Nikil V. Shyamsunder | Searcharxiv

arXiv2026

Formal Performance and Compile Time Guarantees for Compiler Optimization Heuristics

Modern optimizing compilers rely on heuristic search algorithms for NP-hard optimization problems, which can result in poor generated-code performance and long or unpredictable compile times. These are considered bugs by users, but verified compilers rarely reason beyond semantic preservation. We propose verifying performance and compile time properties of compiler passes. As a proof-of-concept, we formulate inline expansion using a cost model estimating instruction-cache performance. We mechanize this in Rocq, prove semantic preservation of the inlining transformation, and verify the algorithm's monotone improvement, convergence-time bound, and performance bounds for intermediate and final solutions.

cs.PL↗

arXiv2026

A Barrier-Free Synchronization Algorithm for Multi-Engine AI Accelerators

Multi-engine AI accelerators such as AWS Trainium comprise specialized compute engines that execute in parallel, and the compiler must synchronize the data dependencies between them. For straight-line code this is simple: each dependency reduces to waiting for a threshold count of instruction completions, which the compiler computes statically. Loops admit no such static threshold; a simple solution inserts all-engine barriers at iteration boundaries, resetting synchronization state so each loop body can be treated as straight-line, at the cost of parallelism. We present a barrier-free synchronization algorithm that instead enforces each dependency precisely across structured control flow with arbitrarily nested, dynamically bounded loops. The key idea is to compute dynamic thresholds at runtime from tracked loop iteration counts. We implemented it as a compiler backend pass at the AWS Neuron ISA level. On a suite of ML kernels, it reduces latency 10-45% relative to the barrier-based baseline, achieves a 3.3x speedup on a synchronization-bound microbenchmark, and often matches or exceeds hand-tuned manual allocation. Issuing a consumer too early violates its dependency, while issuing too late unnecessarily stalls execution. We formally characterize the minimum synchronization required for correctness and verify in the Lean proof assistant, via bisimulation, that our algorithm meets this criterion.

cs.PL↗