302 lines
10 KiB
C++
302 lines
10 KiB
C++
#pragma once
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#include "Dom.h" // 支配树分析依赖
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#include "Loop.h" // 循环分析依赖
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#include "Liveness.h" // 活跃性分析依赖
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#include "AliasAnalysis.h" // 别名分析依赖
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#include "IR.h" // IR定义
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#include "Pass.h" // Pass框架
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#include <algorithm>
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#include <map>
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#include <memory>
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#include <optional>
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#include <set>
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#include <vector>
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namespace sysy {
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// 前向声明
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class LoopCharacteristicsResult;
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/**
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* @brief 循环特征信息结构
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* 存储单个循环的各种特征信息
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*/
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struct LoopCharacteristics {
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Loop* loop; // 关联的循环对象
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// ========== 归纳变量分析 ==========
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std::vector<Value*> basicInductionVars; // 基本归纳变量 (i = phi(init, i+step))
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std::vector<Value*> derivedInductionVars; // 派生归纳变量 (j = i * scale + offset)
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std::map<Value*, int> inductionSteps; // 归纳变量的步长
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std::map<Value*, Value*> inductionInits; // 归纳变量的初始值
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// ========== 循环不变量分析 ==========
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std::set<Value*> loopInvariants; // 循环不变量 (循环内定义但值不变)
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std::set<Instruction*> invariantInsts; // 不变指令 (可以外提的指令)
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// ========== 循环边界分析 ==========
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std::optional<int> staticTripCount; // 静态可确定的循环次数
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Value* dynamicTripCountExpr; // 动态循环次数表达式
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bool hasKnownBounds; // 是否有已知边界
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Value* lowerBound; // 循环下界
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Value* upperBound; // 循环上界
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// ========== 循环形式分析 ==========
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bool isCountingLoop; // 是否为计数循环 (for i=0; i<n; i++)
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bool isSimpleForLoop; // 是否为简单for循环
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bool hasComplexControlFlow; // 是否有复杂控制流 (break, continue)
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bool isInnermost; // 是否为最内层循环
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bool isParallel; // 是否可并行化
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// ========== 内存访问模式 ==========
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struct MemoryAccessPattern {
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bool isSequential; // 是否顺序访问 (a[i], a[i+1], ...)
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bool isStrided; // 是否跨步访问 (a[2*i], a[3*i], ...)
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int stride; // 访问步长
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std::vector<Instruction*> loadInsts; // load指令列表
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std::vector<Instruction*> storeInsts; // store指令列表
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// 使用外部别名分析结果
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AliasType aliasType; // 别名类型（来自别名分析）
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bool isArrayParameter; // 是否为数组参数访问
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bool isGlobalArray; // 是否为全局数组访问
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bool hasConstantIndices; // 是否使用常量索引
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};
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std::map<Value*, MemoryAccessPattern> memoryPatterns; // 内存访问模式
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// ========== 循环优化提示 ==========
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bool benefitsFromUnrolling; // 是否适合循环展开
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bool benefitsFromVectorization; // 是否适合向量化
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bool benefitsFromTiling; // 是否适合分块
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int suggestedUnrollFactor; // 建议的展开因子
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// ========== 性能特征 ==========
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size_t instructionCount; // 循环体指令数
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size_t memoryOperationCount; // 内存操作数
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size_t arithmeticOperationCount; // 算术操作数
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double computeToMemoryRatio; // 计算与内存操作比率
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// 构造函数
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LoopCharacteristics(Loop* l) : loop(l), dynamicTripCountExpr(nullptr),
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hasKnownBounds(false), lowerBound(nullptr), upperBound(nullptr),
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isCountingLoop(false), isSimpleForLoop(false), hasComplexControlFlow(false),
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isInnermost(false), isParallel(false), benefitsFromUnrolling(false),
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benefitsFromVectorization(false), benefitsFromTiling(false),
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suggestedUnrollFactor(1), instructionCount(0), memoryOperationCount(0),
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arithmeticOperationCount(0), computeToMemoryRatio(0.0) {}
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};
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/**
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* @brief 循环特征分析结果类
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* 包含函数中所有循环的特征信息，并提供查询接口
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*/
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class LoopCharacteristicsResult : public AnalysisResultBase {
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public:
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LoopCharacteristicsResult(Function *F) : AssociatedFunction(F) {}
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~LoopCharacteristicsResult() override = default;
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// ========== 基础接口 ==========
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/**
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* 添加循环特征信息
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*/
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void addLoopCharacteristics(std::unique_ptr<LoopCharacteristics> characteristics) {
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auto* loop = characteristics->loop;
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CharacteristicsMap[loop] = std::move(characteristics);
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}
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/**
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* 获取指定循环的特征信息
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*/
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const LoopCharacteristics* getCharacteristics(Loop* loop) const {
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auto it = CharacteristicsMap.find(loop);
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return (it != CharacteristicsMap.end()) ? it->second.get() : nullptr;
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}
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/**
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* 获取所有循环特征信息
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*/
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const std::map<Loop*, std::unique_ptr<LoopCharacteristics>>& getAllCharacteristics() const {
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return CharacteristicsMap;
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}
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// ========== 查询接口 ==========
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/**
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* 获取所有计数循环
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*/
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std::vector<Loop*> getCountingLoops() const {
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std::vector<Loop*> result;
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for (const auto& [loop, chars] : CharacteristicsMap) {
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if (chars->isCountingLoop) {
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result.push_back(loop);
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}
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}
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return result;
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}
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/**
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* 获取所有可向量化循环
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*/
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std::vector<Loop*> getVectorizableLoops() const {
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std::vector<Loop*> result;
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for (const auto& [loop, chars] : CharacteristicsMap) {
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if (chars->benefitsFromVectorization) {
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result.push_back(loop);
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}
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}
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return result;
