for a given function.
981 bool needIterativeInference() const;
982
983 /// Apply an iterative post-processing to infer correct counts for irr loops.
984 void applyIterativeInference();
985
986 using ProbMatrixType = std::vector<std::vector<std::pair<size_t, Scaled64>>>;
987
988 /// Run iterative inference for a probability matrix and initial frequencies.
989 void iterativeInference(const ProbMatrixType &ProbMatrix,
990 std::vector<Scaled64> &Freq) const;
991
992 /// Find all blocks to apply inference on, that is, reachable from the entry
993 /// and backward reachable from exists along edges with positive probability.
994 void findReachableBlocks(std::vector<const BlockT *> &Blocks) const;
995
996 /// Build a matrix of probabilities with transitions (edges) between the
997 /// blocks: ProbMatrix[I] holds pairs (J, P), where Pr[J -> I | J] = P
998 void initTransitionProbabilities(
999 const std::vector<const BlockT *> &Blocks,
1000 const DenseMap<const BlockT *, size_t> &BlockIndex,
1001 ProbMatrixType &ProbMatrix) const;
1002
1003#ifndef NDEBUG
1004 /// Compute the discrepancy between current block frequencies and the
1005 /// probability matrix.
1006 Scaled64 discrepancy(const ProbMatrixType &ProbMatrix,
1007 const std::vector<Scaled64> &Freq) const;
1008#endif
1009
1010public:
1012
1013 const FunctionT *getFunction() const { return F; }
1014
1015 void calculate(const FunctionT &F, const BranchProbabilityInfoT &BPI,
1016 const LoopInfoT &LI);
1017
1019
1020 BlockFrequency getBlockFreq(const BlockT *BB) const {
1021 return BlockFrequencyInfoImplBase::getBlockFreq(getNode(BB));
1022 }
1023
1024 std::optional<uint64_t>
1025 getBlockProfileCount(const Function &F, const BlockT *BB,
1026 bool AllowSynthetic = false) const {
1028 AllowSynthetic);
1029 }
1030
1031 std::optional<uint64_t>
1033 bool AllowSynthetic = false) const {
1035 AllowSynthetic);
1036 }
1037
1038 bool isIrrLoopHeader(const BlockT *BB) {
1040 }
1041
1042 void setBlockFreq(const BlockT *BB, uint64_t Freq);
1043
1044 void forgetBlock(const BlockT *BB) {
1045 // We don't erase corresponding items from `Freqs`, `RPOT` and other to
1046 // avoid invalidating indices. Doing so would have saved some memory, but
1047 // it's not worth it.
1048 Nodes.erase(BB);
1049 }
1050
1051 Scaled64 getFloatingBlockFreq(const BlockT *BB) const {
1053 }
1054
1055 const BranchProbabilityInfoT &getBPI() const { return *BPI; }
1056
1057 /// Print the frequencies for the current function.
1058 ///
1059 /// Prints the frequencies for the blocks in the current function.
1060 ///
1061 /// Blocks are printed in the natural iteration order of the function, rather
1062 /// than reverse post-order. This provides two advantages: writing -analyze
1063 /// tests is easier (since blocks come out in source order), and even
1064 /// unreachable blocks are printed.
1065 ///
1066 /// \a BlockFrequencyInfoImplBase::print() only knows reverse post-order, so
1067 /// we need to override it here.
1068 raw_ostream &print(raw_ostream &OS) const override;
1069
1072
1073 raw_ostream &printBlockFreq(raw_ostream &OS, const BlockT *BB) const {
1075 }
1076
1078};
1079
1080namespace bfi_detail {
1081
1082template <class BFIImplT>
1083class BFICallbackVH<BasicBlock, BFIImplT> : public CallbackVH {
1084 BFIImplT *BFIImpl;
1085
1086public:
1087 BFICallbackVH() = default;
1088
1089 BFICallbackVH(const BasicBlock *BB, BFIImplT *BFIImpl)
1090 : CallbackVH(BB), BFIImpl(BFIImpl) {}
1091
1092 virtual ~BFICallbackVH() = default;
1093
1094 void deleted() override {
1095 BFIImpl->forgetBlock(cast<BasicBlock>(getValPtr()));
1096 }
1097};
1098
1099/// Dummy implementation since MachineBasicBlocks aren't Values, so ValueHandles
1100/// don't apply to them.
1101template <class BFIImplT>
1103public:
1104 BFICallbackVH() = default;
1105 BFICallbackVH(const MachineBasicBlock *, BFIImplT *) {}
1106};
1107
1108} // end namespace bfi_detail
1109
1110template <class BT>
1112 const BranchProbabilityInfoT &BPI,
1113 const LoopInfoT &LI) {
1114 // Save the parameters.
1115 this->BPI = &BPI;
1116 this->LI = &LI;
1117 this->F = &F;
1118
1119 // Clean up left-over data structures.
