Optimizing Java Bytecodes

This paper concentrates on optimizations which rely on the knowledge of the target architecture, so they cannot be performed by the compiler which generates the class file, since the target machine is not known at that time. At the same time, the optimization techniques we consider cannot be easily performed on bytecodes directly and require the recovery of high-level representation of the code which is being optimized. Briki is a compiler developed to research the issues of potential benefits of high-level optimizations for Java programs. Briki reads in a Java program distributed in the bytecode form, converts it into JavaIR (an intermediate representation used to represent Java programs in our compiler), performs the optimizations, and writes out the optimized code. We are primarily interested in the configuration of Briki which performs JIT compilation, i.e., a compiler which is integrated with the virtual machine and generates machine code for immediate execution. The current implementation of Briki, which was used in the experiments presented in this paper, reads in a class file and writes the optimized code to another file in the form of Java source. We chose off-line compilation and Java source as the output form for the ease of debugging and better understanding of the quality of the recovered code. A JIT implementation of Briki which will integrate our compiler with kaffe [3], a publicly available JIT Java system, is under way. Section 2 presents the organization of Briki including a brief discussion of JavaIR