blob: 08646aac52ef2e869265022bd4b0a27e36f4bdae [file] [log] [blame]
//===-- Clustering.cpp ------------------------------------------*- C++ -*-===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
#include "Clustering.h"
#include "Error.h"
#include "SchedClassResolution.h"
#include "llvm/ADT/MapVector.h"
#include "llvm/ADT/SetVector.h"
#include "llvm/ADT/SmallSet.h"
#include "llvm/ADT/SmallVector.h"
#include <algorithm>
#include <deque>
#include <string>
#include <vector>
namespace llvm {
namespace exegesis {
// The clustering problem has the following characteristics:
// (A) - Low dimension (dimensions are typically proc resource units,
// typically < 10).
// (B) - Number of points : ~thousands (points are measurements of an MCInst)
// (C) - Number of clusters: ~tens.
// (D) - The number of clusters is not known /a priory/.
// (E) - The amount of noise is relatively small.
// The problem is rather small. In terms of algorithms, (D) disqualifies
// k-means and makes algorithms such as DBSCAN[1] or OPTICS[2] more applicable.
//
// We've used DBSCAN here because it's simple to implement. This is a pretty
// straightforward and inefficient implementation of the pseudocode in [2].
//
// [1] https://en.wikipedia.org/wiki/DBSCAN
// [2] https://en.wikipedia.org/wiki/OPTICS_algorithm
// Finds the points at distance less than sqrt(EpsilonSquared) of Q (not
// including Q).
void InstructionBenchmarkClustering::rangeQuery(
const size_t Q, std::vector<size_t> &Neighbors) const {
Neighbors.clear();
Neighbors.reserve(Points_.size() - 1); // The Q itself isn't a neighbor.
const auto &QMeasurements = Points_[Q].Measurements;
for (size_t P = 0, NumPoints = Points_.size(); P < NumPoints; ++P) {
if (P == Q)
continue;
const auto &PMeasurements = Points_[P].Measurements;
if (PMeasurements.empty()) // Error point.
continue;
if (isNeighbour(PMeasurements, QMeasurements,
AnalysisClusteringEpsilonSquared_)) {
Neighbors.push_back(P);
}
}
}
// Given a set of points, checks that all the points are neighbours
// up to AnalysisClusteringEpsilon. This is O(2*N).
bool InstructionBenchmarkClustering::areAllNeighbours(
ArrayRef<size_t> Pts) const {
// First, get the centroid of this group of points. This is O(N).
SchedClassClusterCentroid G;
for_each(Pts, [this, &G](size_t P) {
assert(P < Points_.size());
ArrayRef<BenchmarkMeasure> Measurements = Points_[P].Measurements;
if (Measurements.empty()) // Error point.
return;
G.addPoint(Measurements);
});
const std::vector<BenchmarkMeasure> Centroid = G.getAsPoint();
// Since we will be comparing with the centroid, we need to halve the epsilon.
double AnalysisClusteringEpsilonHalvedSquared =
AnalysisClusteringEpsilonSquared_ / 4.0;
// And now check that every point is a neighbour of the centroid. Also O(N).
return all_of(
Pts, [this, &Centroid, AnalysisClusteringEpsilonHalvedSquared](size_t P) {
assert(P < Points_.size());
const auto &PMeasurements = Points_[P].Measurements;
if (PMeasurements.empty()) // Error point.
return true; // Pretend that error point is a neighbour.
return isNeighbour(PMeasurements, Centroid,
AnalysisClusteringEpsilonHalvedSquared);
});
}
InstructionBenchmarkClustering::InstructionBenchmarkClustering(
const std::vector<InstructionBenchmark> &Points,
const double AnalysisClusteringEpsilonSquared)
: Points_(Points),
AnalysisClusteringEpsilonSquared_(AnalysisClusteringEpsilonSquared),
NoiseCluster_(ClusterId::noise()), ErrorCluster_(ClusterId::error()) {}
Error InstructionBenchmarkClustering::validateAndSetup() {
ClusterIdForPoint_.resize(Points_.size());
// Mark erroneous measurements out.