1121 RPOT.clear();
1122 Nodes.clear();
1123
1124 // Initialize.
1125 LLVM_DEBUG(dbgs() << "\nblock-frequency: " << F.getName()
1126 << "\n================="
1127 << std::string(F.getName().size(), '=') << "\n");
1128 initializeRPOT();
1129 initializeLoops();
1130
1131 // Visit loops in post-order to find the local mass distribution, and then do
1132 // the full function.
1133 computeMassInLoops();
1134 computeMassInFunction();
1135 unwrapLoops();
1136 // Apply a post-processing step improving computed frequencies for functions
1137 // with irreducible loops.
1138 if (needIterativeInference())
1139 applyIterativeInference();
1140 finalizeMetrics();
1141
1143 // To detect BFI queries for unknown blocks, add entries for unreachable
1144 // blocks, if any. This is to distinguish between known/existing unreachable
1145 // blocks and unknown blocks.
1146 for (const BlockT &BB : F)
1147 if (!Nodes.count(&BB))
1148 setBlockFreq(&BB, 0);
1149 }
1150}
1151
1152template <class BT>
1154 if (Nodes.count(BB))
1156 else {
1157 // If BB is a newly added block after BFI is done, we need to create a new
1158 // BlockNode for it assigned with a new index. The index can be determined
1159 // by the size of Freqs.
1160 BlockNode NewNode(Freqs.size());
1161 Nodes[BB] = {NewNode, BFICallbackVH(BB, this)};
1162 Freqs.emplace_back();
1164 }
1165}
1166
1167template <class BT> void BlockFrequencyInfoImpl<BT>::initializeRPOT() {
1168 const BlockT *Entry = &F->front();
1169 RPOT.reserve(F->size());
1170 std::copy(po_begin(Entry), po_end(Entry), std::back_inserter(RPOT));
1171 std::reverse(RPOT.begin(), RPOT.end());
1172
1173 assert(RPOT.size() - 1 <= BlockNode::getMaxIndex() &&
1174 "More nodes in function than Block Frequency Info supports");
1175
1176 LLVM_DEBUG(dbgs() << "reverse-post-order-traversal\n");
1177 for (rpot_iterator I = rpot_begin(), E = rpot_end(); I != E; ++I) {
1178 BlockNode Node = getNode(I);
1179 LLVM_DEBUG(dbgs() << " - " << getIndex(I) << ": " << getBlockName(Node)
1180 << "\n");
1181 Nodes[*I] = {Node, BFICallbackVH(*I, this)};
1182 }
1183
1184 Working.reserve(RPOT.size());
1185 for (size_t Index = 0; Index < RPOT.size(); ++Index)
1186 Working.emplace_back(Index);
1187 Freqs.resize(RPOT.size());
1188}
1189
1190template <class BT> void BlockFrequencyInfoImpl<BT>::initializeLoops() {
1191 LLVM_DEBUG(dbgs() << "loop-detection\n");
1192 if (LI->empty())
1193 return;
1194
1195 // Visit loops top down and assign them an index.
1196 std::deque<std::pair<const LoopT *, LoopData *>> Q;
1197 for (const LoopT *L : *LI)
1198 Q.emplace_back(L, nullptr);
1199 while (!Q.empty()) {
1200 const LoopT *Loop = Q.front().first;
1201 LoopData *Parent = Q.front().second;
1202 Q.pop_front();
1203
1204 BlockNode Header = getNode(Loop->getHeader());
1205 assert(Header.isValid());
1206
1207 Loops.emplace_back(Parent, Header);
1208 Working[Header.Index].Loop = &Loops.back();
1209 LLVM_DEBUG(dbgs() << " - loop = " << getBlockName(Header) << "\n");
1210
1211 for (const LoopT *L : *Loop)
1212 Q.emplace_back(L, &Loops.back());
1213 }
1214
1215 // Visit nodes in reverse post-order and add them to their deepest containing
1216 // loop.
1217 for (size_t Index = 0; Index < RPOT.size(); ++Index) {
1218 // Loop headers have already been mostly mapped.
1219 if (Working[Index].isLoopHeader()) {
1220 LoopData *ContainingLoop = Working[Index].getContainingLoop();
1221 if (ContainingLoop)
1222 ContainingLoop->Nodes.push_back(Index);
1223 continue;
1224 }
1225
1226 const LoopT *Loop = LI->getLoopFor(RPOT[Index]);
1227 if (!Loop)
1228 continue;
1229
1230 // Add this node to its containing loop's member list.
1231 BlockNode Header = getNode(Loop->getHeader());
1232 assert(Header.isValid());
1233 const auto &HeaderData = Working[Header.Index];
1234 assert(HeaderData.isLoopHeader());
1235
1236 Working[Index].Loop = HeaderData.Loop;
1237 HeaderData.Loop->Nodes.push_back(Index);
1238 LLVM_DEBUG(dbgs() << " - loop = " << getBlockName(Header)
1239 << ": member = " << getBlockName(Index) << "\n");
1240 }
1241}
1242
1243template <class BT> void BlockFrequencyInfoImpl<BT>::computeMassInLoops() {
1244 // Visit loops with the deepest first, and the top-level loops last.
