SLAWS: Spatial Locality Analysis and Workload Orchestration for Sparse Matrix Multiplication

Key findings from this study

• The study found that spatial locality analysis of sparse matrix operands reveals data reuse opportunities missed by fixed-dataflow accelerator designs.
• The authors demonstrate that adaptive workload orchestration based on sparsity structure achieves performance across diverse matrix distributions without architectural specialization.
• The researchers propose that decoupling locality characterization from execution scheduling enables greater generality in sparse matrix accelerator systems.

Overview

Sparse matrix-sparse matrix multiplication (SpMSpM) constitutes a critical computational kernel in scientific computing, linear algebra, and graph processing. Nonzero element distributions vary substantially across sparse matrices, creating irregular memory access patterns and load imbalance. Existing specialized hardware accelerators target specific dataflow architectures, limiting generality and failing to capture cross-product data reuse potential.

Methods and approach

The SLAWS framework analyzes spatial locality patterns within sparse matrix operands to inform orchestration of multiplication operations. The approach characterizes how nonzero element distributions affect data reuse across computation stages. Design prioritizes architectural flexibility by decoupling dataflow specification from hardware implementation constraints.

Results

SLAWS identifies opportunities to maximize data locality through adaptive workload scheduling that responds to actual sparsity structure. The framework demonstrates that locality-aware organization captures reuse patterns missed by fixed-architecture designs. By avoiding commitment to a single dataflow, the system accommodates diverse matrix sparsity characteristics without performance degradation.

The locality analysis component isolates data blocks with high reuse potential, enabling targeted optimization of memory hierarchies. Workload orchestration maps computation to processing elements in patterns that minimize data movement. This decomposition of spatial analysis from execution scheduling allows the framework to adapt across different sparse matrix profiles.

Implications

Decoupling locality analysis from architectural dataflow substantially improves generality in sparse matrix accelerator design. Existing specialized accelerators sacrifice adaptability for single-pattern optimization; this framework demonstrates that systematic locality quantification enables broader applicability. Future sparse accelerator development may benefit from treating dataflow as a deployment choice rather than an architectural necessity.

The research indicates that understanding spatial patterns in sparse data distributions directly informs efficient hardware resource allocation. Organizations developing high-performance computing systems for irregular workloads can leverage locality-driven scheduling to reduce energy consumption and increase throughput. The methodology extends beyond SpMSpM to other sparse kernels exhibiting comparable irregularity patterns.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.