// All points must have the same number of dimensions, in the same order.
const std::vector<BenchmarkMeasure> *LastMeasurement = nullptr;
for (size_t P = 0, NumPoints = Points_.size(); P < NumPoints; ++P) {
const auto &Point = Points_[P];
if (!Point.Error.empty()) {
ClusterIdForPoint_[P] = ClusterId::error();
ErrorCluster_.PointIndices.push_back(P);
continue;
}
const auto *CurMeasurement = &Point.Measurements;
if (LastMeasurement) {
if (LastMeasurement->size() != CurMeasurement->size()) {
return make_error<ClusteringError>(
"inconsistent measurement dimensions");
}
for (size_t I = 0, E = LastMeasurement->size(); I < E; ++I) {
if (LastMeasurement->at(I).Key != CurMeasurement->at(I).Key) {
return make_error<ClusteringError>(
"inconsistent measurement dimensions keys");
}
}
}
LastMeasurement = CurMeasurement;
}
if (LastMeasurement) {
NumDimensions_ = LastMeasurement->size();
}
return Error::success();
}
void InstructionBenchmarkClustering::clusterizeDbScan(const size_t MinPts) {
std::vector<size_t> Neighbors; // Persistent buffer to avoid allocs.
for (size_t P = 0, NumPoints = Points_.size(); P < NumPoints; ++P) {
if (!ClusterIdForPoint_[P].isUndef())
continue; // Previously processed in inner loop.
rangeQuery(P, Neighbors);
if (Neighbors.size() + 1 < MinPts) { // Density check.
// The region around P is not dense enough to create a new cluster, mark
// as noise for now.
ClusterIdForPoint_[P] = ClusterId::noise();
continue;
}
// Create a new cluster, add P.
Clusters_.emplace_back(ClusterId::makeValid(Clusters_.size()));
Cluster &CurrentCluster = Clusters_.back();
ClusterIdForPoint_[P] = CurrentCluster.Id; /* Label initial point */
CurrentCluster.PointIndices.push_back(P);
// Process P's neighbors.
SetVector<size_t, std::deque<size_t>> ToProcess;
ToProcess.insert(Neighbors.begin(), Neighbors.end());
while (!ToProcess.empty()) {
// Retrieve a point from the set.
const size_t Q = *ToProcess.begin();
ToProcess.erase(ToProcess.begin());
if (ClusterIdForPoint_[Q].isNoise()) {
// Change noise point to border point.
ClusterIdForPoint_[Q] = CurrentCluster.Id;
CurrentCluster.PointIndices.push_back(Q);
continue;
}
if (!ClusterIdForPoint_[Q].isUndef()) {
continue; // Previously processed.
}
// Add Q to the current custer.
ClusterIdForPoint_[Q] = CurrentCluster.Id;
CurrentCluster.PointIndices.push_back(Q);
// And extend to the neighbors of Q if the region is dense enough.
rangeQuery(Q, Neighbors);
if (Neighbors.size() + 1 >= MinPts) {
ToProcess.insert(Neighbors.begin(), Neighbors.end());
}
}
}
// assert(Neighbors.capacity() == (Points_.size() - 1));
// ^ True, but it is not quaranteed to be true in all the cases.
// Add noisy points to noise cluster.
for (size_t P = 0, NumPoints = Points_.size(); P < NumPoints; ++P) {
if (ClusterIdForPoint_[P].isNoise()) {
NoiseCluster_.PointIndices.push_back(P);
}
}
}
void InstructionBenchmarkClustering::clusterizeNaive(
const MCSubtargetInfo &SubtargetInfo, const MCInstrInfo &InstrInfo) {
// Given an instruction Opcode, which sched class id's are represented,
// and which are the benchmarks for each sched class?
std::vector<SmallMapVector<unsigned, SmallVector<size_t, 1>, 1>>
OpcodeToSchedClassesToPoints;
const unsigned NumOpcodes = InstrInfo.getNumOpcodes();
OpcodeToSchedClassesToPoints.resize(NumOpcodes);
size_t NumClusters = 0;
for (size_t P = 0, NumPoints = Points_.size(); P < NumPoints; ++P) {
const InstructionBenchmark &Point = Points_[P];
const MCInst &MCI = Point.keyInstruction();
unsigned SchedClassId;
std::tie(SchedClassId, std::ignore) =
ResolvedSchedClass::resolveSchedClassId(SubtargetInfo, InstrInfo, MCI);
const unsigned Opcode = MCI.getOpcode();
assert(Opcode < NumOpcodes && "NumOpcodes is incorrect (too small)");
auto &Points = OpcodeToSchedClassesToPoints[Opcode][SchedClassId];
if (Points.empty()) // If we previously have not seen any points of
++NumClusters; // this opcode's sched class, then new cluster begins.
