The recently introduced second generation of Intel SGX (SGXv2) lifts memory size limitations of the first generation. Theoretically, this promises to enable secure and highly efficient analytical DBMSs in the cloud. To validate this promise, in this paper, we conduct the first in-depth evaluation study of running analytical query processing algorithms inside SGXv2. Our study reveals that state-of-the-art query operators like radix joins and SIMD-based scans can indeed achieve high performance inside SGXv2 enclaves. These operations are orders of magnitude faster than joins optimized for the discontinued SGXv1 hardware. However, substantial performance overheads are still caused by subtle hardware and software differences influencing code execution inside an SGX enclave. We investigate these differences and propose new optimizations to bring the performance inside the enclave on par with native code execution outside an enclave.

中文翻译：

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英特尔 SGXv2 中的分析查询处理基准测试

最近推出的第二代 Intel SGX (SGXv2) 解除了第一代的内存大小限制。从理论上讲，这有望在云中实现安全、高效的分析 DBMS。为了验证这一承诺，在本文中，我们首次对在 SGXv2 内运行分析查询处理算法进行了深入的评估研究。我们的研究表明，最先进的查询运算符（例如基数连接和基于 SIMD 的扫描）确实可以在 SGXv2 enclave 内实现高性能。这些操作比针对已停产的 SGXv1 硬件优化的连接要快几个数量级。然而，影响 SGX enclave 内部代码执行的细微硬件和软件差异仍然会导致大量性能开销。我们研究这些差异并提出新的优化，以使 enclave 内部的性能与 enclave 外部的本机代码执行相同。