98dd2f9ed3
This is a continuation of D109860.
Traditional flow-based algorithms cannot guarantee that the resulting edge
frequencies correspond to a *connected* flow in the control-flow graph. For
example, for an instance in the attached figure, a flow-based (or any other)
inference algorithm may produce an output in which the hot loop is disconnected
from the entry block (refer to the rightmost graph in the figure). Furthermore,
creating a connected minimum-cost maximum flow is a computationally NP-hard
problem. Hence, we apply a post-processing adjustments to the computed flow
by connecting all isolated flow components ("islands").
This feature helps to keep all blocks with sample counts connected and results
in significant performance wins for some binaries.
{F19077343}
Reviewed By: hoy
Differential Revision: https://reviews.llvm.org/D109903
671 lines
23 KiB
C++
671 lines
23 KiB
C++
//===- SampleProfileInference.cpp - Adjust sample profiles in the IR ------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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//
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// This file implements a profile inference algorithm. Given an incomplete and
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// possibly imprecise block counts, the algorithm reconstructs realistic block
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// and edge counts that satisfy flow conservation rules, while minimally modify
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// input block counts.
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//
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//===----------------------------------------------------------------------===//
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#include "llvm/Transforms/Utils/SampleProfileInference.h"
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#include "llvm/Support/Debug.h"
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#include <queue>
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#include <set>
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using namespace llvm;
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#define DEBUG_TYPE "sample-profile-inference"
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namespace {
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/// A value indicating an infinite flow/capacity/weight of a block/edge.
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/// Not using numeric_limits<int64_t>::max(), as the values can be summed up
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/// during the execution.
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static constexpr int64_t INF = ((int64_t)1) << 50;
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/// The minimum-cost maximum flow algorithm.
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///
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/// The algorithm finds the maximum flow of minimum cost on a given (directed)
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/// network using a modified version of the classical Moore-Bellman-Ford
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/// approach. The algorithm applies a number of augmentation iterations in which
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/// flow is sent along paths of positive capacity from the source to the sink.
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/// The worst-case time complexity of the implementation is O(v(f)*m*n), where
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/// where m is the number of edges, n is the number of vertices, and v(f) is the
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/// value of the maximum flow. However, the observed running time on typical
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/// instances is sub-quadratic, that is, o(n^2).
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///
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/// The input is a set of edges with specified costs and capacities, and a pair
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/// of nodes (source and sink). The output is the flow along each edge of the
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/// minimum total cost respecting the given edge capacities.
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class MinCostMaxFlow {
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public:
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// Initialize algorithm's data structures for a network of a given size.
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void initialize(uint64_t NodeCount, uint64_t SourceNode, uint64_t SinkNode) {
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Source = SourceNode;
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Target = SinkNode;
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Nodes = std::vector<Node>(NodeCount);
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Edges = std::vector<std::vector<Edge>>(NodeCount, std::vector<Edge>());
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}
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// Run the algorithm.
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int64_t run() {
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// Find an augmenting path and update the flow along the path
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size_t AugmentationIters = 0;
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while (findAugmentingPath()) {
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augmentFlowAlongPath();
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AugmentationIters++;
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}
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// Compute the total flow and its cost
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int64_t TotalCost = 0;
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int64_t TotalFlow = 0;
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for (uint64_t Src = 0; Src < Nodes.size(); Src++) {
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for (auto &Edge : Edges[Src]) {
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if (Edge.Flow > 0) {
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TotalCost += Edge.Cost * Edge.Flow;
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if (Src == Source)
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TotalFlow += Edge.Flow;
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}
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}
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}
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LLVM_DEBUG(dbgs() << "Completed profi after " << AugmentationIters
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<< " iterations with " << TotalFlow << " total flow"
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<< " of " << TotalCost << " cost\n");
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(void)TotalFlow;
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return TotalCost;
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}
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/// Adding an edge to the network with a specified capacity and a cost.
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/// Multiple edges between a pair of nodes are allowed but self-edges
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/// are not supported.