1245 for (auto L = Loops.rbegin(), E = Loops.rend(); L != E; ++L) {
1246 if (computeMassInLoop(*L))
1247 continue;
1248 auto Next = std::next(L);
1249 computeIrreducibleMass(&*L, L.base());
1250 L = std::prev(Next);
1251 if (computeMassInLoop(*L))
1252 continue;
1253 llvm_unreachable("unhandled irreducible control flow");
1254 }
1255}
1256
1257template <class BT>
1258bool BlockFrequencyInfoImpl<BT>::computeMassInLoop(LoopData &Loop) {
1259 // Compute mass in loop.
1260 LLVM_DEBUG(dbgs() << "compute-mass-in-loop: " << getLoopName(Loop) << "\n");
1261
1262 if (Loop.isIrreducible()) {
1263 LLVM_DEBUG(dbgs() << "isIrreducible = true\n");
1264 Distribution Dist;
1265 unsigned NumHeadersWithWeight = 0;
1266 std::optional<uint64_t> MinHeaderWeight;
1267 DenseSet<uint32_t> HeadersWithoutWeight;
1268 HeadersWithoutWeight.reserve(Loop.NumHeaders);
1269 for (uint32_t H = 0; H < Loop.NumHeaders; ++H) {
1270 auto &HeaderNode = Loop.Nodes[H];
1271 const BlockT *Block = getBlock(HeaderNode);
1272 IsIrrLoopHeader.set(Loop.Nodes[H].Index);
1273 std::optional<uint64_t> HeaderWeight = Block->getIrrLoopHeaderWeight();
1274 if (!HeaderWeight) {
1275 LLVM_DEBUG(dbgs() << "Missing irr loop header metadata on "
1276 << getBlockName(HeaderNode) << "\n");
1277 HeadersWithoutWeight.insert(H);
1278 continue;
1279 }
1280 LLVM_DEBUG(dbgs() << getBlockName(HeaderNode)
1281 << " has irr loop header weight " << *HeaderWeight
1282 << "\n");
1283 NumHeadersWithWeight++;
1284 uint64_t HeaderWeightValue = *HeaderWeight;
1285 if (!MinHeaderWeight || HeaderWeightValue < MinHeaderWeight)
1286 MinHeaderWeight = HeaderWeightValue;
1287 if (HeaderWeightValue) {
1288 Dist.addLocal(HeaderNode, HeaderWeightValue);
1289 }
1290 }
1291 // As a heuristic, if some headers don't have a weight, give them the
1292 // minimum weight seen (not to disrupt the existing trends too much by
1293 // using a weight that's in the general range of the other headers' weights,
1294 // and the minimum seems to perform better than the average.)
1295 // FIXME: better update in the passes that drop the header weight.
1296 // If no headers have a weight, give them even weight (use weight 1).
1297 if (!MinHeaderWeight)
1298 MinHeaderWeight = 1;
1299 for (uint32_t H : HeadersWithoutWeight) {
1300 auto &HeaderNode = Loop.Nodes[H];
1301 assert(!getBlock(HeaderNode)->getIrrLoopHeaderWeight() &&
1302 "Shouldn't have a weight metadata");
1303 uint64_t MinWeight = *MinHeaderWeight;
1304 LLVM_DEBUG(dbgs() << "Giving weight " << MinWeight << " to "
1305 << getBlockName(HeaderNode) << "\n");
1306 if (MinWeight)
1307 Dist.addLocal(HeaderNode, MinWeight);
1308 }
1309 distributeIrrLoopHeaderMass(Dist);
1310 for (const BlockNode &M : Loop.Nodes)
1311 if (!propagateMassToSuccessors(&Loop, M))
1312 llvm_unreachable("unhandled irreducible control flow");
1313 if (NumHeadersWithWeight == 0)
1314 // No headers have a metadata. Adjust header mass.
1315 adjustLoopHeaderMass(Loop);
1316 } else {
1317 Working[Loop.getHeader().Index].getMass() = BlockMass::getFull();
1318 if (!propagateMassToSuccessors(&Loop, Loop.getHeader()))
1319 llvm_unreachable("irreducible control flow to loop header!?");
1320 for (const BlockNode &M : Loop.members())
1321 if (!propagateMassToSuccessors(&Loop, M))
1322 // Irreducible backedge.
1323 return false;
1324 }
1325
1326 computeLoopScale(Loop);
1327 packageLoop(Loop);
1328 return true;
1329}
1330
1331template <class BT>
1332bool BlockFrequencyInfoImpl<BT>::tryToComputeMassInFunction() {
1333 // Compute mass in function.
