Performance Analysis of Scientific Computing Workloads on Trusted Execution Environments

TL;DR

This paper evaluates the performance of AMD SEV and Intel SGX trusted execution environments on various HPC workloads, revealing significant performance impacts and limitations for secure scientific computing.

Contribution

It provides a comprehensive analysis of how TEEs affect HPC workloads, highlighting their performance costs and suitability issues for scientific computing applications.

Findings

SEV performance depends on memory placement and NUMA awareness

Virtualization causes significant slowdown for irregular memory workloads

SGX is unsuitable for HPC due to limited secure memory and inflexible programming

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 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$\times$$-$3.4$\times$ slowdown without and 1$\times$$-$1.15$\times$ 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$\times$$-$4$\times$ slowdown compared to native execution for graph applications) and 3) SGX is inappropriate for HPC given its limited secure memory size and inflexible programming model (1.2$\times$$-$126$\times$ slowdown over unsecure execution). Finally, we discuss forthcoming new TEE designs and their potential impact on scientific computing.