SIMT-Step Execution: A Flexible Operational Semantics For GPU Subgroup Behavior - Zheyuan ChenSearch

PLDI ✦ April 2026SIMT-Step Execution: A Flexible Operational Semantics For GPU Subgroup Behavior<br>Zheyuan Chen, Naomi Rehman, Guido Martínez, Tyler SorensenACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) , 2026Conference Papers<br>Download PDF<br>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 tests. To investigate how closely SIMT-Step models real-world GPU behavior, we conduct a fuzzing campaign, spanning ten GPUs and eight vendors.Our empirical study shows that non-synchronous behaviors are rare and appear on only a small number of devices; however, detailed investigation into these behaviors was inconclusive as to whether they are intentional or not. Combined, these contributions provide both a theoretical foundation and practical tools for reasoning about subgroup semantics in GPU programming languages.<br>© Zheyuan Chen, 2026Powered by Zola and Duckquill GitHub Google Scholar LinkedIn Copy Codehttps://arbersephirotheca.github.io/search_index.en.json$MATCHES more matches