Floatbased

Floatbased is a software platform designed to facilitate the development and deployment of systems that process continuous numerical data using floating-point representations. It combines a runtime, a domain-specific language, and a set of libraries to enable real-time analytics, simulations, and modeling tasks where precision can be managed dynamically to balance accuracy and performance.

The project originated in the late 2010s as a research initiative by a consortium of academic and industry partners, aiming to provide explicit control over numerical precision in streaming and batch workloads. The first public release outlined an adaptive precision model and a portable runtime intended to run on conventional CPUs and accelerators, with a permissive open-source license.

Core concepts include an adaptive precision layer that attaches metadata to floating-point values, guiding rounding, range, and error reporting during computation. The runtime offers streaming operators, windowed aggregations, and support for time-stamped data, while the language layer emphasizes concise expressions for numerical transformations and interoperability with existing ecosystems through bindings.

Use cases span meteorology, physical simulations, energy systems, and financial risk analytics, where large volumes of measurements must be stored efficiently and processed under bounded error. Floatbased also supports hybrid workloads combining streaming and batched processing, and emphasizes hardware acceleration and vectorized execution.

Reception has been mixed. Proponents cite improved memory efficiency and explicit error budgets, while critics point to potential numerical instability, the need for robust verification, and interoperability challenges with fixed-point or decimal-based software. Adoption remains concentrated in niche domains requiring precise control of numerical error.

See also: floating-point arithmetic, data streaming, numerical analysis.