$ show kfrlib/kfr
The KFR library is a high-performance C++ framework for digital signal processing (DSP) and audio applications. It provides optimized implementations of FFT/DFT, filter design (FIR/IIR), sample rate conversion, audio file I/O (supporting WAV, FLAC, MP3, AIFF, CAF, etc.), SIMD-accelerated mathematical operations, and tensor/multi-dimensional array operations. The library is designed for real-time processing and scientific computing, with architecture-specific optimizations for x86 SIMD, ARM NEON, and RISC-V RVV, and runtime dispatch for multiarchitecture support. Key architectural features include a header-only design (with optional separate compilation), expression templates for lazy evaluation and operator fusion, a portable SIMD abstraction layer, and a tensor system with NumPy interoperability. The library is used in production by scientific collaborations such as LIGO, Virgo, and KAGRA for gravitational-wave signal processing, benchmarked against FFTW. It employs a dual licensing model (GPLv2+/commercial) and requires C++20 for compilation. The codebase demonstrates strong engineering practices through reusable components such as the SIMD vector abstraction (vec<T, N>), expression template system, tensor class, and audio data handling unit. These components are self-contained, exhibit non-obvious skill in C++ template metaprogramming, and can be extracted for use in other projects with minimal modification. The library is actively maintained, with recent commits and CI/CD pipelines, and shows low technical drift by keeping pace with modern C++ standards and hardware advancements.
The LLM identified 4 files worth highlighting for reuse:
Provides a portable SIMD vector type (vec<T, N>) with architecture-specific backends (SSE, AVX, NEON, RVV) and scalar fallback. Enables writing SIMD code using standard C++ operators without intrinsics, supporting arbitrary vector lengths and types (including complex). Demonstrates non-obvious skill in abstracting hardware-specific details while maintaining performance.
Implements an expression template system for lazy evaluation of mathematical expressions, enabling operator fusion (e.g., a + b * c computes without temporary buffers). Used throughout the library for math, DSP, and tensor operations. Demonstrates advanced C++ metaprogramming skill and is a key performance optimization.
Provides an N-dimensional tensor class with support for slicing, reshaping, transposing, and NumPy (.npy) format I/O. Built on expression templates for efficient element-wise operations and reductions. Demonstrates skill in combining generic programming with practical data science interoperability.
Offers a unified interface for interleaved and planar audio data with zero-copy construction and conversion utilities. Essential for audio processing pipelines, allowing efficient handling of multi-channel audio without unnecessary data duplication. Demonstrates practical engineering for real-world audio applications.