PmxQnx

PmxQnx is a cross‑platform software library designed for real‑time processing of high‑dimensional data streams. Developed as an open‑source project in 2017, it is licensed under the Apache License 2.0 and maintained by a community of contributors focused on scientific computing, machine learning, and signal analysis. The core of PmxQnx consists of a modular architecture that separates data ingestion, transformation, and output stages, allowing developers to plug in custom algorithms without modifying the underlying framework.

Key features of PmxQnx include a low‑latency pipeline engine written in C++ with bindings for Python and Java, support for parallel execution across CPUs and GPUs via OpenMP and CUDA, and built‑in adapters for common data formats such as CSV, JSON, and binary tensor stores. The library provides a set of pre‑implemented operators for filtering, dimensionality reduction, and statistical aggregation, which can be composed using a declarative configuration language or through an imperative API.

PmxQnx is employed in a variety of domains, including financial tick‑data analysis, biomedical signal monitoring, and large‑scale environmental sensor networks. Its emphasis on deterministic performance and reproducibility has made it a preferred choice for research groups that require rigorous benchmarking of algorithmic pipelines. Documentation includes tutorials, API references, and a collection of example projects hosted on the project's GitHub repository.

Since its inception, PmxQnx has seen regular releases, with version 2.3 introducing native support for tensor‑core acceleration and an expanded plugin ecosystem. The project continues to receive contributions from academic institutions and industry partners aiming to advance real‑time data processing capabilities.