sem_cython12

OpenMP-parallel numerical kernel library for Python. Pre-built Linux shared object included; no compilation required at install time.

Contents

• sem_cython12/sem_core12.cpython-312-x86_64-linux-gnu.so - compiled extension (Linux, CPython 3.12, x86_64).
• sem_cython12/wrapper.py - Python API.
• sem_cython12/__init__.py - package entry.

Requirements

• Linux x86_64.
• CPython 3.12.
• numpy >= 1.23 (see requirements.txt).
• A modern glibc + libgomp. Both ship with Ubuntu 20.04 LTS and later. No other system libraries needed.

The Windows / macOS binaries are not included in this distribution.

Install

git clone https://git.sevana.biz/vvs/sem_cython12.git
cd sem_cython12
pip install -r requirements.txt
# Make the package importable, either:
pip install -e .                      # if pyproject.toml/setup.py is added
# or just put the package on PYTHONPATH:
export PYTHONPATH=$PWD:$PYTHONPATH

Quick start

import numpy as np
from sem_cython12 import wrapper as cy

# Sanity check
assert cy.available(), "compiled extension did not load"
print("backend:", cy.backend())

# Thread count (defaults to ~50% of logical cores; set explicitly via
# either the SEM_NUM_THREADS env var or set_num_threads()):
cy.set_num_threads(8)
print("threads:", cy.get_num_threads())

# Example workload
rng = np.random.default_rng(0)
Q = rng.standard_normal((1000, 32))         # 1000 queries
M = rng.standard_normal((5000, 32))         # 5000 reference points

# For each query: max similarity to any reference, with kernel scale lam.
sim = cy.batch_max_similarity(Q, M, lam=1.0)
print(sim.shape, sim.dtype)                 # (1000,) float64

API reference

All functions accept either Python lists or numpy arrays; inputs are internally cast to contiguous float64. Outputs are numpy arrays.

Configuration

Function	Purpose
available() -> bool	True iff the compiled extension loaded
backend() -> str	'cython12' or 'python-fallback'
get_num_threads() -> int	Active OpenMP worker count
set_num_threads(n: int)	Set OpenMP worker count (n >= 1)

Distance / similarity

Function	Inputs	Output
batch_max_similarity(X_query, X_members, lam)	(Q, D), (M, D), lam > 0	(Q,) - per-query similarity score in [0, 1] against the closest member
concept_support_matrix(X_query, member_mats, lam)	(Q, D), list of (M_k, D), lam > 0	(Q, K) - one similarity column per member set
pairwise_distances(X)	(N, D)	(N, N) - symmetric distance matrix between rows
nn_distances(X)	(N, D)	(N,) - min positive distance per row; inf if none

Best-tradeoff filtering

Function	Inputs	Output
pareto_core_mask(S)	(N, k)	(N,) byte mask: rows that survive the multi-objective best-tradeoff filter
one_sided_mask(S)	(N, k)	(N, k) byte mask: rows contributing meaningfully on a single column only
non_redundant_witnesses(S)	(N, k)	int32 array of row indices contributing meaningfully across multiple columns

Vector reduction

Function	Inputs	Output
extend_frontier_kernel(cur_centers, cur_radii, new_emb, cur_arity)	(F, D), (F,), (A, D), int	(flat_centers (F*A, D), flat_radii (F*A,))

See the wrapper docstrings for exact semantics of each function.

Performance notes

Threads are configured globally per process; calling set_num_threads(n) updates the OpenMP team size for all subsequent calls. The default uses approximately 50% of the host's logical cores so other processes are not starved on shared machines.

For workloads dominated by pairwise_distances and pareto_core_mask, near-linear scaling up to ~8 threads is typical on commodity x86 hardware. batch_max_similarity is BLAS-friendly and benefits most from larger M (reference set) at fixed D.

Memory / threading model

• All arrays are processed in shared memory; no inter-process serialisation.
• Each routine releases the GIL during its inner loops, so calling it concurrently from Python threads is safe.
• The compiled extension links against the system OpenMP runtime (libgomp); avoid mixing with conda's intel-openmp in the same process if possible.

Diagnostics

backend() returns 'python-fallback' only when the .so failed to import (wrong architecture, glibc too old, missing libgomp). In that state, every numerical function raises RuntimeError; check available() before each batch to fail loudly rather than silently fall back.

Licence

Proprietary. Internal use only.

Support

Open an issue at https://git.sevana.biz/vvs/sem_cython12.