Automatic Program Parallelization Using Traces

• Main Author: Bradel, Borys
• Other Authors: Abdelrahman, Tarek S.
• Language: en_ca
• Published: 2010
• Subjects: automatic parallelization; traces; 0544
• Online Access: http://hdl.handle.net/1807/26502

We present a novel automatic parallelization approach that uses traces. Our approach uses a binary representation of a program, allowing for the parallelization of programs even if their full source code is not available. Furthermore, traces can represent both iteration and recursion. We use hardware transactional memory (HTM) to ensure correct execution in the presence of dependences. We describe a parallel trace execution model that allows sequential programs to execute in parallel. In the model, traces are identified by a trace collection system (TCS), the program is transformed to allow the traces to execute on multiple processors, and the traces are executed in parallel. We present a framework with four components that, with a TCS, realizes our execution model. The grouping component groups traces into tasks to reduce overhead and make identification of successor traces easier. The packaging component allows tasks to execute on multiple processors. The dependence component deals with dependences on reduction and induction variables. In addition, transactions are committed in sequential program order on an HTM system to deal with dependences that are not removed. Finally, the scheduler assigns tasks to processors. We create a prototype that parallelizes programs and uses an HTM simulator to deal with dependences. To overcome the limitations of simulation, we also create another prototype that automatically parallelizes programs on a real system. Since HTM is not used, only dependences on induction and reduction variables are handled. We demonstrate the feasibility of our trace-based parallelization approach by performing an experimental evaluation on several recursive and loop-based Java programs. On the HTM system, the average speedup of the computational phase of the benchmarks on four processors is 2.79. On a real system, the average speedup on four processors is 1.83. Therefore, the evaluation indicates that trace-based parallelization can be used to effectively parallelize recursive and loop-based Java programs based on their binary representation.