Kn2X

Kn2X is an open-source software framework for high-performance numerical computing, designed to simplify the development of optimized kernels for modern hardware. It provides a domain-specific language to express tensor operations and data transformations, which can be compiled into highly efficient kernels for GPUs and CPUs.

Architecture and features: The core includes a graph-based intermediate representation, a just-in-time compiler, and a runtime that orchestrates execution across devices. It supports kernel fusion, memory optimizations, automatic parallelization, and backends for CUDA, ROCm (HIP), and CPU SIMD. The API is available in Python and C++, with interoperable data structures for tensors and sparse arrays.

History and development: Kn2X originated in an international collaboration of researchers and engineers seeking portable performance. Initial release in 2021 introduced core compiler and a small set of kernels; subsequent releases expanded backend support and tooling, including profiling and debugging utilities.

Applications: It is used in machine learning research, scientific computing, and real-time analytics to accelerate common operations such as matrix multiplications, convolutions, and elementwise transforms. It aims to reduce development time by abstracting low-level optimizations behind a high-level API.

Reception: Evaluations in benchmark suites report competitive performance with established frameworks on selected workloads, while users note a learning curve and the need for mature debugging tools as ongoing concerns. Community contributions continue to drive compatibility with emerging hardware.