Key concepts include specialized data structures such as sparse tensors and sparse graphs, operation kernels that operate directly on sparse formats, and deployment models that blend dense and sparse processing. Framework sparse emphasizes separating the concerns of data representation from computation, enabling backends to optimize storage, caching, and parallelism based on actual sparsity patterns. It also encompasses mechanisms for detecting sparsity automatically, lazy evaluation, and dynamic adaptation as sparsity changes during execution.
Architecturally, a frameworksparse-enabled system provides a sparse core with modular backends for storage, computation, and memory management. Interfaces aim to be framework-agnostic, allowing it to work with various languages and runtimes. Common components include a sparse tensor library, a sparse-aware scheduler, and adapters that map high-level operations to efficient sparse kernels on CPUs, GPUs, or accelerators.
Applications span machine learning with sparse feature representations, natural language processing, graph analytics, and scientific computing where matrices or tensors are large but sparsity-dense. It is often discussed in the context of data pipelines and model training workflows that require scalable handling of sparse inputs.
Relation to related areas: framework sparse intersects with sparse linear algebra, graph processing frameworks, and memory-efficient computing. While not a single standardized specification, it informs design choices in many modern systems that need to handle sparse data at scale.