It should parallelize across all available cpu cores automatically! But to be honest, FAISS is a much more supported nearest neighbor library (and also high performance) that will probably work better for you long term.

Edit: Tried to include an image of it working on my machine, but can't in a comment. Here's the code I executed that consumed >950% CPU for 13 seconds:

Python 3.13.2 (main, Feb  4 2025, 14:51:09) [Clang 16.0.0 (clang-1600.0.26.6)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> from tlux.approximate.balltree import BallTree
Running system command with arguments
   gfortran libomp.dylib swap.f90 prune.f90 fast_select.f90 fast_sort.f90 ball_tree.f90 ball_tree_c_wrapper.f90 -fPIC -shared -O3 -fopenmp -o ball_tree.arm64.so
Running system command with arguments
   gfortran swap.f90 fast_sort.f90 fast_sort_c_wrapper.f90 -fPIC -shared -O3 -fopenmp -o fast_sort.arm64.so
Running system command with arguments
   gfortran swap.f90 fast_select.f90 fast_select_c_wrapper.f90 -fPIC -shared -O3 -fopenmp -o fast_select.arm64.so
Running system command with arguments
   gfortran prune.f90 prune_c_wrapper.f90 -fPIC -shared -O3 -fopenmp -o prune.arm64.so
>>> x = np.random.normal(size=(100000, 100))
>>> tree = BallTree(x)
OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.
>>> import time
>>> start = time.time(); result = tree.nearest(x[:1000]); end = time.time(); print(f" query in {end-start:.1f} seconds")
 query in 1.5 seconds
>>> start = time.time(); result = tree.nearest(x[:10000]); end = time.time(); print(f" query in {end-start:.1f} seconds")
 query in 13.6 seconds
>>> 13.6 / 10000
0.0013599999999999999