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void addEdge(uint64_t Src, uint64_t Dst, int64_t Capacity, int64_t Cost) {
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assert(Capacity > 0 && "adding an edge of zero capacity");
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assert(Src != Dst && "loop edge are not supported");
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Edge SrcEdge;
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SrcEdge.Dst = Dst;
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SrcEdge.Cost = Cost;
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SrcEdge.Capacity = Capacity;
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SrcEdge.Flow = 0;
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SrcEdge.RevEdgeIndex = Edges[Dst].size();
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Edge DstEdge;
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DstEdge.Dst = Src;
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DstEdge.Cost = -Cost;
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DstEdge.Capacity = 0;
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DstEdge.Flow = 0;
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DstEdge.RevEdgeIndex = Edges[Src].size();
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Edges[Src].push_back(SrcEdge);
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Edges[Dst].push_back(DstEdge);
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}
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/// Adding an edge to the network of infinite capacity and a given cost.
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void addEdge(uint64_t Src, uint64_t Dst, int64_t Cost) {
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addEdge(Src, Dst, INF, Cost);
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}
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/// Get the total flow from a given source node.
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/// Returns a list of pairs (target node, amount of flow to the target).
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const std::vector<std::pair<uint64_t, int64_t>> getFlow(uint64_t Src) const {
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std::vector<std::pair<uint64_t, int64_t>> Flow;
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for (auto &Edge : Edges[Src]) {
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if (Edge.Flow > 0)
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Flow.push_back(std::make_pair(Edge.Dst, Edge.Flow));
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}
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return Flow;
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}
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/// Get the total flow between a pair of nodes.
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int64_t getFlow(uint64_t Src, uint64_t Dst) const {
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int64_t Flow = 0;
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for (auto &Edge : Edges[Src]) {
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if (Edge.Dst == Dst) {
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Flow += Edge.Flow;
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}
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}
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return Flow;
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}
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/// A cost of increasing a block's count by one.
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static constexpr int64_t AuxCostInc = 10;
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/// A cost of decreasing a block's count by one.
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static constexpr int64_t AuxCostDec = 20;
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/// A cost of increasing a count of zero-weight block by one.
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static constexpr int64_t AuxCostIncZero = 11;
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/// A cost of increasing the entry block's count by one.
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static constexpr int64_t AuxCostIncEntry = 40;
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/// A cost of decreasing the entry block's count by one.
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static constexpr int64_t AuxCostDecEntry = 10;
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/// A cost of taking an unlikely jump.
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static constexpr int64_t AuxCostUnlikely = ((int64_t)1) << 20;
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private:
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/// Check for existence of an augmenting path with a positive capacity.
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bool findAugmentingPath() {
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// Initialize data structures
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for (auto &Node : Nodes) {
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Node.Distance = INF;
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Node.ParentNode = uint64_t(-1);
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Node.ParentEdgeIndex = uint64_t(-1);
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Node.Taken = false;
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}
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std::queue<uint64_t> Queue;
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Queue.push(Source);
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Nodes[Source].Distance = 0;
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Nodes[Source].Taken = true;
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while (!Queue.empty()) {
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uint64_t Src = Queue.front();
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Queue.pop();
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Nodes[Src].Taken = false;
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// Although the residual network contains edges with negative costs
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// (in particular, backward edges), it can be shown that there are no
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// negative-weight cycles and the following two invariants are maintained:
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// (i) Dist[Source, V] >= 0 and (ii) Dist[V, Target] >= 0 for all nodes V,
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// where Dist is the length of the shortest path between two nodes. This
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// allows to prune the search-space of the path-finding algorithm using
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// the following early-stop criteria:
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// -- If we find a path with zero-distance from Source to Target, stop the
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// search, as the path is the shortest since Dist[Source, Target] >= 0;
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// -- If we have Dist[Source, V] > Dist[Source, Target], then do not
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// process node V, as it is guaranteed _not_ to be on a shortest path
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// from Source to Target; it follows from inequalities
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// Dist[Source, Target] >= Dist[Source, V] + Dist[V, Target]
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// >= Dist[Source, V]
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if (Nodes[Target].Distance == 0)
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break;
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if (Nodes[Src].Distance > Nodes[Target].Distance)
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continue;
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// Process adjacent edges
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for (uint64_t EdgeIdx = 0; EdgeIdx < Edges[Src].size(); EdgeIdx++) {
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auto &Edge = Edges[Src][EdgeIdx];
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if (Edge.Flow < Edge.Capacity) {
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uint64_t Dst = Edge.Dst;
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int64_t NewDistance = Nodes[Src].Distance + Edge.Cost;
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if (Nodes[Dst].Distance > NewDistance) {
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// Update the distance and the parent node/edge
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Nodes[Dst].Distance = NewDistance;
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Nodes[Dst].ParentNode = Src;
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Nodes[Dst].ParentEdgeIndex = EdgeIdx;
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// Add the node to the queue, if it is not there yet
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if (!Nodes[Dst].Taken) {
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Queue.push(Dst);
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Nodes[Dst].Taken = true;
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}
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}
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}
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}
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}
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return Nodes[Target].Distance != INF;
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}
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/// Update the current flow along the augmenting path.