1334 LLVM_DEBUG(dbgs() << "compute-mass-in-function\n");
1335 assert(!Working.empty() && "no blocks in function");
1336 assert(!Working[0].isLoopHeader() && "entry block is a loop header");
1337
1338 Working[0].getMass() = BlockMass::getFull();
1339 for (rpot_iterator I = rpot_begin(), IE = rpot_end(); I != IE; ++I) {
1340 // Check for nodes that have been packaged.
1341 BlockNode Node = getNode(I);
1342 if (Working[Node.Index].isPackaged())
1343 continue;
1344
1345 if (!propagateMassToSuccessors(nullptr, Node))
1346 return false;
1347 }
1348 return true;
1349}
1350
1351template <class BT> void BlockFrequencyInfoImpl<BT>::computeMassInFunction() {
1352 if (tryToComputeMassInFunction())
1353 return;
1354 computeIrreducibleMass(nullptr, Loops.begin());
1355 if (tryToComputeMassInFunction())
1356 return;
1357 llvm_unreachable("unhandled irreducible control flow");
1358}
1359
1360template <class BT>
1361bool BlockFrequencyInfoImpl<BT>::needIterativeInference() const {
1363 return false;
1364 if (!F->getFunction().hasProfileData())
1365 return false;
1366 // Apply iterative inference only if the function contains irreducible loops;
1367 // otherwise, computed block frequencies are reasonably correct.
1368 for (auto L = Loops.rbegin(), E = Loops.rend(); L != E; ++L) {
1369 if (L->isIrreducible())
1370 return true;
1371 }
1372 return false;
1373}
1374
1375template <class BT> void BlockFrequencyInfoImpl<BT>::applyIterativeInference() {
1376 // Extract blocks for processing: a block is considered for inference iff it
1377 // can be reached from the entry by edges with a positive probability.
1378 // Non-processed blocks are assigned with the zero frequency and are ignored
1379 // in the computation
1380 std::vector<const BlockT *> ReachableBlocks;
1381 findReachableBlocks(ReachableBlocks);
1382 if (ReachableBlocks.empty())
1383 return;
1384
1385 // The map is used to to index successors/predecessors of reachable blocks in
1386 // the ReachableBlocks vector
1387 DenseMap<const BlockT *, size_t> BlockIndex;
1388 // Extract initial frequencies for the reachable blocks
1389 auto Freq = std::vector<Scaled64>(ReachableBlocks.size());
1390 Scaled64 SumFreq;
1391 for (size_t I = 0; I < ReachableBlocks.size(); I++) {
1392 const BlockT *BB = ReachableBlocks[I];
1393 BlockIndex[BB] = I;
1394 Freq[I] = getFloatingBlockFreq(BB);
1395 SumFreq += Freq[I];
1396 }
1397 assert(!SumFreq.isZero() && "empty initial block frequencies");
1398
1399 LLVM_DEBUG(dbgs() << "Applying iterative inference for " << F->getName()
1400 << " with " << ReachableBlocks.size() << " blocks\n");
1401
1402 // Normalizing frequencies so they sum up to 1.0
1403 for (auto &Value : Freq) {
1404 Value /= SumFreq;
1405 }
1406
1407 // Setting up edge probabilities using sparse matrix representation:
1408 // ProbMatrix[I] holds a vector of pairs (J, P) where Pr[J -> I | J] = P
1409 ProbMatrixType ProbMatrix;
1410 initTransitionProbabilities(ReachableBlocks, BlockIndex, ProbMatrix);
1411
1412 // Run the propagation
1413 iterativeInference(ProbMatrix, Freq);
1414
1415 // Assign computed frequency values
1416 for (const BlockT &BB : *F) {
1417 auto Node = getNode(&BB);
1418 if (!Node.isValid())
1419 continue;
1420 if (BlockIndex.count(&BB)) {
1421 Freqs[Node.Index].Scaled = Freq[BlockIndex[&BB]];
1422 } else {
1423 Freqs[Node.Index].Scaled = Scaled64::getZero();
1424 }
1425 }
1426}
1427
1428template <class BT>
1429void BlockFrequencyInfoImpl<BT>::iterativeInference(
1430 const ProbMatrixType &ProbMatrix, std::vector<Scaled64> &Freq) const {
1432 "incorrectly specified precision");
1433 // Convert double precision to Scaled64
1434 const auto Precision =
1435 Scaled64::getInverse(static_cast<uint64_t>(1.0 / IterativeBFIPrecision));
1436 const size_t MaxIterations = IterativeBFIMaxIterationsPerBlock * Freq.size();
1437
1438#ifndef NDEBUG
1439 LLVM_DEBUG(dbgs() << " Initial discrepancy = "
1440 << discrepancy(ProbMatrix, Freq).toString() << "\n");
1441#endif
1442
1443 // Successors[I] holds unique sucessors of the I-th block
1444 auto Successors = std::vector<std::vector<size_t>>(Freq.size());
1445 for (size_t I = 0; I < Freq.size(); I++) {
1446 for (const auto &Jump : ProbMatrix[I]) {
1447 Successors[Jump.first].push_back(I);
1448 }
1449 }
1450
1451 // To speedup computation, we maintain a set of "active" blocks whose
1452 // frequencies need to be updated based on the incoming edges.
