Evaluating Auto-Vectorizing Compilers through Objective Withdrawal of Useful Information

Abstract

The need for compilers to generate highly vectorized code is at an all-time high with the increasing vectorization capabilities of modern processors. To this end, the information that compilers have at their disposal, either through code analysis or via user annotations, is instrumental for auto-vectorization, and hence for the overall performance. However, the information that is available to compilers at compile time and its accuracy varies greatly, as does the resulting performance of vectorizing compilers. Benchmarks like the Test Suite for Vectorizing Compilers (TSVC) have been developed to evaluate the vectorization capability of such compilers. The overarching approach of TSVC and similar benchmarks is to evaluate the compilers under the best possible scenario (i.e., assuming that compilers have access to all useful contextual information at compile time). Although this idealistic view is useful to observe the capability of compilers for auto-vectorization, it is not a true reflection of the conditions found in real-world applications. In this article, we propose a novel method for evaluating the auto-vectorization capability of compilers. Instead of assuming that compilers have access to a wealth of information at compile time, we formulate a method to objectively supply or withdraw information that would otherwise aid the compiler in the autovectorization process. This method is orthogonal to the approach adopted by TSVC, and as such, it provides the means of assessing the capabilities of modern vectorizing compilers in a more detailed way. Using this new method, we exhaustively evaluated five industry-grade compilers (GNU, Intel, Clang, PGI, and IBM) on four representative vector platforms (AVX-2, AVX-512 (Skylake), AVX-512 (KNL), and AltiVec) using the modified version of TSVC and application-level proxy kernels. The results show the impact that withdrawing information has on the vectorization capabilities of each compiler and also prove the validity of the presented technique. CCS Concepts: • Software and its engineering → Compilers;

An SLP Vectorization Method Based on Equivalent Extended Transformation

“…For example, SVE instructions currently support 2048-bit operands. The development of these technologies also brings new problems and challenges to the field of autovectorization [16,17].…”