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void augmentFlowAlongPath() {
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// Find path capacity
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int64_t PathCapacity = INF;
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uint64_t Now = Target;
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while (Now != Source) {
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uint64_t Pred = Nodes[Now].ParentNode;
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auto &Edge = Edges[Pred][Nodes[Now].ParentEdgeIndex];
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PathCapacity = std::min(PathCapacity, Edge.Capacity - Edge.Flow);
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Now = Pred;
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}
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assert(PathCapacity > 0 && "found incorrect augmenting path");
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// Update the flow along the path
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Now = Target;
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while (Now != Source) {
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uint64_t Pred = Nodes[Now].ParentNode;
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auto &Edge = Edges[Pred][Nodes[Now].ParentEdgeIndex];
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auto &RevEdge = Edges[Now][Edge.RevEdgeIndex];
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Edge.Flow += PathCapacity;
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RevEdge.Flow -= PathCapacity;
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Now = Pred;
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}
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}
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/// An node in a flow network.
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struct Node {
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/// The cost of the cheapest path from the source to the current node.
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int64_t Distance;
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/// The node preceding the current one in the path.
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uint64_t ParentNode;
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/// The index of the edge between ParentNode and the current node.
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uint64_t ParentEdgeIndex;
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/// An indicator of whether the current node is in a queue.
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bool Taken;
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};
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/// An edge in a flow network.
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struct Edge {
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/// The cost of the edge.
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int64_t Cost;
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/// The capacity of the edge.
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int64_t Capacity;
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/// The current flow on the edge.
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int64_t Flow;
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/// The destination node of the edge.
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uint64_t Dst;
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/// The index of the reverse edge between Dst and the current node.
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uint64_t RevEdgeIndex;
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};
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/// The set of network nodes.
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std::vector<Node> Nodes;
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/// The set of network edges.
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std::vector<std::vector<Edge>> Edges;
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/// Source node of the flow.
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uint64_t Source;
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/// Target (sink) node of the flow.
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uint64_t Target;
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};
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/// Post-processing adjustment of the control flow.
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class FlowAdjuster {
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public:
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FlowAdjuster(FlowFunction &Func) : Func(Func) {
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assert(Func.Blocks[Func.Entry].isEntry() &&
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"incorrect index of the entry block");
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}
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// Run the post-processing
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void run() {
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/// We adjust the control flow in a function so as to remove all
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/// "isolated" components with positive flow that are unreachable
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/// from the entry block. For every such component, we find the shortest
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/// path from the entry to an exit passing through the component, and
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/// increase the flow by one unit along the path.