1453 // The set is dynamic and changes after every update. Initially all blocks
1454 // with a positive frequency are active
1455 auto IsActive = BitVector(Freq.size(), false);
1456 std::queue<size_t> ActiveSet;
1457 for (size_t I = 0; I < Freq.size(); I++) {
1458 if (Freq[I] > 0) {
1459 ActiveSet.push(I);
1460 IsActive[I] = true;
1461 }
1462 }
1463
1464 // Iterate over the blocks propagating frequencies
1465 size_t It = 0;
1466 while (It++ < MaxIterations && !ActiveSet.empty()) {
1467 size_t I = ActiveSet.front();
1468 ActiveSet.pop();
1469 IsActive[I] = false;
1470
1471 // Compute a new frequency for the block: NewFreq := Freq \times ProbMatrix.
1472 // A special care is taken for self-edges that needs to be scaled by
1473 // (1.0 - SelfProb), where SelfProb is the sum of probabilities on the edges
1474 Scaled64 NewFreq;
1475 Scaled64 OneMinusSelfProb = Scaled64::getOne();
1476 for (const auto &Jump : ProbMatrix[I]) {
1477 if (Jump.first == I) {
1478 OneMinusSelfProb -= Jump.second;
1479 } else {
1480 NewFreq += Freq[Jump.first] * Jump.second;
1481 }
1482 }
1483 if (OneMinusSelfProb != Scaled64::getOne())
1484 NewFreq /= OneMinusSelfProb;
1485
1486 // If the block's frequency has changed enough, then
1487 // make sure the block and its successors are in the active set
1488 auto Change = Freq[I] >= NewFreq ? Freq[I] - NewFreq : NewFreq - Freq[I];
1489 if (Change > Precision) {
1490 ActiveSet.push(I);
1491 IsActive[I] = true;
1492 for (size_t Succ : Successors[I]) {
1493 if (!IsActive[Succ]) {
1494 ActiveSet.push(Succ);
1495 IsActive[Succ] = true;
1496 }
1497 }
1498 }
1499
1500 // Update the frequency for the block
1501 Freq[I] = NewFreq;
1502 }
1503
1504 LLVM_DEBUG(dbgs() << " Completed " << It << " inference iterations"
1505 << format(" (%0.0f per block)", double(It) / Freq.size())
1506 << "\n");
1507#ifndef NDEBUG
1508 LLVM_DEBUG(dbgs() << " Final discrepancy = "
1509 << discrepancy(ProbMatrix, Freq).toString() << "\n");
1510#endif
1511}
1512
1513template <class BT>
1514void BlockFrequencyInfoImpl<BT>::findReachableBlocks(
1515 std::vector<const BlockT *> &Blocks) const {
1516 // Find all blocks to apply inference on, that is, reachable from the entry
1517 // along edges with non-zero probablities
1518 std::queue<const BlockT *> Queue;
1519 SmallPtrSet<const BlockT *, 8> Reachable;
1520 const BlockT *Entry = &F->front();
1521 Queue.push(Entry);
1522 Reachable.insert(Entry);
1523 while (!Queue.empty()) {
1524 const BlockT *SrcBB = Queue.front();
1525 Queue.pop();
1526 for (const BlockT *DstBB : children<const BlockT *>(SrcBB)) {
1527 auto EP = BPI->getEdgeProbability(SrcBB, DstBB);
1528 if (EP.isZero())
1529 continue;
1530 if (Reachable.insert(DstBB).second)
1531 Queue.push(DstBB);
1532 }
1533 }
1534
1535 // Find all blocks to apply inference on, that is, backward reachable from
1536 // the entry along (backward) edges with non-zero probablities
1537 SmallPtrSet<const BlockT *, 8> InverseReachable;
1538 for (const BlockT &BB : *F) {
1539 // An exit block is a block without any successors
1540 bool HasSucc = GraphTraits<const BlockT *>::child_begin(&BB) !=
1541 GraphTraits<const BlockT *>::child_end(&BB);
1542 if (!HasSucc && Reachable.count(&BB)) {
1543 Queue.push(&BB);
1544 InverseReachable.insert(&BB);
1545 }
1546 }
1547 while (!Queue.empty()) {
1548 const BlockT *SrcBB = Queue.front();
1549 Queue.pop();
1550 for (const BlockT *DstBB : children<Inverse<const BlockT *>>(SrcBB)) {
1551 auto EP = BPI->getEdgeProbability(DstBB, SrcBB);
1552 if (EP.isZero())
1553 continue;
1554 if (InverseReachable.insert(DstBB).second)
1555 Queue.push(DstBB);
1556 }
1557 }
1558
1559 // Collect the result
1560 Blocks.reserve(F->size());
1561 for (const BlockT &BB : *F) {
1562 if (Reachable.count(&BB) && InverseReachable.count(&BB)) {
