SpComp: A Sparsity Structure-Specific Compilation of Matrix Operations

TL;DR

SpComp is a compile-time technique that automatically optimizes sparse matrix operations by customizing computations based on non-zero data positions, achieving significant performance improvements without run-time overhead.

Contribution

It introduces a novel compile-time method, SpComp, for optimizing complex sparse matrix operations tailored to data sparsity, eliminating manual tuning and run-time costs.

Findings

Achieves 79% performance gain over TACO

Achieves 83% performance gain over piecewise-regular generator

Achieves 65% performance gain in sparse Cholesky decomposition

Abstract

Sparse matrix operations involve a large number of zero operands which makes most of the operations redundant. The amount of redundancy magnifies when a matrix operation repeatedly executes on sparse data. Optimizing matrix operations for sparsity involves either reorganization of data or reorganization of computations, performed either at compile-time or run-time. Although compile-time techniques avert from introducing run-time overhead, their application either is limited to simple sparse matrix operations generating dense output and handling immutable sparse matrices or requires manual intervention to customize the technique to different matrix operations. We contribute a compile time technique called SpComp that optimizes a sparse matrix operation by automatically customizing its computations to the positions of non-zero values of the data. Our approach neither incurs any run-time overhead nor requires any manual intervention. It is also applicable to complex matrix operations generating sparse output and handling mutable sparse matrices. We introduce a data-flow analysis, named Essential Indices Analysis, that statically collects the symbolic information about the computations and helps the code generator to reorganize the computations. The generated code includes piecewise-regular loops, free from indirect references and amenable to further optimization. We see a substantial performance gain by SpComp-generated SpMSpV code when compared against the state-of-the-art TACO compiler and piecewise-regular code generator. On average, we achieve 79% performance gain against TACO and 83% performance gain against the piecewise-regular code generator. When compared against the CHOLMOD library, SpComp generated sparse Cholesky decomposition code showcases 65% performance gain on average.