Function/Kernel Vectorization via Loop Vectorizer

Currently, there are three vectorizers in the LLVM trunk: Loop Vectorizer, SLP Vectorizer, and Load-Store Vectorizer. There is a need for vectorizing functions/kernels: 1) Function calls are an integral part of programming real world application code and we cannot always rely on fully inlining them. When a function call is made from a vectorized context such as vectorized loop or vectorized function, if there are no vectorized callees available, the call has to be made to a scalar callee, one vector element at a time. At the programming model level, OpenMP declare simd is a standardized syntax to address this problem. LLVM needs a vectorizer to properly vectorize OpenMP declare simd functions. 2) Also, in the GPGPU programming model, such as OpenCL, work-item (thread) parallelism is not expressed with a loop; it is implicit in the execution of the kernels. In order to exploit SIMD parallelism at this top-level (thread-level), we need to start from vectorizing the kernel. One of the obvious ways to vectorize functions/kernels is to add a fourth vectorizer that specifically deals with function vectorization. In this paper, we argue that such a naive approach will lead us to sub-optimal performance and/or higher maintenance burden. Instead, we present a technique to take advantages of the current functionalities and future improvements of Loop Vectorizer in order to vectorize functions and kernels.