In the present computing landscape, interpreters are in use in a wide range of systems. Recent trends in consumer electronics have created a new category of portable, lightweight software applications. Typically, these applications have fast development cycles and short life spans. They run on a wide range of systems and are deployed in a target independent bytecode format over Internet and cellular networks. Their authors are untrusted third-party vendors, and they are executed in secure managed runtimes or virtual machines. Furthermore, due to security policies or development time constraints, these virtual machines often lack just-in-time compilers and rely on interpreted execution. At the other end of the spectrum, interpreters are also a reality in the field of high-performance computations because of the flexibility they provide. The main performance penalty in interpreters arises from instruction dispatch. Each bytecode requires a minimum number of machine instructions to be executed. In this work, we introduce a novel approach for interpreter optimization that reduces instruction dispatch thanks to vectorization technology. We extend the split compilation paradigm to interpreters, thus guaranteeing that our approach exhibits almost no overhead at runtime. We take advantage of the vast research in vectorization and its presence in modern compilers. Complex analyses are performed ahead of time, and their results are conveyed to the executable bytecode. At runtime, the interpreter retrieves this additional information to build the SIMD IR (intermediate representation) instructions that carry the vector semantics. The bytecode language remains unmodified, making this representation compatible with legacy interpreters and previously proposed JIT compilers. We show that this approach drastically reduces the number of instructions to interpret and decreases execution time of vectorizable applications. Moreover, we map SIMD IR instructions to hardware SIMD instructions when available, with a substantial additional improvement. Finally, we finely analyze the impact of our extension on the behavior of the caches and branch predictors.

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.