1563 Blocks.push_back(&BB);
1564 }
1565 }
1566}
1567
1568template <class BT>
1569void BlockFrequencyInfoImpl<BT>::initTransitionProbabilities(
1570 const std::vector<const BlockT *> &Blocks,
1571 const DenseMap<const BlockT *, size_t> &BlockIndex,
1572 ProbMatrixType &ProbMatrix) const {
1573 const size_t NumBlocks = Blocks.size();
1574 auto Succs = std::vector<std::vector<std::pair<size_t, Scaled64>>>(NumBlocks);
1575 auto SumProb = std::vector<Scaled64>(NumBlocks);
1576
1577 // Find unique successors and corresponding probabilities for every block
1578 for (size_t Src = 0; Src < NumBlocks; Src++) {
1579 const BlockT *BB = Blocks[Src];
1580 SmallPtrSet<const BlockT *, 2> UniqueSuccs;
1581 for (const auto SI : children<const BlockT *>(BB)) {
1582 // Ignore cold blocks
1583 if (BlockIndex.find(SI) == BlockIndex.end())
1584 continue;
1585 // Ignore parallel edges between BB and SI blocks
1586 if (!UniqueSuccs.insert(SI).second)
1587 continue;
1588 // Ignore jumps with zero probability
1589 auto EP = BPI->getEdgeProbability(BB, SI);
1590 if (EP.isZero())
1591 continue;
1592
1593 auto EdgeProb =
1594 Scaled64::getFraction(EP.getNumerator(), EP.getDenominator());
1595 size_t Dst = BlockIndex.find(SI)->second;
1596 Succs[Src].push_back(std::make_pair(Dst, EdgeProb));
1597 SumProb[Src] += EdgeProb;
1598 }
1599 }
1600
1601 // Add transitions for every jump with positive branch probability
1602 ProbMatrix = ProbMatrixType(NumBlocks);
1603 for (size_t Src = 0; Src < NumBlocks; Src++) {
1604 // Ignore blocks w/o successors
1605 if (Succs[Src].empty())
1606 continue;
1607
1608 assert(!SumProb[Src].isZero() && "Zero sum probability of non-exit block");
1609 for (auto &Jump : Succs[Src]) {
1610 size_t Dst = Jump.first;
1611 Scaled64 Prob = Jump.second;
1612 ProbMatrix[Dst].push_back(std::make_pair(Src, Prob / SumProb[Src]));
1613 }
1614 }
1615
1616 // Add transitions from sinks to the source
1617 size_t EntryIdx = BlockIndex.find(&F->front())->second;
1618 for (size_t Src = 0; Src < NumBlocks; Src++) {
1619 if (Succs[Src].empty()) {
1620 ProbMatrix[EntryIdx].push_back(std::make_pair(Src, Scaled64::getOne()));
1621 }
1622 }
1623}
1624
1625#ifndef NDEBUG
1626template <class BT>
1627BlockFrequencyInfoImplBase::Scaled64 BlockFrequencyInfoImpl<BT>::discrepancy(
1628 const ProbMatrixType &ProbMatrix, const std::vector<Scaled64> &Freq) const {
1629 assert(Freq[0] > 0 && "Incorrectly computed frequency of the entry block");
1630 Scaled64 Discrepancy;
1631 for (size_t I = 0; I < ProbMatrix.size(); I++) {
1632 Scaled64 Sum;
1633 for (const auto &Jump : ProbMatrix[I]) {
1634 Sum += Freq[Jump.first] * Jump.second;
1635 }
1636 Discrepancy += Freq[I] >= Sum ? Freq[I] - Sum : Sum - Freq[I];
1637 }
1638 // Normalizing by the frequency of the entry block
1639 return Discrepancy / Freq[0];
1640}
1641#endif
1642
1643/// \note This should be a lambda, but that crashes GCC 4.7.
1644namespace bfi_detail {
1645
1646template <class BT> struct BlockEdgesAdder {
1647 using BlockT = BT;
1650
1652
1654 : BFI(BFI) {}
1655
1657 const LoopData *OuterLoop) {
1658 const BlockT *BB = BFI.RPOT[Irr.Node.Index];
1659 for (const auto *Succ : children<const BlockT *>(BB))
1660 G.addEdge(Irr, BFI.getNode(Succ), OuterLoop);
1661 }
1662};
1663
1664} // end namespace bfi_detail
1665
1666template <class BT>
1667void BlockFrequencyInfoImpl<BT>::computeIrreducibleMass(
1668 LoopData *OuterLoop, std::list<LoopData>::iterator Insert) {
1669 LLVM_DEBUG(dbgs() << "analyze-irreducible-in-";
1670 if (OuterLoop) dbgs()
1671 << "loop: " << getLoopName(*OuterLoop) << "\n";
1672 else dbgs() << "function\n");
1673
1674 using namespace bfi_detail;
1675
1676 // Ideally, addBlockEdges() would be declared here as a lambda, but that
1677 // crashes GCC 4.7.
