Safe and Practical GPU Computation in TrustZone
Heejin Park, Felix Xiaozhu Lin
Abstract
For mobile devices, it is compelling to run sensitive GPU computation within a TrustZone trusted execution environment (TEE). To minimize GPU software deployed in TEE, the replay approach is promising: record CPU/GPU interactions on a full GPU stack outside the TEE; replay the interactions inside the TEE without the GPU stack. A key dilemma is that the recording process must both (1) occur in a safe environment and (2) access the exact GPU models to be used for replay. To this end, we present a novel recording architecture called GR-T: a mobile device possessing the GPU hardware collaborates with a GPU-less cloud service which runs the GPU software; the two parties exercise the GPU hardware/software jointly for recording. To overcome the resultant network delays, GR-T contributes optimizations: register access deferral, speculation, and meta-only synchronization. These techniques reduce the recording delay by 20x, from hundreds of seconds to tens of seconds. Replay-based GPU computation incurs 25% lower delays compared to native execution outside TEE. The code is available at https://github.com/bakhi/GPUReplay.