Many graph workloads repeatedly run the same traversal or iterative computation from many different source vertices. Multi-instance execution can share work across sources and, if implemented carefully, exploit SIMD by packing per-source state into vectors. However, in vertex-centric graph processing systems, naive ''vectorize everything'' transformations can be wrong: a vertex may be active for only a subset of sources in a round, yet unguarded SIMD updates implicitly advance all lanes, leading to incorrect answers. We present AutoMI, a source-to-source compilation framework that automatically converts a single-instance program into a provably correct SIMD-vectorized multi-instance program runnable on existing vertex-centric engines. AutoMI uses a per-message bit-vector track to recover per-source activation and generate masked SIMD updates. It further provides a TrackFree optimization, guided by an algebraic idempotence characterization, that safely drops track and produces streamlined code when over-activation is harmless. On six real graphs (up to billions of edges) and up to 256 sources, AutoMI achieves 9.6 to 29.5x speedup over parallelized serial evaluation, 7.1 to 26.4x over batch processing, and 2.6 to 4.6x over prior handcrafted multi-instance code.
Zhao et al. (2026) studied this question.