High-Performance Dynamic Language Implementation

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Dynamic programming languages are being used in all areas of the software industry from utilitarian scripts to web apps to embedded systems. Recent surveys show that dynamic languages like Python and JavaScript have surpassed static languages like C and Java in popularity for software development. This is unsurprising due to their ease of use and the large user communities that contribute to the vast collections of ready to use libraries. Current implementations of dynamic languages use highly complex multi-tier designs which nevertheless still lag in terms of execution speed when compared to static languages. The unfortunate consequence is that performance critical applications, such as those in the increasingly important domains of machine learning, data mining, and physical simulation, are usually programmed in static languages and can't benefit from the features of dynamic languages. The long term objectives of the proposed research program is to increase the performance of dynamic languages and to lower the development cost of VMs for these languages. The work outlined in this research proposal builds on our many years of experience developing high-performance implementations of dynamic languages. We plan to continue exploring the Basic Block Versioning (BBV) compilation approach which we have designed and is particularly well suited to lower the cost of dynamic constructs while at the same time being relatively easy to put in place in a Just In Time (JIT) compiler or VM. A promising part of our research is an Ahead Of Time (AOT) variant of BBV which can be applied when a JIT is not an option, or to lower the warmup time of the JIT. We will also explore the use of BBV to perform partial function inlining, an optimization well suited for dynamic languages whose builtin operations are complex but a hot path that is worth inlining often exists. We foresee that BBV can be used to optimize memory allocation, a frequent operation for dynamic languages, so this will be investigated. The knowledge learned from this research will help improve the performance of implementations of dynamic languages, and specifically Scheme and Python in the short-term through our work on the Gambit and Zipi compilers respectively. Moreover, we will work on a toolkit based on BBV that provides an intermediate language for easily building high-performance VMs for any dynamic language.