Analyzing Secure Memory Architecture for GPUs

Wide adoption of cloud computing makes privacy and security a primary concern. Although recent CPUs have integrated secure memory architecture, such support is still missing for GPUs, a key accelerator in data centers. In this paper, we explore two secure memory architectures, counter-mode encryption and direct encryption, for GPUs and show that we need to architect secure memory differently from it for CPUs. Our in-depth study reveals the following insights. First, as GPUs are designed for high-throughput computation, its secure memory needs to deliver high bandwidth. Second, with counter-mode encryption, the memory traffic resulting from the metadata, i.e., the counters, MACs (message-authentication codes), and integrity tree, may cause significant performance degradation, even in the presence of metadata caches. Third, the sectored cache structure adopted by GPUs leads to multiple sequential accesses to the same metadata cache line, which necessitates the use of MSHRs (miss-status handling registers) for meta-data caches. Fourth, unlike CPUs, separate/partitioned metadata caches perform better than unified metadata caches on GPUs. The reason is that GPU workloads feature streaming accesses, which cause severe contention in the unified metadata cache and the cached counters and integrity tree nodes may be evicted before being reused. Fifth, the massive-threaded nature of GPUs make them latency-tolerant and the performance impact due to the extra encryption/decryption latency is limited. As a result, direct encryption can be a promising alternative for GPU secure memory. The challenge, however, lies in memory integrity verification as the integrity tree may incur high storage overhead and metadata traffic.