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joinIsolatedComponents();
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}
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private:
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void joinIsolatedComponents() {
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// Find blocks that are reachable from the source
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auto Visited = std::vector<bool>(NumBlocks(), false);
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findReachable(Func.Entry, Visited);
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// Iterate over all non-reachable blocks and adjust their weights
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for (uint64_t I = 0; I < NumBlocks(); I++) {
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auto &Block = Func.Blocks[I];
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if (Block.Flow > 0 && !Visited[I]) {
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// Find a path from the entry to an exit passing through the block I
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auto Path = findShortestPath(I);
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// Increase the flow along the path
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assert(Path.size() > 0 && Path[0]->Source == Func.Entry &&
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"incorrectly computed path adjusting control flow");
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Func.Blocks[Func.Entry].Flow += 1;
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for (auto &Jump : Path) {
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Jump->Flow += 1;
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Func.Blocks[Jump->Target].Flow += 1;
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// Update reachability
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findReachable(Jump->Target, Visited);
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}
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}
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}
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}
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/// Run bfs from a given block along the jumps with a positive flow and mark
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/// all reachable blocks.
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void findReachable(uint64_t Src, std::vector<bool> &Visited) {
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if (Visited[Src])
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return;
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std::queue<uint64_t> Queue;
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Queue.push(Src);
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Visited[Src] = true;
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while (!Queue.empty()) {
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Src = Queue.front();
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Queue.pop();
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for (auto Jump : Func.Blocks[Src].SuccJumps) {
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uint64_t Dst = Jump->Target;
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if (Jump->Flow > 0 && !Visited[Dst]) {
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Queue.push(Dst);
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Visited[Dst] = true;
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}
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}
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}
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}
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/// Find the shortest path from the entry block to an exit block passing
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/// through a given block.
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std::vector<FlowJump *> findShortestPath(uint64_t BlockIdx) {
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// A path from the entry block to BlockIdx
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auto ForwardPath = findShortestPath(Func.Entry, BlockIdx);
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// A path from BlockIdx to an exit block
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auto BackwardPath = findShortestPath(BlockIdx, AnyExitBlock);
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// Concatenate the two paths
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std::vector<FlowJump *> Result;
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Result.insert(Result.end(), ForwardPath.begin(), ForwardPath.end());
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Result.insert(Result.end(), BackwardPath.begin(), BackwardPath.end());
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return Result;
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}
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/// Apply the Dijkstra algorithm to find the shortest path from a given
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/// Source to a given Target block.
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/// If Target == -1, then the path ends at an exit block.
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std::vector<FlowJump *> findShortestPath(uint64_t Source, uint64_t Target) {
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// Quit early, if possible
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if (Source == Target)
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return std::vector<FlowJump *>();
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if (Func.Blocks[Source].isExit() && Target == AnyExitBlock)
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return std::vector<FlowJump *>();
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// Initialize data structures
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auto Distance = std::vector<int64_t>(NumBlocks(), INF);
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auto Parent = std::vector<FlowJump *>(NumBlocks(), nullptr);
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Distance[Source] = 0;
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std::set<std::pair<uint64_t, uint64_t>> Queue;
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Queue.insert(std::make_pair(Distance[Source], Source));
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// Run the Dijkstra algorithm
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while (!Queue.empty()) {
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uint64_t Src = Queue.begin()->second;
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Queue.erase(Queue.begin());
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// If we found a solution, quit early
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if (Src == Target ||
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(Func.Blocks[Src].isExit() && Target == AnyExitBlock))
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break;
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for (auto Jump : Func.Blocks[Src].SuccJumps) {
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uint64_t Dst = Jump->Target;
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int64_t JumpDist = jumpDistance(Jump);
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if (Distance[Dst] > Distance[Src] + JumpDist) {
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Queue.erase(std::make_pair(Distance[Dst], Dst));
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Distance[Dst] = Distance[Src] + JumpDist;
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Parent[Dst] = Jump;
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Queue.insert(std::make_pair(Distance[Dst], Dst));
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}
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}
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}
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// If Target is not provided, find the closest exit block
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if (Target == AnyExitBlock) {
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for (uint64_t I = 0; I < NumBlocks(); I++) {
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if (Func.Blocks[I].isExit() && Parent[I] != nullptr) {
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if (Target == AnyExitBlock || Distance[Target] > Distance[I]) {
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Target = I;
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}
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}
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}
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}
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assert(Parent[Target] != nullptr && "a path does not exist");
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// Extract the constructed path
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std::vector<FlowJump *> Result;
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uint64_t Now = Target;
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while (Now != Source) {
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assert(Now == Parent[Now]->Target && "incorrect parent jump");
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Result.push_back(Parent[Now]);
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Now = Parent[Now]->Source;
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}
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// Reverse the path, since it is extracted from Target to Source
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std::reverse(Result.begin(), Result.end());
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return Result;
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}
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/// A distance of a path for a given jump.
