| Title | Performance Analysis of Scientific Computing Workloads on General Purpose TEEs |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Akram, Ayaz, Giannakou, Anna, Akella, Venkatesh, Lowe-Power, Jason, Peisert, Sean |
| Conference Name | 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS) |
| Keywords | compositionality, Degradation, Hardware, hardware security, HPC, machine learning, Memory management, Predictive Metrics, Prefetching, pubcrawl, Resiliency, Scientific computing, Scientific Computing Security, SEV, SGX, Technological innovation, TEE |
| Abstract | 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 (1x-3.4x slowdown without and 1x-1.15x 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 (1x-4x 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 (1.2x-126x slowdown over unsecure execution). Finally, we discuss forthcoming new TEE designs and their potential impact on scientific computing. |
| DOI | 10.1109/IPDPS49936.2021.00115 |
| Citation Key | akram_performance_2021 |
Performance Analysis of Scientific Computing Workloads on General Purpose TEEs