Bring Your Own Formats and Kernels: Composable Abstractions for Sparse Matrix Computation

Abstract

Real-world sparse matrices often feature multiple forms of structured sparsity -- rectangular dense blocks, diagonal bands, and scattered entries -- that no single storage format can efficiently exploit. Hybrid formats address this by storing each subregion of a matrix in its most efficient form. Existing hybrid approaches, however, only support fixed sets of formats and kernels, so incorporating a new representation or kernel requires modifying their internals. We present SABLE, a framework that lets users build bespoke hybrid formats compositionally through a \emph{plan-extract-dispatch} interface. Users define \emph{extractors} that carve a matrix into format-specific regions and \emph{kernels} that emit specialized C code for each region; SABLE assembles these pieces into a single program specialized to the target matrix at compile time. Both components are independent and composable, so a new format automatically integrates with all existing kernels without any changes to the framework. We demonstrate this extensibility by introducing VDIA, a novel format for diagonal bands of non-uniform length, and composing it to build two new hybrid formats -- VDIA+CSR and VDIA+VBR+CSR. We evaluate SABLE on SpMV and SpMM using matrices from the SuiteSparse benchmarks, demonstrating geometric-mean speedups over the best fully-sparse baselines of $1.10\times/1.20\times$ (SpMV/SpMM) for VBR+CSR, and $1.14\times/1.31\times$ for VDIA+CSR, with the full VDIA+VBR+CSR composition yielding a further $1.08\times/1.25\times$ over VBR+CSR.