A Comprehensive Benchmark Suite for Intel SGX

TL;DR

This paper introduces SGXGauge, a comprehensive benchmark suite for Intel SGX TEEs, to standardize performance evaluation and understand behavior across diverse workloads and platform configurations.

Contribution

It presents the first widely accepted benchmark suite for Intel SGX, covering various workloads and analyzing performance impacts with and without a library OS layer.

Findings

Performance drops sharply when memory exceeds EPC size.

Library OS adds minimal overhead (~10%).

Performance metrics focus on paging, memory, and TLB accesses.

Abstract

Trusted execution environments (TEEs) such as \intelsgx facilitate the secure execution of an application on untrusted machines. Sadly, such environments suffer from serious limitations and performance overheads in terms of writing back data to the main memory, their interaction with the OS, and the ability to issue I/O instructions. There is thus a plethora of work that focuses on improving the performance of such environments -- this necessitates the need for a standard, widely accepted benchmark suite (something similar to SPEC and PARSEC). To the best of our knowledge, such a suite does not exist. Our suite, SGXGauge, contains a diverse set of workloads such as blockchain codes, secure machine learning algorithms, lightweight web servers, secure key-value stores, etc. We thoroughly characterizes the behavior of the benchmark suite on a native platform and on a platform that uses a library OS-based shimming layer (GrapheneSGX). We observe that the most important metrics of interest are performance counters related to paging, memory, and TLB accesses. There is an abrupt change in performance when the memory footprint starts to exceed the size of the EPC size in Intel SGX, and the library OS does not add a significant overhead (~ +- 10%).