[llvm-exegesis] Add an analysis mode.
Summary:
The analysis mode gives the user a clustered view of the measurement results.
Next steps are (requires the split ok AsmTemplate.Name into {mnemonic, mode}):
- Show the sched class.
- Highlight any inconsistencies with the checked-in data.
Reviewers: gchatelet
Subscribers: mgorny, llvm-commits, tschuett
Differential Revision: https://reviews.llvm.org/D46865
llvm-svn: 332344
diff --git a/llvm/tools/llvm-exegesis/lib/Clustering.cpp b/llvm/tools/llvm-exegesis/lib/Clustering.cpp
index c8646c7..e0eaee3 100644
--- a/llvm/tools/llvm-exegesis/lib/Clustering.cpp
+++ b/llvm/tools/llvm-exegesis/lib/Clustering.cpp
@@ -57,17 +57,18 @@
} // namespace
-InstructionBenchmarkClustering::InstructionBenchmarkClustering()
- : NoiseCluster_(ClusterId::noise()), ErrorCluster_(ClusterId::error()) {}
+InstructionBenchmarkClustering::InstructionBenchmarkClustering(
+ const std::vector<InstructionBenchmark> &Points)
+ : Points_(Points), NoiseCluster_(ClusterId::noise()),
+ ErrorCluster_(ClusterId::error()) {}
-llvm::Error InstructionBenchmarkClustering::validateAndSetup(
- const std::vector<InstructionBenchmark> &Points) {
- ClusterIdForPoint_.resize(Points.size());
+llvm::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];
+ 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);
@@ -96,13 +97,12 @@
return llvm::Error::success();
}
-void InstructionBenchmarkClustering::dbScan(
- const std::vector<InstructionBenchmark> &Points, const size_t MinPts,
- const double EpsilonSquared) {
- for (size_t P = 0, NumPoints = Points.size(); P < NumPoints; ++P) {
+void InstructionBenchmarkClustering::dbScan(const size_t MinPts,
+ const double EpsilonSquared) {
+ for (size_t P = 0, NumPoints = Points_.size(); P < NumPoints; ++P) {
if (!ClusterIdForPoint_[P].isUndef())
continue; // Previously processed in inner loop.
- const auto Neighbors = rangeQuery(Points, P, EpsilonSquared);
+ const auto Neighbors = rangeQuery(Points_, P, EpsilonSquared);
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.
@@ -136,7 +136,7 @@
ClusterIdForPoint_[Q] = CurrentCluster.Id;
CurrentCluster.PointIndices.push_back(Q);
// And extend to the neighbors of Q if the region is dense enough.
- const auto Neighbors = rangeQuery(Points, Q, EpsilonSquared);
+ const auto Neighbors = rangeQuery(Points_, Q, EpsilonSquared);
if (Neighbors.size() + 1 >= MinPts) {
ToProcess.insert(Neighbors.begin(), Neighbors.end());
}
@@ -144,7 +144,7 @@
}
// Add noisy points to noise cluster.
- for (size_t P = 0, NumPoints = Points.size(); P < NumPoints; ++P) {
+ for (size_t P = 0, NumPoints = Points_.size(); P < NumPoints; ++P) {
if (ClusterIdForPoint_[P].isNoise()) {
NoiseCluster_.PointIndices.push_back(P);
}
@@ -155,15 +155,15 @@
InstructionBenchmarkClustering::create(
const std::vector<InstructionBenchmark> &Points, const size_t MinPts,
const double Epsilon) {
- InstructionBenchmarkClustering Clustering;
- if (auto Error = Clustering.validateAndSetup(Points)) {
- return std::move(Error);
+ InstructionBenchmarkClustering Clustering(Points);
+ if (auto Error = Clustering.validateAndSetup()) {
+ return Error;
}
if (Clustering.ErrorCluster_.PointIndices.size() == Points.size()) {
return Clustering; // Nothing to cluster.
}
- Clustering.dbScan(Points, MinPts, Epsilon * Epsilon);
+ Clustering.dbScan(MinPts, Epsilon * Epsilon);
return Clustering;
}