Sparse data handling and buffer sharing to reduce memory allocation and reclamation

Abstract

Sparse data handling and/or buffer sharing are implemented. Data may be buffered in reusable buffer arrays. Data may comprise fixed or variable length vectors, which may be represented as sparse or dense vectors in a values array and indices array. Data may be materialized from a dataview comprising a non-materialized view of data in a machine-learning pipeline by cursoring over rows of the dataview and calling delegate functions to compute data for rows in an active column. A buffer and/or its set of arrays storing a first vector may be reused for a second and additional vectors, for example, when the length of buffer arrays is equal to or greater than the length of the second and additional vectors, which may be selectively stored as sparse or dense vectors to fit the array set. Shared buffers may be passed as references between delegate functions for reuse.

Dependencies are inferred from the claim text.