Update 'Is there GPU or multicore-CPU support?' FAQ

doc/faq.rst

@@ -95,27 +95,20 @@ issues.
 Is there GPU or multicore-CPU support?
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-Not at this time. The bottlenecks in the code are the
-(approximate) nearest neighbor search and the optimization
-of the low dimensional representation. The first of these
-(ANN) is performed by a random projection forest and
-nearest-neighbor-descent. Both of those are, at the least,
-parellelisable in principle, and could be converted to
-support multicore (at the cost of single core performance).
-The optimization is performed via a (slightly custom)
-stochastic gradient descent. SGD is both parallelisable
-and amenable to GPUs. This means that in principle UMAP
-could support multicore and use GPUs for optimization.
-In practice this would involve GPU expertise and would
-potentially hurt single core performance, and so has
-been deferred for now. If you have expertise in GPU
-programming with Numba and would be interested in
-adding GPU support we would welcome your contributions.
-
 There is a UMAP implementation for GPU available in
 the NVIDIA RAPIDS cuML library, so if you need GPU
 support that is currently the best place to go.
 
+For multicore CPU, the two main bottlenecks in the code are the
+(approximate) nearest neighbor search and the optimization of the low
+dimensional representation. The first of these has a multicore implementation
+in the pynndescent library, which is used by UMAP if it is installed.
+Otherwise UMAP uses its own version of nearest neighbor search, which is not
+multicore. The second bottleneck, the optimization of the low dimensional
+representation is performed via a (slightly custom) stochastic gradient
+descent. SGD in UMAP can take advantage of multicore, but only if
+`random_state` is set to `None` (as explained in the next question).
+
 Is the output of UMAP reproducible?
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