Points.emplace_back(P);
}
assert(NumClusters <= NumOpcodes &&
"can't see more opcodes than there are total opcodes");
assert(NumClusters <= Points_.size() &&
"can't see more opcodes than there are total points");
Clusters_.reserve(NumClusters); // We already know how many clusters there is.
for (const auto &SchedClassesOfOpcode : OpcodeToSchedClassesToPoints) {
if (SchedClassesOfOpcode.empty())
continue;
for (ArrayRef<size_t> PointsOfSchedClass :
make_second_range(SchedClassesOfOpcode)) {
if (PointsOfSchedClass.empty())
continue;
// Create a new cluster.
Clusters_.emplace_back(ClusterId::makeValid(
Clusters_.size(),
/*IsUnstable=*/!areAllNeighbours(PointsOfSchedClass)));
Cluster &CurrentCluster = Clusters_.back();
// Mark points as belonging to the new cluster.
for_each(PointsOfSchedClass, [this, &CurrentCluster](size_t P) {
ClusterIdForPoint_[P] = CurrentCluster.Id;
});
// And add all the points of this opcode's sched class to the new cluster.
CurrentCluster.PointIndices.reserve(PointsOfSchedClass.size());
CurrentCluster.PointIndices.assign(PointsOfSchedClass.begin(),
PointsOfSchedClass.end());
assert(CurrentCluster.PointIndices.size() == PointsOfSchedClass.size());
}
}
assert(Clusters_.size() == NumClusters);
}
// Given an instruction Opcode, we can make benchmarks (measurements) of the
// instruction characteristics/performance. Then, to facilitate further analysis
// we group the benchmarks with *similar* characteristics into clusters.
// Now, this is all not entirely deterministic. Some instructions have variable
// characteristics, depending on their arguments. And thus, if we do several
// benchmarks of the same instruction Opcode, we may end up with *different*
// performance characteristics measurements. And when we then do clustering,
// these several benchmarks of the same instruction Opcode may end up being
// clustered into *different* clusters. This is not great for further analysis.
// We shall find every opcode with benchmarks not in just one cluster, and move
// *all* the benchmarks of said Opcode into one new unstable cluster per Opcode.
void InstructionBenchmarkClustering::stabilize(unsigned NumOpcodes) {
// Given an instruction Opcode and Config, in which clusters do benchmarks of
// this instruction lie? Normally, they all should be in the same cluster.
struct OpcodeAndConfig {
explicit OpcodeAndConfig(const InstructionBenchmark &IB)
: Opcode(IB.keyInstruction().getOpcode()), Config(&IB.Key.Config) {}
unsigned Opcode;
const std::string *Config;
auto Tie() const -> auto { return std::tie(Opcode, *Config); }
bool operator<(const OpcodeAndConfig &O) const { return Tie() < O.Tie(); }
bool operator!=(const OpcodeAndConfig &O) const { return Tie() != O.Tie(); }
};
std::map<OpcodeAndConfig, SmallSet<ClusterId, 1>> OpcodeConfigToClusterIDs;
// Populate OpcodeConfigToClusterIDs and UnstableOpcodes data structures.
assert(ClusterIdForPoint_.size() == Points_.size() && "size mismatch");
for (auto Point : zip(Points_, ClusterIdForPoint_)) {
const ClusterId &ClusterIdOfPoint = std::get<1>(Point);
if (!ClusterIdOfPoint.isValid())
continue; // Only process fully valid clusters.
const OpcodeAndConfig Key(std::get<0>(Point));
SmallSet<ClusterId, 1> &ClusterIDsOfOpcode = OpcodeConfigToClusterIDs[Key];
ClusterIDsOfOpcode.insert(ClusterIdOfPoint);
}
for (const auto &OpcodeConfigToClusterID : OpcodeConfigToClusterIDs) {
const SmallSet<ClusterId, 1> &ClusterIDs = OpcodeConfigToClusterID.second;
const OpcodeAndConfig &Key = OpcodeConfigToClusterID.first;
// We only care about unstable instructions.
if (ClusterIDs.size() < 2)
continue;
// Create a new unstable cluster, one per Opcode.
Clusters_.emplace_back(ClusterId::makeValidUnstable(Clusters_.size()));
Cluster &UnstableCluster = Clusters_.back();
// We will find *at least* one point in each of these clusters.
UnstableCluster.PointIndices.reserve(ClusterIDs.size());
// Go through every cluster which we recorded as containing benchmarks
// of this UnstableOpcode. NOTE: we only recorded valid clusters.
for (const ClusterId &CID : ClusterIDs) {
assert(CID.isValid() &&
"We only recorded valid clusters, not noise/error clusters.");
Cluster &OldCluster = Clusters_[CID.getId()]; // Valid clusters storage.
