Reductive

Training of optimized product quantizers

Training of optimized product quantizers requires a LAPACK implementation. For this reason, training of the OPQ and GaussianOPQ quantizers is feature-gated by the opq-train feature. opq-train is automatically enabled by selecting a BLAS/LAPACK implementation. The supported implementations are:

• OpenBLAS (feature: openblas)
• Netlib (feature: netlib)
• Intel MKL (feature: intel-mk;)

A backend can be selected as follows:

~~~toml [dependencies] reductive = { version = "0.3", features = ["openblas"] } ~~~

Running tests

To run all tests, specify the BLAS/LAPACK implementation:

~~~shell $ cargo test --verbose --features "openblas" ~~~

Multi-threaded OpenBLAS

reductive uses Rayon to parallelize quantizer training. However, multi-threaded OpenBLAS is known to conflict with application threading. Is you use OpenBLAS, ensure that threading is disabled, for instance by setting the number of threads to 1:

~~~shell $ export OPENBLASNUMTHREADS=1 ~~~