Evaluating Rust for Sparse Matrix Kernels in Scientific Computing
This work provides evidence-based guidance for modernizing high-performance numerical software stacks by evaluating Rust's performance and safety trade-offs in sparse linear algebra.
Rust's sparse matrix kernels achieve performance comparable to Eigen and PSBLAS for CSC formats, but trail PETSc's blocked CSR optimizations, demonstrating Rust's viability as a memory-safe alternative for scientific computing.
Sparse matrix kernels form the computational backbone of scientific computing, traditionally relying on C/C++ and Fortran implementations that prioritize performance over memory safety. This work evaluates Rust as a systems-level alternative for sparse linear algebra by implementing and benchmarking three core workloads: sparse matrix-vector multiplication (SpMV), Lanczos-based Krylov methods, and matrix-exponential evaluation. We compare native Rust code against established baselines (Intel oneMKL, Eigen, PETSc, and PSBLAS) across a suite of representative matrices. Our results show that Rust's sparse kernels achieve performance comparable to Eigen and PSBLAS, tracking the state-of-the-art for CSC formats, while trailing PETSc's advanced blocked CSR optimizations. By analyzing compile-time monomorphization, SIMD vectorization, and FFI boundaries, we assess the practical impact of Rust's safety model and ecosystem readiness. The study provides concrete, evidence-based guidance for modernizing high-performance numerical software stacks.