Performance Analysis of Scientific Computing Workloads on General Purpose TEEs
Performance Analysis of Scientific Computing Workloads on General Purpose TEEs
复制标题
DOI：
10.1109/ipdps49936.2021.00115
复制
发表时间：
2021-05
期刊：
影响因子：
--
通讯作者：
Ayaz Akram;Anna Giannakou;V. Akella;Jason Lowe-Power;S. Peisert
中科院分区：
文献类型：
--
作者：
Ayaz Akram;Anna Giannakou;V. Akella;Jason Lowe-Power;S. Peisert
科学计算有时涉及敏感数据的计算。根据数据和执行环境，HPC（高性能计算）用户或数据提供者可能需要机密性和/或完整性保证。为了研究基于硬件的可信执行环境（TEE）在实现安全科学计算方面的适用性，我们深入分析了通用TEE、AMD SEV和英特尔SGX对各种HPC基准测试的性能影响，包括传统科学计算、机器学习、图形分析和新兴科学计算工作负载。我们观察到三个主要发现：1）SEV需要在大规模NUMA机器上仔细放置内存（1×-3.4×减速（无NUMA感知放置）和1×-1.15×减速（有NUMA感知放置）），2）虚拟化（SEV的先决条件）会导致具有不规则内存访问和大型工作集的工作负载的性能下降（与图形应用程序的本机执行相比，速度减慢1×-4×）和3）SGX不适合HPC，因为它的安全内存大小有限，编程模型不灵活（与不安全执行相比，速度减慢1.2×-126×）。最后，我们将讨论即将推出的新的TEE设计及其对科学计算的潜在影响。
Scientific computing sometimes involves computation on sensitive data. Depending on the data and the execution environment, the HPC (high-performance computing) user or data provider may require confidentiality and/or integrity guarantees. To study the applicability of hardware-based trusted execution environments (TEEs) to enable secure scientific computing, we deeply analyze the performance impact of general purpose TEEs, AMD SEV, and Intel SGX, for diverse HPC benchmarks including traditional scientific computing, machine learning, graph analytics, and emerging scientific computing workloads. We observe three main findings: 1) SEV requires careful memory placement on large scale NUMA machines (1×–3.4× slowdown without and 1×–1.15× slowdown with NUMA aware placement), 2) virtualization—a prerequisite for SEV— results in performance degradation for workloads with irregular memory accesses and large working sets (1×–4× slowdown compared to native execution for graph applications) and 3) SGX is inappropriate for HPC given its limited secure memory size and inflexible programming model (1.2×–126× slowdown over unsecure execution). Finally, we discuss forthcoming new TEE designs and their potential impact on scientific computing.