Constructing and characterizing covert channels on GPGPUs

Abstract

General Purpose Graphics Processing Units (GPGPUs) are present in most modern computing platforms. They are also increasingly integrated as a computational resource on clusters, data centers, and cloud infrastructure, making them possible targets for attacks. We present a first study of covert channel attacks on GPGPUs. GPGPU attacks offer a number of attractive properties relative to CPU covert channels. These channels also have characteristics different from their counterparts on CPUs. To enable the attack, we first reverse engineer the hardware block scheduler as well as the warp to warp scheduler to characterize how co-location is established. We exploit this information to manipulate the scheduling algorithms to create co-residency between the trojan and the spy. We study contention on different resources including caches, functional units and memory, and construct operational covert channels on all these resources. We also investigate approaches to increase the bandwidth of the channel including: (1) using synchronization to reduce the communication cycle and increase robustness of the channel; (2) exploiting the available parallelism on the GPU to increase the bandwidth; and (3) exploiting the scheduling algorithms to create exclusive co-location to prevent interference from other possible applications. We demonstrate operational versions of all channels on three different Nvidia GPGPUs, obtaining error-free bandwidth of over 4 Mbps, making it the fastest known microarchitectural covert channel under realistic conditions.