1678 BlockEdgesAdder<BT> addBlockEdges(*this);
1679 IrreducibleGraph G(*this, OuterLoop, addBlockEdges);
1680
1681 for (auto &L : analyzeIrreducible(G, OuterLoop, Insert))
1682 computeMassInLoop(L);
1683
1684 if (!OuterLoop)
1685 return;
1686 updateLoopWithIrreducible(*OuterLoop);
1687}
1688
1689// A helper function that converts a branch probability into weight.
1691 return Prob.getNumerator();
1692}
1693
1694template <class BT>
1695bool
1696BlockFrequencyInfoImpl<BT>::propagateMassToSuccessors(LoopData *OuterLoop,
1697 const BlockNode &Node) {
1698 LLVM_DEBUG(dbgs() << " - node: " << getBlockName(Node) << "\n");
1699 // Calculate probability for successors.
1700 Distribution Dist;
1701 if (auto *Loop = Working[Node.Index].getPackagedLoop()) {
1702 assert(Loop != OuterLoop && "Cannot propagate mass in a packaged loop");
1703 if (!addLoopSuccessorsToDist(OuterLoop, *Loop, Dist))
1704 // Irreducible backedge.
1705 return false;
1706 } else {
1707 const BlockT *BB = getBlock(Node);
1708 for (auto SI = GraphTraits<const BlockT *>::child_begin(BB),
1709 SE = GraphTraits<const BlockT *>::child_end(BB);
1710 SI != SE; ++SI)
1711 if (!addToDist(
1712 Dist, OuterLoop, Node, getNode(*SI),
1713 getWeightFromBranchProb(BPI->getEdgeProbability(BB, SI))))
1714 // Irreducible backedge.
1715 return false;
1716 }
1717
1718 // Distribute mass to successors, saving exit and backedge data in the
1719 // loop header.
1720 distributeMass(Node, OuterLoop, Dist);
1721 return true;
1722}
1723
1724template <class BT>
1726 if (!F)
1727 return OS;
1728 OS << "block-frequency-info: " << F->getName() << "\n";
1729 for (const BlockT &BB : *F) {
1730 OS << " - " << bfi_detail::getBlockName(&BB) << ": float = ";
1731 getFloatingBlockFreq(&BB).print(OS, 5)
1732 << ", int = " << getBlockFreq(&BB).getFrequency();
1733 if (std::optional<uint64_t> ProfileCount =
1735 F->getFunction(), getNode(&BB)))
1736 OS << ", count = " << *ProfileCount;
1737 if (std::optional<uint64_t> IrrLoopHeaderWeight =
1738 BB.getIrrLoopHeaderWeight())
1739 OS << ", irr_loop_header_weight = " << *IrrLoopHeaderWeight;
1740 OS << "\n";
1741 }
1742
1743 // Add an extra newline for readability.
1744 OS << "\n";
1745 return OS;
1746}
1747
1748template <class BT>
1751 bool Match = true;
1754 for (auto &Entry : Nodes) {
1755 const BlockT *BB = Entry.first;
1756 if (BB) {
1757 ValidNodes[BB] = Entry.second.first;
1758 }
1759 }
1760 for (auto &Entry : Other.Nodes) {
1761 const BlockT *BB = Entry.first;
1762 if (BB) {
1763 OtherValidNodes[BB] = Entry.second.first;
1764 }
1765 }
1766 unsigned NumValidNodes = ValidNodes.size();
1767 unsigned NumOtherValidNodes = OtherValidNodes.size();
1768 if (NumValidNodes != NumOtherValidNodes) {
1769 Match = false;
1770 dbgs() << "Number of blocks mismatch: " << NumValidNodes << " vs "
1771 << NumOtherValidNodes << "\n";
1772 } else {
1773 for (auto &Entry : ValidNodes) {
1774 const BlockT *BB = Entry.first;
1775 BlockNode Node = Entry.second;
1776 if (OtherValidNodes.count(BB)) {
1777 BlockNode OtherNode = OtherValidNodes[BB];
1778 const auto &Freq = Freqs[Node.Index];
1779 const auto &OtherFreq = Other.Freqs[OtherNode.Index];
1780 if (Freq.Integer != OtherFreq.Integer) {
1781 Match = false;
1782 dbgs() << "Freq mismatch: " << bfi_detail::getBlockName(BB) << " "
1783 << Freq.Integer << " vs " << OtherFreq.Integer << "\n";
1784 }
1785 } else {
1786 Match = false;
1787 dbgs() << "Block " << bfi_detail::getBlockName(BB) << " index "
1788 << Node.Index << " does not exist in Other.\n";
1789 }
1790 }
1791 // If there's a valid node in OtherValidNodes that's not in ValidNodes,
1792 // either the above num check or the check on OtherValidNodes will fail.
