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VWDictionary: use multi-core FLANN kNN search (#1760)

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* VWDictionary: use multi-core FLANN kNN search

* Add parameter with default 1 thread

* Kp/FlannTreads plumbing  to UI. Also added to performance tests for comparison.

* fixing ci error

* dump debug data for windows ci

* Adding  more dll debugging report windows ci

* install vc2012 runtime explicitly

* updated comment

---------

Co-authored-by: matlabbe <[email protected]>
This commit is contained in:
Torjus Iveland
2026-09-10 23:22:00 -07:00
committed by GitHub
co-authored by matlabbe
parent fb457255b7
commit 2fbbe19d70
10 changed files with 283 additions and 32 deletions
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corelib/test/FlannIndexBackends.h
+24 -18
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@@ -28,12 +28,21 @@ struct Backend
float rebalancingFactor = 2.0f;
// Not a FlannIndex at all: cv::BFMatcher, what the brute force strategies of
// VWDictionary and RegistrationVis use. Kept in the comparisons as the
// baseline every index has to beat. OpenCV threads its search where the
// indexes here search on one core, so it comes in two flavours: as the
// application gets it, and held to one core to compare the work done rather
// than the time it takes on an idle machine.
// baseline every index has to beat.
bool bruteForce = false;
bool singleCore = false;
// Threads the batch of queries is searched with, as Kp/FlannThreads sets it
// on VWDictionary: 1 to search on one core, 0 for one per core. It says the
// same thing on both sides of bruteForce, which is what makes the rows
// comparable: cv::BFMatcher threads its search too, so it appears in the
// same two flavours as the rtflann trees. A row named "threaded" is the one
// per core one, a row named without it searches on a single core, so that
// the tables compare the work done rather than the time it takes on an idle
// machine.
//
// Of the indexes only the rtflann ones read it, they are the ones searching
// a batch under an OpenMP loop; FlannIndex ignores it for the nanoflann
// ones, which always search on one core.
int cores = 1;
};
// Every algorithm that indexes float features. The exhaustive search comes
@@ -41,9 +50,8 @@ struct Backend
// found and for the time taken.
const Backend FLOAT_BACKENDS[] = {
{"linear exhaustive ", FlannIndex::FLANN_INDEX_LINEAR},
// No single core row for the float features: OpenCV doesn't thread that
// match at these sizes, it measures the same thing as the one above.
{"cv BFMatcher ", FlannIndex::FLANN_INDEX_LINEAR, 1.0f, true},
{"cv BFMatcher ", FlannIndex::FLANN_INDEX_LINEAR, 1.0f, true, 1},
{"cv BFMatcher threaded ", FlannIndex::FLANN_INDEX_LINEAR, 1.0f, true, 0},
{"rtflann kd-tree (4 randomized) ", FlannIndex::FLANN_INDEX_KDTREE},
{"rtflann kd-tree single ", FlannIndex::FLANN_INDEX_KDTREE_SINGLE},
{"nanoflann kd-tree single ", FlannIndex::NANOFLANN_INDEX_KDTREE_SINGLE, 1.0f},
@@ -64,8 +72,8 @@ const Backend EXACT_BACKENDS[] = {
// LSH is for.
const Backend BINARY_BACKENDS[] = {
{"linear exhaustive (hamming) ", FlannIndex::FLANN_INDEX_LINEAR},
{"cv BFMatcher (hamming) ", FlannIndex::FLANN_INDEX_LINEAR, 1.0f, true},
{"cv BFMatcher (hamming,1 core)", FlannIndex::FLANN_INDEX_LINEAR, 1.0f, true, true},
{"cv BFMatcher hamming ", FlannIndex::FLANN_INDEX_LINEAR, 1.0f, true, 1},
{"cv BFMatcher hamming threaded", FlannIndex::FLANN_INDEX_LINEAR, 1.0f, true, 0},
{"rtflann LSH ", FlannIndex::FLANN_INDEX_LSH},
};
@@ -188,11 +196,12 @@ inline Result run(
if(backend.bruteForce)
{
// cv::setNumThreads() is global, put it back before leaving.
// cv::setNumThreads() is global, put it back before leaving. Left alone
// for cores=0: OpenCV's own default is already one thread per core.
const int threads = cv::getNumThreads();
if(backend.singleCore)
if(backend.cores > 0)
{
cv::setNumThreads(1);
cv::setNumThreads(backend.cores);
}
UTimer timer;
@@ -221,10 +230,7 @@ inline Result run(
result.radiusTime = timer.ticks();
}
result.memory = 0; // it indexes nothing
if(backend.singleCore)
{
cv::setNumThreads(threads);
}
cv::setNumThreads(threads);
return result;
}
@@ -233,7 +239,7 @@ inline Result run(
index.buildIndex(backend.algorithm, data, false, rebalancingFactor);
result.buildTime = timer.ticks();
index.knnSearch(queries, result.indices, dists, knn);
index.knnSearch(queries, result.indices, dists, knn, 32, 0.0f, true, backend.cores);
result.knnTime = timer.ticks();
if(radius > 0.0f)