GMAI: Understanding and exploiting the internals of GPU resource allocation in critical systems

Critical real-time systems require strict resource provisioning in terms of memory and timing. The constant need for higher performance in these systems has led industry to recently include GPUs. However, GPU software ecosystems are by their nature closed source, forcing system engineers to consider them as black boxes, complicating resource provisioning. In this work, we reverse engineer the internal operations of the GPU system software to increase the understanding of their observed behaviour and how resources are internally managed. We present our methodology that is incorporated in GMAI (GPU Memory Allocation Inspector), a tool that allows system engineers to accurately determine the exact amount of resources required by their critical systems, avoiding underprovisioning. We first apply our methodology on a wide range of GPU hardware from different vendors showing its generality in obtaining the properties of the GPU memory allocators. Next, we demonstrate the benefits of such knowledge in resource provisioning of two case studies from the automotive domain, where the actual memory consumption is up to 5.6× more than the memory requested by the application.
This work has been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2015-65316-P, the HiPEAC Network of Excellence and the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation programme (grant agreement No. 772773). Leonidas Kosmidis is also funded by the Spanish Ministry of Economy and Competitiveness (MINECO) under a Juan de la Cierva Formación postdoctoral fellowship (FJCI-2017-34095).
Peer Reviewed
Postprint (author's final draft)