1793 }
1794 if (!Match) {
1795 dbgs() << "This\n";
1796 print(dbgs());
1797 dbgs() << "Other\n";
1798 Other.print(dbgs());
1799 }
1800 assert(Match && "BFI mismatch");
1801}
1802
1803// Graph trait base class for block frequency information graph
1804// viewer.
1805
1807
1808template <class BlockFrequencyInfoT, class BranchProbabilityInfoT>
1811 using NodeRef = typename GTraits::NodeRef;
1812 using EdgeIter = typename GTraits::ChildIteratorType;
1813 using NodeIter = typename GTraits::nodes_iterator;
1814
1816
1817 explicit BFIDOTGraphTraitsBase(bool isSimple = false)
1819
1820 static StringRef getGraphName(const BlockFrequencyInfoT *G) {
1821 return G->getFunction()->getName();
1822 }
1823
1824 std::string getNodeAttributes(NodeRef Node, const BlockFrequencyInfoT *Graph,
1825 unsigned HotPercentThreshold = 0) {
1826 std::string Result;
1827 if (!HotPercentThreshold)
1828 return Result;
1829
1830 // Compute MaxFrequency on the fly:
1831 if (!MaxFrequency) {
1832 for (NodeIter I = GTraits::nodes_begin(Graph),
1833 E = GTraits::nodes_end(Graph);
1834 I != E; ++I) {
1835 NodeRef N = *I;
1836 MaxFrequency =
1837 std::max(MaxFrequency, Graph->getBlockFreq(N).getFrequency());
1838 }
1839 }
1840 BlockFrequency Freq = Graph->getBlockFreq(Node);
1841 BlockFrequency HotFreq =
1843 BranchProbability::getBranchProbability(HotPercentThreshold, 100));
1844
1845 if (Freq < HotFreq)
1846 return Result;
1847
1848 raw_string_ostream OS(Result);
1849 OS << "color=\"red\"";
1850 OS.flush();
1851 return Result;
1852 }
1853
1854 std::string getNodeLabel(NodeRef Node, const BlockFrequencyInfoT *Graph,
1855 GVDAGType GType, int layout_order = -1) {
1856 std::string Result;
1857 raw_string_ostream OS(Result);
1858
1859 if (layout_order != -1)
1860 OS << Node->getName() << "[" << layout_order << "] : ";
1861 else
1862 OS << Node->getName() << " : ";
1863 switch (GType) {
1864 case GVDT_Fraction:
1865 Graph->printBlockFreq(OS, Node);
1866 break;
1867 case GVDT_Integer:
1868 OS << Graph->getBlockFreq(Node).getFrequency();
1869 break;
1870 case GVDT_Count: {
1871 auto Count = Graph->getBlockProfileCount(Node);
1872 if (Count)
1873 OS << *Count;
1874 else
1875 OS << "Unknown";
1876 break;
1877 }
1878 case GVDT_None:
1879 llvm_unreachable("If we are not supposed to render a graph we should "
1880 "never reach this point.");
1881 }
1882 return Result;
1883 }
1884
1886 const BlockFrequencyInfoT *BFI,
1887 const BranchProbabilityInfoT *BPI,
1888 unsigned HotPercentThreshold = 0) {
1889 std::string Str;
1890 if (!BPI)
1891 return Str;
1892
1893 BranchProbability BP = BPI->getEdgeProbability(Node, EI);
1894 uint32_t N = BP.getNumerator();
1895 uint32_t D = BP.getDenominator();
1896 double Percent = 100.0 * N / D;
1898 OS << format("label=\"%.1f%%\"", Percent);
1899
1900 if (HotPercentThreshold) {
1901 BlockFrequency EFreq = BFI->getBlockFreq(Node) * BP;
1903 BranchProbability(HotPercentThreshold, 100);
1904
1905 if (EFreq >= HotFreq) {
1906 OS << ",color=\"red\"";
1907 }
1908 }
1909
1910 OS.flush();
1911 return Str;
1912 }
1913};
1914
1915} // end namespace llvm
1916
1917#undef DEBUG_TYPE
1918
1919#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.
DenseMap< Block *, BlockRelaxAux > Blocks
Definition: ELF_riscv.cpp:491
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())
raw_pwrite_stream & OS
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:202
bool erase(const KeyT &Val)
Definition: DenseMap.h:329
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:151
Class to represent profile counts.
Definition: Function.h:252
Represents a single loop in the control flow graph.
Definition: LoopInfo.h:47
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:309
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:334
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:858
#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)