[llvm] r350207 - Revert rL350035 "[llvm-exegesis] Clustering: don't enqueue a point multiple times"
Clement Courbet via llvm-commits
llvm-commits at lists.llvm.org
Wed Jan 2 01:21:01 PST 2019
Author: courbet
Date: Wed Jan 2 01:21:00 2019
New Revision: 350207
URL: http://llvm.org/viewvc/llvm-project?rev=350207&view=rev
Log:
Revert rL350035 "[llvm-exegesis] Clustering: don't enqueue a point multiple times"
Let's discuss this on the review thread before submitting.
Modified:
llvm/trunk/tools/llvm-exegesis/lib/Clustering.cpp
Modified: llvm/trunk/tools/llvm-exegesis/lib/Clustering.cpp
URL: http://llvm.org/viewvc/llvm-project/llvm/trunk/tools/llvm-exegesis/lib/Clustering.cpp?rev=350207&r1=350206&r2=350207&view=diff
==============================================================================
--- llvm/trunk/tools/llvm-exegesis/lib/Clustering.cpp (original)
+++ llvm/trunk/tools/llvm-exegesis/lib/Clustering.cpp Wed Jan 2 01:21:00 2019
@@ -8,6 +8,7 @@
//===----------------------------------------------------------------------===//
#include "Clustering.h"
+#include "llvm/ADT/SetVector.h"
#include "llvm/ADT/SmallVector.h"
#include <string>
@@ -91,14 +92,8 @@ llvm::Error InstructionBenchmarkClusteri
}
void InstructionBenchmarkClustering::dbScan(const size_t MinPts) {
- const size_t NumPoints = Points_.size();
-
- // Persistent buffers to avoid allocs.
- std::vector<size_t> Neighbors;
- std::vector<size_t> ToProcess(NumPoints);
- std::vector<char> Processed(NumPoints);
-
- for (size_t P = 0; P < NumPoints; ++P) {
+ 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);
@@ -114,40 +109,43 @@ void InstructionBenchmarkClustering::dbS
Cluster &CurrentCluster = Clusters_.back();
ClusterIdForPoint_[P] = CurrentCluster.Id; /* Label initial point */
CurrentCluster.PointIndices.push_back(P);
- Processed[P] = 1;
- // Enqueue P's neighbors.
- size_t Tail = 0;
- auto EnqueueUnprocessed = [&](const std::vector<size_t> &Neighbors) {
- for (size_t Q : Neighbors)
- if (!Processed[Q]) {
- ToProcess[Tail++] = Q;
- Processed[Q] = 1;
- }
- };
- EnqueueUnprocessed(Neighbors);
-
- for (size_t Head = 0; Head < Tail; ++Head) {
- // Retrieve a point from the queue and add it to the current cluster.
- P = ToProcess[Head];
- ClusterId OldCID = ClusterIdForPoint_[P];
- ClusterIdForPoint_[P] = CurrentCluster.Id;
- CurrentCluster.PointIndices.push_back(P);
- if (OldCID.isNoise())
+ // Process P's neighbors.
+ llvm::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;
- assert(OldCID.isUndef());
-
- // And extend to the neighbors of P if the region is dense enough.
- rangeQuery(P, Neighbors);
- if (Neighbors.size() + 1 >= MinPts)
- EnqueueUnprocessed(Neighbors);
+ }
+ 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; P < NumPoints; ++P)
- if (ClusterIdForPoint_[P].isNoise())
+ for (size_t P = 0, NumPoints = Points_.size(); P < NumPoints; ++P) {
+ if (ClusterIdForPoint_[P].isNoise()) {
NoiseCluster_.PointIndices.push_back(P);
+ }
+ }
}
llvm::Expected<InstructionBenchmarkClustering>
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