A high-performance tensor library for the V programming language, providing n-dimensional data structures and linear algebra operations.
VTL (V Tensor Library) is a tensor computation library for the V programming language that provides n-dimensional tensor data structures and a comprehensive suite of mathematical operations. It solves the need for efficient numerical computing and linear algebra within the V ecosystem, enabling developers to perform complex data manipulations and scientific calculations.
V developers working on scientific computing, data analysis, machine learning, or any project requiring numerical computations and linear algebra operations.
Developers choose VTL for its native integration with V, high performance, and the ability to leverage VSL for advanced linear algebra, offering a streamlined alternative to using external libraries or languages for tensor operations.
The V Tensor Library
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Installs directly via V's package manager ('v install vtl') and integrates natively with V code, as shown in the quick start example with 'import vtl'.
Supports n-dimensional tensors with sophisticated reduction, elementwise, and accumulation functions, enabling complex data manipulations for scientific computing.
Data structures are designed for easy passing to C libraries, facilitating extended functionality and leveraging existing C codebases without overhead.
Utilizes VSL for powerful linear algebra routines, providing efficient computations like matrix multiplications and decompositions, as noted in the key features.
Tied to the V language, which has a smaller community and fewer resources, limiting third-party integrations and long-term stability compared to established ecosystems.
Full linear algebra capabilities require separate VSL installation, adding setup complexity and maintenance overhead, as highlighted in the installation instructions.
Lacks published benchmarks against libraries like NumPy, so performance efficiency is uncertain and may not meet expectations for high-throughput numerical tasks.