Splyce

Sparse tensor contractions are bottlenecked by coiteration loops that resist standard loop vectorization. We present Splyce, an auto-vectorization framework in MLIR that overcomes this through a dual-path execution model. By decoupling coordinate intersection from pointer management via selective predication, Splyce inherently eliminates data-dependent branches as a side-effect, allowing modern superscalar engines to maximize instruction-level parallelism and hide memory latency. Beyond simple branch elimination, our transformation enables high-throughput speculative execution by saturating parallel functional units previously starved by sequential dependencies. Evaluation across foundational sparse tensor kernels demonstrates performance ranging from 1.94× to 2.86× on synthetic inputs, with consistent speedups sustained across a vast majority of irregular real-world datasets from the SuiteSparse collection. Ultimately, Splyce demonstrates that by converting unpredictable control-flow into a predictable data stream, compiler-driven speculation can effectively reconcile the memory efficiency of compressed storage with the execution-unit throughput of modern superscalar architectures.

Project repository can be found here: https://github.com/KabilanMA/Splyce