On-Line Trace Based Automatic Parallelization of Java Programs on Multicore Platforms

We propose a new approach that automatically parallelizes Java programs at runtime. The approach collects on-line trace information during program execution, and dynamically recompiles methods that can be executed in parallel. Wealso describe a cost/benefit model that makes intelligent parallelization decisions, as well as a parallel execution environment to execute parallelized code. We implement these techniques upon Jikes RVM and evaluate our approach by parallelizing sequential benchmarks and comparing the performance to manually parallelized version of those benchmarks. According to the experimental results, our approach has low overheads and achieves competitive speed-ups compared to manually parallelized code.