Levine, R. (CSE) – Validating GPU Memory Consistency and Safety at Scale

Graphics Processing Units (GPUs) have become essential platforms for parallel computing, supporting applications far beyond graphics. Central to GPU programming models is its memory consistency specification (MCS), which defines the semantics of concurrent shared-memory operations and interacts with other language features to determine security guarantees such as memory safety. Understanding whether implementations conform to an MCS, and whether the MCS provides a sound abstraction of real hardware, is essential for reasoning about GPU programs and validating implementations.

This thesis develops techniques and large-scale studies for validating GPU memory consistency and memory safety. First, it introduces MC Mutants, a mutation testing methodology that systematically evaluates GPU MCS test environments. Applied to WebGPU, MC Mutants generates a suite of conformance tests and uncovers two implementation bugs. Next, it presents GPUHarbor, a browser- and Android-based framework for large-scale testing across commodity GPUs. GPUHarbor enables a study of 106 GPUs from seven vendors, reveals two previously unknown memory consistency bugs, and provides new insights into GPU behavior that inform subsequent architectural and security studies. Finally, this thesis presents SafeRace, a collection of security assessments and specification proposals for preserving WebGPU memory safety in the presence of data races. Evaluated across dozens of GPUs and 21 WebGPU compilation stacks, SafeRace identifies vulnerabilities in multiple GPU implementations, including one assigned a CVE, and proposes a validated path toward stronger memory safety guarantees in WebGPU.

Event Host: Reese Levine, Ph.D. Candidate, Computer Science & Engineering

Advisor: Tyler Sorensen

Zoom: https://ucsc.zoom.us/j/94641390195?pwd=RWXp9aprCMqmaAo8nq7oKwqTt02zwN.1

Passcode: 628349