Date Approved

Graphics Processing Units (GPUs) are highly parallel shared memory microprocessors, and as such, they are prone to the same concurrency considerations as their traditional multicore CPU counterparts. In this thesis, we consider shared memory consistency, i.e. what values can be read when issued concurrently with writes on current GPU hardware. While memory consistency has been relatively well studied for CPUs, GPUs present substan-tially different concurrency systems with an explicit thread and memory hierarchy. Because documentation on GPU memory models is limited, it remains unclear what behaviors are allowed by current GPU implementations. To this end, this work focuses on testing shared memory consistency behavior on NVIDIA GPUs. We present a format for describing GPU memory consistency tests (dubbed GPU litmus tests) which includes the placement of testing threads into the GPU thread hierarchy (e.g. cooperative thread arrays, warps) and memory locations into GPU memory regions (e.g. shared, global). We then present a framework for running GPU litmus tests under system stress designed to trigger weak memory model behaviors, that is, executions that d