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/// In order to incite the path to use blocks/jumps with large positive flow,
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/// and avoid changing branch probability of outgoing edges drastically,
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/// set the distance as follows:
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/// if Jump.Flow > 0, then distance = max(100 - Jump->Flow, 0)
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/// if Block.Weight > 0, then distance = 1
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/// otherwise distance >> 1
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int64_t jumpDistance(FlowJump *Jump) const {
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int64_t BaseDistance = 100;
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if (Jump->IsUnlikely)
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return MinCostMaxFlow::AuxCostUnlikely;
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if (Jump->Flow > 0)
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return std::max(BaseDistance - (int64_t)Jump->Flow, (int64_t)0);
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if (Func.Blocks[Jump->Target].Weight > 0)
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return BaseDistance;
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return BaseDistance * (NumBlocks() + 1);
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};
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uint64_t NumBlocks() const { return Func.Blocks.size(); }
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/// A constant indicating an arbitrary exit block of a function.
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static constexpr uint64_t AnyExitBlock = uint64_t(-1);
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/// The function.
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FlowFunction &Func;
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};
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/// Initializing flow network for a given function.
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///
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/// Every block is split into three nodes that are responsible for (i) an
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/// incoming flow, (ii) an outgoing flow, and (iii) penalizing an increase or
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/// reduction of the block weight.
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void initializeNetwork(MinCostMaxFlow &Network, FlowFunction &Func) {
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uint64_t NumBlocks = Func.Blocks.size();
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assert(NumBlocks > 1 && "Too few blocks in a function");
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LLVM_DEBUG(dbgs() << "Initializing profi for " << NumBlocks << " blocks\n");
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// Pre-process data: make sure the entry weight is at least 1
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if (Func.Blocks[Func.Entry].Weight == 0) {
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Func.Blocks[Func.Entry].Weight = 1;
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}
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// Introducing dummy source/sink pairs to allow flow circulation.
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// The nodes corresponding to blocks of Func have indicies in the range
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// [0..3 * NumBlocks); the dummy nodes are indexed by the next four values.
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uint64_t S = 3 * NumBlocks;
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uint64_t T = S + 1;
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uint64_t S1 = S + 2;
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uint64_t T1 = S + 3;
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Network.initialize(3 * NumBlocks + 4, S1, T1);
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// Create three nodes for every block of the function
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for (uint64_t B = 0; B < NumBlocks; B++) {
|
|
auto &Block = Func.Blocks[B];
|
|
assert((!Block.UnknownWeight || Block.Weight == 0 || Block.isEntry()) &&
|
|
"non-zero weight of a block w/o weight except for an entry");
|
|
|
|
// Split every block into two nodes
|
|
uint64_t Bin = 3 * B;
|
|
uint64_t Bout = 3 * B + 1;
|
|
uint64_t Baux = 3 * B + 2;
|
|
if (Block.Weight > 0) {
|
|
Network.addEdge(S1, Bout, Block.Weight, 0);
|
|
Network.addEdge(Bin, T1, Block.Weight, 0);
|
|
}
|
|
|
|
// Edges from S and to T
|
|
assert((!Block.isEntry() || !Block.isExit()) &&
|
|
"a block cannot be an entry and an exit");
|
|
if (Block.isEntry()) {
|
|
Network.addEdge(S, Bin, 0);
|
|
} else if (Block.isExit()) {
|
|
Network.addEdge(Bout, T, 0);
|
|
}
|
|
|
|
// An auxiliary node to allow increase/reduction of block counts:
|
|
// We assume that decreasing block counts is more expensive than increasing,
|
|
// and thus, setting separate costs here. In the future we may want to tune
|
|
// the relative costs so as to maximize the quality of generated profiles.
