Sparso: Context-driven optimizations of sparse linear algebra

The sparse matrix is a key data structure in various domains such as high-performance computing, machine learning, and graph analytics. To maximize performance of sparse matrix operations, it is especially important to optimize across the operations and not just within individual operations. While a straightforward per-operation mapping to library routines misses optimization opportunities, manually optimizing across the boundary of library routines is time-consuming and error-prone, sacrificing productivity. This paper introduces Sparso, a framework that automates such optimizations, enabling both high performance and high productivity. In Sparso, a compiler and sparse linear algebra libraries collaboratively discover and exploit context, which we define as the invariant properties of matrices and relationships between them in a program. We present compiler analyses, namely collective reordering analysis and matrix property discovery, to discover the context. The context discovered from these analyses drives key optimizations across library routines and matrices. We have implemented Sparso with the Julia language, Intel MKL and SpMP libraries. We evaluate our context-driven optimizations in 6 representative sparse linear algebra algorithms. Compared with a baseline that invokes high-performance libraries without context optimizations, Sparso results in 1.2~17x (average 5.7x) speedups. Our approach of compiler-library collaboration and context-driven optimizations should be also applicable to other productivity languages such as Matlab, Python, and R.