Added timings to demonstrate efficacy of the approximations.

This is done both in serial and with OpenMP-based parallelization.

.gitignore

@@ -1,6 +1,8 @@
 *.o
 *.swp
 *.so
+*.Rout
+*.pdf
 build/
 docs/html
 docs/latex

README.md

@@ -74,6 +74,15 @@ This eliminates near-redundant searches through the tree for each point $i$, wit
 We call this approach the "leaf approximation", which is enabled through the `leaf_approximation` parameter.
 Note that this only has an effect in `max_depth`-bounded trees where multiple points are assigned to a leaf node.
 
+Some testing indicates that both approximations can significantly speed up calculation of the embeddings.
+Timings are shown below in seconds, based on a mock dataset containing 50,000 points (see [`tests/R/examples/basic.R`](tests/R/examples/basic.R) for details).
+
+|Strategy|Serial|Parallel (OpenMP, n = 4)|
+|----|----|---|
+|Default|136|42| 
+|`max_depth = 7`|85|26| 
+|`max_depth = 7`, `leaf_approximation = true`|46|16| 
+
 ## Building projects
 
 ### CMake with `FetchContent`

tests/R/examples/basic.R

@@ -15,3 +15,7 @@ par(mfrow=c(1,3))
 plot(ref[,1], ref[,2], col=chosen, main="No approximation")
 plot(maxd[,1], maxd[,2], col=chosen, main="Maximum depth")
 plot(leaf[,1], leaf[,2], col=chosen, main="Leaf approximation")
+
+system.time(ref <- runTsne(res$index, res$distance, init=init, num.threads=4))
+system.time(maxd <- runTsne(res$index, res$distance, init=init, max.depth=7, num.threads=4))
+system.time(leaf <- runTsne(res$index, res$distance, init=init, max.depth=7, leaf.approx=TRUE, num.threads=4))

tests/R/package/src/Makevars

@@ -3,3 +3,5 @@
 # are available.
 BUILD_DIR=../../../../build/_deps
 PKG_CPPFLAGS=-I${BUILD_DIR}/aarand-src/include -I${BUILD_DIR}/kmeans-src/include -I${BUILD_DIR}/powerit-src/include -I${BUILD_DIR}/knncolle-src/include -I../../../../include
+PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS)
+PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS)

tests/R/package/src/test.cpp

@@ -21,6 +21,7 @@ Rcpp::NumericMatrix run_tsne(
     opt.stop_lying_iter = lie_iter;
     opt.mom_switch_iter = mom_iter;
     opt.leaf_approximation = leaf_approx;
+    opt.num_threads = num_threads;
 
     int nr = indices.nrow(), nc = indices.ncol();
     qdtsne::NeighborList<int, double> neighbors(nc);