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}
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/**
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* 获取所有适合展开的循环
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*/
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std::vector<Loop*> getUnrollCandidateLoops() const {
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std::vector<Loop*> result;
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for (const auto& [loop, chars] : CharacteristicsMap) {
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if (chars->benefitsFromUnrolling) {
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result.push_back(loop);
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}
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}
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return result;
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}
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/**
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* 获取所有可并行化循环
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*/
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std::vector<Loop*> getParallelizableLoops() const {
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std::vector<Loop*> result;
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for (const auto& [loop, chars] : CharacteristicsMap) {
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if (chars->isParallel) {
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result.push_back(loop);
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}
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}
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return result;
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}
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/**
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* 获取所有有静态已知循环次数的循环
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*/
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std::vector<Loop*> getStaticBoundLoops() const {
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std::vector<Loop*> result;
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for (const auto& [loop, chars] : CharacteristicsMap) {
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if (chars->staticTripCount.has_value()) {
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result.push_back(loop);
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}
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}
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return result;
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}
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/**
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* 根据热度排序循环 (用于优化优先级)
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*/
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std::vector<Loop*> getLoopsByHotness() const {
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std::vector<Loop*> result;
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for (const auto& [loop, chars] : CharacteristicsMap) {
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result.push_back(loop);
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}
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// 按循环热度排序 (嵌套深度 + 循环次数 + 指令数)
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std::sort(result.begin(), result.end(), [](Loop* a, Loop* b) {
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double hotnessA = a->getLoopHotness();
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double hotnessB = b->getLoopHotness();
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return hotnessA > hotnessB; // 降序排列
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});
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return result;
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}
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// ========== 统计接口 ==========
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/**
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* 获取优化候选统计
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*/
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struct OptimizationStats {
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size_t totalLoops;
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size_t countingLoops;
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size_t vectorizableLoops;
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size_t unrollCandidates;
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size_t parallelizableLoops;
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size_t staticBoundLoops;
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double avgInstructionCount;
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double avgComputeMemoryRatio;
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};
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OptimizationStats getOptimizationStats() const {
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OptimizationStats stats = {};
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stats.totalLoops = CharacteristicsMap.size();
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size_t totalInstructions = 0;
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double totalComputeMemoryRatio = 0.0;
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for (const auto& [loop, chars] : CharacteristicsMap) {
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if (chars->isCountingLoop) stats.countingLoops++;
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if (chars->benefitsFromVectorization) stats.vectorizableLoops++;
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if (chars->benefitsFromUnrolling) stats.unrollCandidates++;
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if (chars->isParallel) stats.parallelizableLoops++;
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if (chars->staticTripCount.has_value()) stats.staticBoundLoops++;
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totalInstructions += chars->instructionCount;
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totalComputeMemoryRatio += chars->computeToMemoryRatio;
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}
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if (stats.totalLoops > 0) {
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stats.avgInstructionCount = static_cast<double>(totalInstructions) / stats.totalLoops;
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stats.avgComputeMemoryRatio = totalComputeMemoryRatio / stats.totalLoops;
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}
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return stats;
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}
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// 打印分析结果
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void print() const;
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private:
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Function *AssociatedFunction; // 关联的函数
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std::map<Loop*, std::unique_ptr<LoopCharacteristics>> CharacteristicsMap; // 循环特征映射
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};
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/**
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* @brief 循环特征分析遍
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* 基于循环分析结果，分析每个循环的特征信息，为优化决策提供依据
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*/
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class LoopCharacteristicsPass : public AnalysisPass {
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public:
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// 唯一的 Pass ID
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static void *ID;
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LoopCharacteristicsPass() : AnalysisPass("LoopCharacteristics", Pass::Granularity::Function) {}
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// 实现 getPassID
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void *getPassID() const override { return &ID; }
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// 核心运行方法
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bool runOnFunction(Function *F, AnalysisManager &AM) override;
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// 获取分析结果
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std::unique_ptr<AnalysisResultBase> getResult() override { return std::move(CurrentResult); }
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private:
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std::unique_ptr<LoopCharacteristicsResult> CurrentResult; // 当前函数的分析结果
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// 内部分析方法
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void analyzeLoop(Loop* loop, LoopCharacteristics* characteristics, AnalysisManager &AM);
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void identifyInductionVariables(Loop* loop, LoopCharacteristics* characteristics);
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void identifyLoopInvariants(Loop* loop, LoopCharacteristics* characteristics);
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void analyzeLoopBounds(Loop* loop, LoopCharacteristics* characteristics);
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void analyzeLoopForm(Loop* loop, LoopCharacteristics* characteristics);
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void analyzeMemoryAccessPatterns(Loop* loop, LoopCharacteristics* characteristics);
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void evaluateOptimizationOpportunities(Loop* loop, LoopCharacteristics* characteristics);
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void computePerformanceMetrics(Loop* loop, LoopCharacteristics* characteristics);
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// 辅助方法
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bool isInductionVariable(Value* val, Loop* loop);
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bool isLoopInvariant(Value* val, Loop* loop);
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bool hasLoopCarriedDependence(Loop* loop);
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int estimateUnrollFactor(Loop* loop);
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bool benefitsFromVectorization(Loop* loop);
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};
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} // namespace sysy
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