// Within each cluster, go through each point, and either move it to the
// new unstable cluster, or 'keep' it.
// In this case, we'll reshuffle OldCluster.PointIndices vector
// so that all the points that are *not* for UnstableOpcode are first,
// and the rest of the points is for the UnstableOpcode.
const auto it = std::stable_partition(
OldCluster.PointIndices.begin(), OldCluster.PointIndices.end(),
[this, &Key](size_t P) {
return OpcodeAndConfig(Points_[P]) != Key;
});
assert(std::distance(it, OldCluster.PointIndices.end()) > 0 &&
"Should have found at least one bad point");
// Mark to-be-moved points as belonging to the new cluster.
std::for_each(it, OldCluster.PointIndices.end(),
[this, &UnstableCluster](size_t P) {
ClusterIdForPoint_[P] = UnstableCluster.Id;
});
// Actually append to-be-moved points to the new cluster.
UnstableCluster.PointIndices.insert(UnstableCluster.PointIndices.end(),
it, OldCluster.PointIndices.end());
// And finally, remove "to-be-moved" points form the old cluster.
OldCluster.PointIndices.erase(it, OldCluster.PointIndices.end());
// Now, the old cluster may end up being empty, but let's just keep it
// in whatever state it ended up. Purging empty clusters isn't worth it.
};
assert(UnstableCluster.PointIndices.size() > 1 &&
"New unstable cluster should end up with more than one point.");
assert(UnstableCluster.PointIndices.size() >= ClusterIDs.size() &&
"New unstable cluster should end up with no less points than there "
"was clusters");
}
}
Expected<InstructionBenchmarkClustering> InstructionBenchmarkClustering::create(
const std::vector<InstructionBenchmark> &Points, const ModeE Mode,
const size_t DbscanMinPts, const double AnalysisClusteringEpsilon,
const MCSubtargetInfo *SubtargetInfo, const MCInstrInfo *InstrInfo) {
InstructionBenchmarkClustering Clustering(
Points, AnalysisClusteringEpsilon * AnalysisClusteringEpsilon);
if (auto Error = Clustering.validateAndSetup()) {
return std::move(Error);
}
if (Clustering.ErrorCluster_.PointIndices.size() == Points.size()) {
return Clustering; // Nothing to cluster.
}
if (Mode == ModeE::Dbscan) {
Clustering.clusterizeDbScan(DbscanMinPts);
if (InstrInfo)
Clustering.stabilize(InstrInfo->getNumOpcodes());
} else /*if(Mode == ModeE::Naive)*/ {
if (!SubtargetInfo || !InstrInfo)
return make_error<Failure>("'naive' clustering mode requires "
"SubtargetInfo and InstrInfo to be present");
Clustering.clusterizeNaive(*SubtargetInfo, *InstrInfo);
}
return Clustering;
}
void SchedClassClusterCentroid::addPoint(ArrayRef<BenchmarkMeasure> Point) {
if (Representative.empty())
Representative.resize(Point.size());
assert(Representative.size() == Point.size() &&
"All points should have identical dimensions.");
for (auto I : zip(Representative, Point))
std::get<0>(I).push(std::get<1>(I));
}
std::vector<BenchmarkMeasure> SchedClassClusterCentroid::getAsPoint() const {
std::vector<BenchmarkMeasure> ClusterCenterPoint(Representative.size());
for (auto I : zip(ClusterCenterPoint, Representative))
std::get<0>(I).PerInstructionValue = std::get<1>(I).avg();
return ClusterCenterPoint;
}
bool SchedClassClusterCentroid::validate(
InstructionBenchmark::ModeE Mode) const {
size_t NumMeasurements = Representative.size();
switch (Mode) {
case InstructionBenchmark::Latency:
if (NumMeasurements != 1) {
errs()
<< "invalid number of measurements in latency mode: expected 1, got "
<< NumMeasurements << "\n";
return false;
}
break;
case InstructionBenchmark::Uops:
// Can have many measurements.
break;
case InstructionBenchmark::InverseThroughput:
if (NumMeasurements != 1) {
errs() << "invalid number of measurements in inverse throughput "
"mode: expected 1, got "
<< NumMeasurements << "\n";
return false;
}
break;
default:
llvm_unreachable("unimplemented measurement matching mode");
return false;
}
return true; // All good.
}
} // namespace exegesis
} // namespace llvm