Vapor SIMD: Auto-Vectorize Once, Run Everywhere [abstract] (IEEE Xplore)
Dorit Nuzman, Sergei Dyshel, Erven Rohou, Ira Rosen, Kevin Williams, David Yuste, Albert Cohen, and Ayal Zaks
Proceedings of the 2011 International Symposium on Code Generation and Optimization (CGO), April 2011.

Just-in-Time (JIT) compiler technology offers portability while facilitating target- and context-specific specialization. Single-Instruction-Multiple-Data (SIMD) hardware is ubiquitous and markedly diverse, but can be difficult for JIT compilers to efficiently target due to resource and budget constraints. We present our design for a synergistic auto-vectorizing compilation scheme. The scheme is composed of an aggressive, generic offline stage coupled with a lightweight, target-specific online stage. Our method leverages the optimized intermediate results provided by the first stage across disparate SIMD architectures from different vendors, having distinct characteristics ranging from different vector sizes, memory alignment and access constraints, to special computational idioms. We demonstrate the effectiveness of our design using a set of kernels that exercise innermost loop, outer loop, as well as straight-line code vectorization, all automatically extracted by the common offline compilation stage. This results in performance comparable to that provided by specialized monolithic offline compilers. Our framework is implemented using open-source tools and standards, thereby promoting interoperability and extendibility.