|
|
int64_t AuxCostInc = MinCostMaxFlow::AuxCostInc;
|
|
int64_t AuxCostDec = MinCostMaxFlow::AuxCostDec;
|
|
if (Block.UnknownWeight) {
|
|
// Do not penalize changing weights of blocks w/o known profile count
|
|
AuxCostInc = 0;
|
|
AuxCostDec = 0;
|
|
} else {
|
|
// Increasing the count for "cold" blocks with zero initial count is more
|
|
// expensive than for "hot" ones
|
|
if (Block.Weight == 0) {
|
|
AuxCostInc = MinCostMaxFlow::AuxCostIncZero;
|
|
}
|
|
// Modifying the count of the entry block is expensive
|
|
if (Block.isEntry()) {
|
|
AuxCostInc = MinCostMaxFlow::AuxCostIncEntry;
|
|
AuxCostDec = MinCostMaxFlow::AuxCostDecEntry;
|
|
}
|
|
}
|
|
// For blocks with self-edges, do not penalize a reduction of the count,
|
|
// as all of the increase can be attributed to the self-edge
|
|
if (Block.HasSelfEdge) {
|
|
AuxCostDec = 0;
|
|
}
|
|
|
|
Network.addEdge(Bin, Baux, AuxCostInc);
|
|
Network.addEdge(Baux, Bout, AuxCostInc);
|
|
if (Block.Weight > 0) {
|
|
Network.addEdge(Bout, Baux, AuxCostDec);
|
|
Network.addEdge(Baux, Bin, AuxCostDec);
|
|
}
|
|
}
|
|
|
|
// Creating edges for every jump
|
|
for (auto &Jump : Func.Jumps) {
|
|
uint64_t Src = Jump.Source;
|
|
uint64_t Dst = Jump.Target;
|
|
if (Src != Dst) {
|
|
uint64_t SrcOut = 3 * Src + 1;
|
|
uint64_t DstIn = 3 * Dst;
|
|
uint64_t Cost = Jump.IsUnlikely ? MinCostMaxFlow::AuxCostUnlikely : 0;
|
|
Network.addEdge(SrcOut, DstIn, Cost);
|
|
}
|
|
}
|
|
|
|
// Make sure we have a valid flow circulation
|
|
Network.addEdge(T, S, 0);
|
|
}
|
|
|
|
/// Extract resulting block and edge counts from the flow network.
|
|
void extractWeights(MinCostMaxFlow &Network, FlowFunction &Func) {
|
|
uint64_t NumBlocks = Func.Blocks.size();
|
|
|
|
// Extract resulting block counts
|
|
for (uint64_t Src = 0; Src < NumBlocks; Src++) {
|
|
auto &Block = Func.Blocks[Src];
|
|
uint64_t SrcOut = 3 * Src + 1;
|
|
int64_t Flow = 0;
|
|
for (auto &Adj : Network.getFlow(SrcOut)) {
|
|
uint64_t DstIn = Adj.first;
|
|
int64_t DstFlow = Adj.second;
|
|
bool IsAuxNode = (DstIn < 3 * NumBlocks && DstIn % 3 == 2);
|
|
if (!IsAuxNode || Block.HasSelfEdge) {
|
|
Flow += DstFlow;
|
|
}
|
|
}
|
|
Block.Flow = Flow;
|
|
assert(Flow >= 0 && "negative block flow");
|
|
}
|
|
|
|
// Extract resulting jump counts
|
|
for (auto &Jump : Func.Jumps) {
|
|
uint64_t Src = Jump.Source;
|
|
uint64_t Dst = Jump.Target;
|
|
int64_t Flow = 0;
|
|
if (Src != Dst) {
|
|
uint64_t SrcOut = 3 * Src + 1;
|
|
uint64_t DstIn = 3 * Dst;
|
|
Flow = Network.getFlow(SrcOut, DstIn);
|
|
} else {
|
|
uint64_t SrcOut = 3 * Src + 1;
|
|
uint64_t SrcAux = 3 * Src + 2;
|
|
int64_t AuxFlow = Network.getFlow(SrcOut, SrcAux);
|
|
if (AuxFlow > 0)
|
|
Flow = AuxFlow;
|
|
}
|
|
Jump.Flow = Flow;
|
|
assert(Flow >= 0 && "negative jump flow");
|
|
}
|
|
}
|
|
|
|
#ifndef NDEBUG
|
|
