GPU hardware implements a SIMT execution model, where small groups of threads, called subgroups (or warps in CUDA), execute synchronously. Languages expose this through high-performance subgroup-level APIs. However, providing precise subgroup semantics in languages is challenging, as compilers may transform the program, potentially disrupting source-level synchronous behavior even if the hardware is synchronous. As a result, no GPU programming language provides rigorous semantics for subgroup execution. In this work, we present SIMT-Step, a formal and flexible operational semantics for subgroup execution. At its core is a new semantic object, dynamic basic blocks, which enables precise specification of converged subgroup execution. SIMT-Step then provides flexibility for the execution of instructions, which can be collective, synchronous, or independent. We propose several candidate instantiations of SIMT-Step and design a suite of idiomatic tests to distinguish them, highlighting counter-intuitive behavior that arises under relaxed variants. We implement SIMT-Step in TLA+ and validate the behavior of the idiomatic tests, all of which can be verified in under one second. Finally, to investigate how closely SIMT-Step models real-world GPU behavior, we conduct a large fuzzing campaign, spanning nine GPUs and seven vendors. Our results show that, despite hesitancy to provide guarantees in official specifications, most GPUs exhibit strongly synchronous behavior. Combined, these contributions provide both a theoretical foundation and practical tools for reasoning about subgroup semantics in GPU programming languages.

@article{paperbot3763,
  title = {SIMT-Step Execution: A Flexible Operational Semantics for GPU Subgroup Behavior},
  author = {Zheyuan Chen and Naomi Rehman and Guido Martínez and Tyler Sorensen},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {10},
  number = {PLDI},
  year = {2026},
  doi = {10.1145/3808297}
}