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.
This repository is a high-performance computing library optimized for large language model (LLM) inference and training on NVIDIA GPUs. It provides accelerated implementations of core operations including attention mechanisms, GEMM, fused Mixture of Experts (MoE), all-reduce with normalization, sampling, and rotary position embeddings. The architecture combines C++/CUDA kernels for performance-critical operations with Python bindings via PyTorch, enabling easy integration into deep learning frameworks. The library emphasizes kernel fusion, efficient memory access patterns, and specialized optimizations for modern GPU architectures (particularly Hopper and Blackwell).
Algebrite is a symbolic mathematics library (Computer Algebra System) written in TypeScript. It provides a comprehensive set of operations including symbolic differentiation, integration, polynomial factorization, root finding, matrix/tensor algebra, arbitrary-precision arithmetic, and expression simplification. Internally, it uses an S-expression representation (Cons cells) and a recursive-descent parser to convert string expressions into algebraic trees. The library exposes both a string-based `run()` API for evaluating expressions and direct method calls (e.g., `Algebrite.factor`). It was originally forked from EigenMath and retains some CoffeeScript legacy in test files and debug logging. The codebase is large and monolithic, with many modules tightly coupled through shared global state (the `defs` object). Despite this, the implementation is functional and covers a wide range of mathematical operations, making it suitable for embedding in interactive notebooks or computational tools.