安全实用的TrustZone中的GPU加速
Safe and Practical GPU Acceleration in TrustZone
摘要：
我们提出了一个整体设计的GPU加速计算在TrustZone TEE。无需将复杂的GPU软件堆栈拉入TEE，我们遵循一种简单的方法：提前记录CPU/GPU交互，并在运行时间在TEE中重放交互。本文解决了该方法的关键缺失部分-录制环境，该环境需要强大的安全性和对各种移动GPU的访问。为此，我们提出了一种新的架构，称为CODY，其中一个移动终端（拥有GPU硬件）和一个值得信赖的云服务（运行GPU软件）行使GPU硬件/软件的协作，分布式的方式。为了克服大量的网络往返和长时间延迟，CODY提供了针对移动GPU的优化：寄存器访问延迟，推测和仅元状态同步。通过这些优化，记录计算工作负载只需数十秒，比原始方法节省高达95%;与不安全的本机执行相比，重播的延迟降低了25%。
英文摘要：
We present a holistic design for GPU-accelerated computation in TrustZone TEE. Without pulling the complex GPU software stack into the TEE, we follow a simple approach: record the CPU/GPU interactions ahead of time, and replay the interactions in the TEE at run time. This paper addresses the approach's key missing piece -- the recording environment, which needs both strong security and access to diverse mobile GPUs. To this end, we present a novel architecture called CODY, in which a mobile device (which possesses the GPU hardware) and a trustworthy cloud service (which runs the GPU software) exercise the GPU hardware/software in a collaborative, distributed fashion. To overcome numerous network round trips and long delays, CODY contributes optimizations specific to mobile GPUs: register access deferral, speculation, and metastate-only synchronization. With these optimizations, recording a compute workload takes only tens of seconds, which is up to 95% less than a naive approach; replay incurs 25% lower delays compared to insecure, native execution.
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