Code size and performance optimization for mobile JavaScript just-in-time compiler

Authors:
Seong-Won Lee;Soo-Mook Moon;Won-Ki Jung;Jin-Seok Oh;Hyeong-Seok Oh
Affiliations:
Seoul National University, Seoul, Korea;Seoul National University, Seoul, Korea;Seoul National University, Seoul, Korea;Seoul National University, Seoul, Korea;Seoul National University, Seoul, Korea
Venue:
Proceedings of the 2010 Workshop on Interaction between Compilers and Computer Architecture
Year:
2010

Citing 2
Cited 1

Context Threading: A Flexible and Efficient Dispatch Technique for Virtual Machine Interpreters

Proceedings of the international symposium on Code generation and optimization
Trace-based just-in-time type specialization for dynamic languages

Proceedings of the 2009 ACM SIGPLAN conference on Programming language design and implementation

Selective just-in-time compilation for client-side mobile javascript engine

CASES '11 Proceedings of the 14th international conference on Compilers, architectures and synthesis for embedded systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Full web browsing with smart phones requires a high-performance JavaScript engine since JavaScript execution with a mobile CPU is slow. So, mobile JavaScript engines employ a just-in-time compiler (JITC), which translates JavaScript code to machine code at runtime. One issue is that since mobile phones suffer from tight memory constraints, the JITC needs to keep a low memory footprint by generating small-sized machine code. In fact, many mobile CPUs support half-sized encoding for small code size with small performance degradation, as in the ARM Thumb2. This paper describes our code generation and optimization for a mobile JavaScript JITC in the Webkit's SquirrelFish Extreme (SFX) for the ARM Thumb2. We try to generate as many 16-bit instructions as possible and reduce the data area, while strictly following the code generation guidelines of the SFX, which actually leaves little room for code optimization. Our experimental results show that we could reduce the code size by 29% with a performance degradation of 3.5%, compared to the ARM version of the SFX.