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

We propose two new approaches that automatically parallelize Java programs at runtime. These approaches, which rely on run-time trace information collected during program execution, dynamically recompile Java byte code that can be executed in parallel. One approach utilizes trace information to improve traditional loop parallelization, and the other parallelizes traces instead of loop iterations. We also describe a cost/benefit model that makes intelligent parallelization decisions, as well as a parallel execution environment to execute parallelized programs. These techniques are based on Jikes RVM. Our approach is evaluated by parallelizing sequential Java programs, and its performance is compared to that of the manually parallelized code. According to the experimental results, our approach has low overheads and achieves competitive speedups compared to the manually parallelizing code. Moreover, trace parallelization can exploit parallel-ism beyond loop iterations