{"title":"Partial control-flow linearization","authors":"Simon Moll, Sebastian Hack","doi":"10.1145/3192366.3192413","DOIUrl":null,"url":null,"abstract":"If-conversion is a fundamental technique for vectorization. It accounts for the fact that in a SIMD program, several targets of a branch might be executed because of divergence. Especially for irregular data-parallel workloads, it is crucial to avoid if-converting non-divergent branches to increase SIMD utilization. In this paper, we present partial linearization, a simple and efficient if-conversion algorithm that overcomes several limitations of existing if-conversion techniques. In contrast to prior work, it has provable guarantees on which non-divergent branches are retained and will never duplicate code or insert additional branches. We show how our algorithm can be used in a classic loop vectorizer as well as to implement data-parallel languages such as ISPC or OpenCL. Furthermore, we implement prior vectorizer optimizations on top of partial linearization in a more general way. We evaluate the implementation of our algorithm in LLVM on a range of irregular data analytics kernels, a neutronics simulation benchmark and NAB, a molecular dynamics benchmark from SPEC2017 on AVX2, AVX512, and ARM Advanced SIMD machines and report speedups of up to 146 % over ICC, GCC and Clang O3.","PeriodicalId":20583,"journal":{"name":"Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":"24 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"33","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3192366.3192413","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 33
Abstract
If-conversion is a fundamental technique for vectorization. It accounts for the fact that in a SIMD program, several targets of a branch might be executed because of divergence. Especially for irregular data-parallel workloads, it is crucial to avoid if-converting non-divergent branches to increase SIMD utilization. In this paper, we present partial linearization, a simple and efficient if-conversion algorithm that overcomes several limitations of existing if-conversion techniques. In contrast to prior work, it has provable guarantees on which non-divergent branches are retained and will never duplicate code or insert additional branches. We show how our algorithm can be used in a classic loop vectorizer as well as to implement data-parallel languages such as ISPC or OpenCL. Furthermore, we implement prior vectorizer optimizations on top of partial linearization in a more general way. We evaluate the implementation of our algorithm in LLVM on a range of irregular data analytics kernels, a neutronics simulation benchmark and NAB, a molecular dynamics benchmark from SPEC2017 on AVX2, AVX512, and ARM Advanced SIMD machines and report speedups of up to 146 % over ICC, GCC and Clang O3.