/// Verify that the computed flow values satisfy flow conservation rules
|
|
void verifyWeights(const FlowFunction &Func) {
|
|
const uint64_t NumBlocks = Func.Blocks.size();
|
|
auto InFlow = std::vector<uint64_t>(NumBlocks, 0);
|
|
auto OutFlow = std::vector<uint64_t>(NumBlocks, 0);
|
|
for (auto &Jump : Func.Jumps) {
|
|
InFlow[Jump.Target] += Jump.Flow;
|
|
OutFlow[Jump.Source] += Jump.Flow;
|
|
}
|
|
|
|
uint64_t TotalInFlow = 0;
|
|
uint64_t TotalOutFlow = 0;
|
|
for (uint64_t I = 0; I < NumBlocks; I++) {
|
|
auto &Block = Func.Blocks[I];
|
|
if (Block.isEntry()) {
|
|
TotalInFlow += Block.Flow;
|
|
assert(Block.Flow == OutFlow[I] && "incorrectly computed control flow");
|
|
} else if (Block.isExit()) {
|
|
TotalOutFlow += Block.Flow;
|
|
assert(Block.Flow == InFlow[I] && "incorrectly computed control flow");
|
|
} else {
|
|
assert(Block.Flow == OutFlow[I] && "incorrectly computed control flow");
|
|
assert(Block.Flow == InFlow[I] && "incorrectly computed control flow");
|
|
}
|
|
}
|
|
assert(TotalInFlow == TotalOutFlow && "incorrectly computed control flow");
|
|
|
|
// Verify that there are no isolated flow components
|
|
// One could modify FlowFunction to hold edges indexed by the sources, which
|
|
// will avoid a creation of the object
|
|
auto PositiveFlowEdges = std::vector<std::vector<uint64_t>>(NumBlocks);
|
|
for (auto &Jump : Func.Jumps) {
|
|
if (Jump.Flow > 0) {
|
|
PositiveFlowEdges[Jump.Source].push_back(Jump.Target);
|
|
}
|
|
}
|
|
|
|
// Run bfs from the source along edges with positive flow
|
|
std::queue<uint64_t> Queue;
|
|
auto Visited = std::vector<bool>(NumBlocks, false);
|
|
Queue.push(Func.Entry);
|
|
Visited[Func.Entry] = true;
|
|
while (!Queue.empty()) {
|
|
uint64_t Src = Queue.front();
|
|
Queue.pop();
|
|
for (uint64_t Dst : PositiveFlowEdges[Src]) {
|
|
if (!Visited[Dst]) {
|
|
Queue.push(Dst);
|
|
Visited[Dst] = true;
|
|
}
|
|
}
|
|
}
|
|
|
|
// Verify that every block that has a positive flow is reached from the source
|
|
// along edges with a positive flow
|
|
for (uint64_t I = 0; I < NumBlocks; I++) {
|
|
auto &Block = Func.Blocks[I];
|
|
assert((Visited[I] || Block.Flow == 0) && "an isolated flow component");
|
|
}
|
|
}
|
|
#endif
|
|
|
|
} // end of anonymous namespace
|
|
|
|
/// Apply the profile inference algorithm for a given flow function
|
|
void llvm::applyFlowInference(FlowFunction &Func) {
|
|
// Create and apply an inference network model
|
|
auto InferenceNetwork = MinCostMaxFlow();
|
|
initializeNetwork(InferenceNetwork, Func);
|
|
InferenceNetwork.run();
|
|
|
|
// Extract flow values for every block and every edge
|
|
extractWeights(InferenceNetwork, Func);
|
|
|
|
// Post-processing adjustments to the flow
|
|
auto Adjuster = FlowAdjuster(Func);
|
|
Adjuster.run();
|
|
|
|
#ifndef NDEBUG
|
|
// Verify the result
|
|
verifyWeights(Func);
|
|
#endif
|
|
}
|