Evaluation of Compilers’ Capability of Automatic Vectorization Based on Source Code Analysis

Automatic vectorization is an important technique for compilers to improve the parallelism of programs. With the widespread usage of SIMD (Single Instruction Multiple Data) extensions in modern processors, automatic vectorization has become a hot topic in the research of compiler techniques. Accurately evaluating the effectiveness of automatic vectorization in typical compilers is quite valuable for compiler optimization and design. This paper evaluates the effectiveness of automatic vectorization, analyzes the limitation of automatic vectorization and the main causes, and improves the automatic vectorization technology. This paper firstly classifies the programs by two main factors: program characteristics and transformation methods. Then, it evaluates the effectiveness of automatic vectorization in three well-known compilers (GCC, LLVM, and ICC, including their multiple versions in recent 5 years) through TSVC (Test Suite for Vectorizing Compilers) benchmark. Furthermore, this paper analyzes the limitation of automatic vectorization based on source code analysis, and introduces the differences between academic research and engineering practice in automatic vectorization and the main causes, Finally, it gives some suggestions as to how to improve automatic vectorization capability.

Not so fast: understanding and mitigating negative impacts of compiler optimizations on code reuse gadget sets

Despite extensive testing and correctness certification of their functional semantics, a number of compiler optimizations have been shown to violate security guarantees implemented in source code. While prior work has shed light on how such optimizations may introduce semantic security weaknesses into programs, there remains a significant knowledge gap concerning the impacts of compiler optimizations on non-semantic properties with security implications. In particular, little is currently known about how code generation and optimization decisions made by the compiler affect the availability and utility of reusable code segments called gadgets required for implementing code reuse attack methods such as return-oriented programming. In this paper, we bridge this gap through a study of the impacts of compiler optimization on code reuse gadget sets. We analyze and compare 1,187 variants of 20 different benchmark programs built with two production compilers (GCC and Clang) to determine how their optimization behaviors affect the code reuse gadget sets present in program variants with respect to both quantitative and qualitative metrics. Our study exposes an important and unexpected problem; compiler optimizations introduce new gadgets at a high rate and produce code containing gadget sets that are generally more useful to an attacker than those in unoptimized code. Using differential binary analysis, we identify several undesirable behaviors at the root of this phenomenon. In turn, we propose and evaluate several strategies to mitigate these behaviors. In particular, we show that post-production binary recompilation can effectively mitigate these behaviors with negligible performance impacts, resulting in optimized code with significantly smaller and less useful gadget sets.