simdjson is a high-performance C++ JSON parsing library that uses SIMD instructions (AVX2, AVX-512, NEON, RVV, etc.) to parse and validate JSON at multi-gigabyte-per-second speeds. It provides two primary APIs: a DOM API (`simdjson::dom`) that builds a complete in-memory tree, and an On-Demand API (`simdjson::ondemand`) that performs lazy, forward-only parsing for streaming and large-document use cases. The library also includes a builder API for JSON serialization, compile-time JSON parsing via C++26 reflection, JSON Pointer/Path support, NDJSON/JSON-sequence streaming, and a single-header amalgamated distribution. The codebase is organized around a two-stage pipeline: stage 1 performs SIMD-accelerated structural indexing and UTF-8 validation, and stage 2 builds the DOM tape or drives the on-demand iterator. Runtime CPU dispatch selects the best implementation for the host architecture, with fallbacks for non-SIMD platforms. The repository is a mature, production-grade library used by major projects (Node.js, ClickHouse, etc.) and is backed by an extensive test suite, fuzzing harnesses, and cross-architecture CI. It is primarily an application/library rather than a data-research repository, though it contains benchmark and research-oriented components (e.g., performance modeling, static reflection experiments).
Corrfunc is a high-performance scientific computing library for cosmology, specifically for computing two-point correlation functions and the void probability function from galaxy catalogs. It provides both a C library/command-line interface and Python bindings through C extensions. The code implements pair counting for real-space (DD, xi, wp), redshift-space (DDrppi, DDsmu), angular (DDtheta), and mock/survey geometries, with support for periodic simulation boxes and line-of-sight survey data. The architecture is a hybrid C core with Python wrappers: OpenMP parallelized C kernels, runtime dispatch across SSE4.2/AVX/AVX2/AVX512 intrinsics, grid-based cell decomposition, and a custom fast-food binary format for fast I/O. The project also includes a substantial test suite with reference outputs, CI workflows, and benchmark scripts used in a peer-reviewed performance paper. Despite being packaged as an application/library, the repository contains research artifacts: benchmark .npz files, paper scripts, and astrophysical mock-catalog test data. These are support materials for validating and advertising performance, not the primary deliverable. The dominant character is clearly a reusable scientific library, not a data-analysis pipeline. The codebase is mature and well-engineered for its niche, but shows some drift: Python 2 compatibility remnants, a complex dual Makefile/setup.py build, heavy code duplication between theory and mock modules, and platform-specific SIMD code that requires careful maintenance. It remains actively maintained and is the de facto standard CPU tool for fast correlation functions in cosmology.