Automatic Parallelization for Graphics Processing Units in JikesRVM

Abstract Accelerated graphics cards, including specialized high-performance processors called Graphics Processing Units (GPUs), have become ubiquitous in recent years. On the right kinds of problems, GPUs greatly surpass CPUs in terms of raw performance. However, GPUs are currently used only for a narrow class of special-purpose appli- cations; the raw processing power available in a typical desktop PC is unused most of the time. The goal of this work is to present an extension to JikesRVM that automatically executes suitable code on the GPU instead of the CPU. Both static and dynamic features are used to decide whether it is feasible and benecial,to o-load,a piece of code to the GPU. Feasible code is discovered by an implementation of data dependence analysis. A cost model that balances the speedup available from the GPU against the cost of transferring input and output data between main memory and GPU memory,has been deployed to determine if a feasible parallelization is indeed benecial.,The cost model is parameterized so that it can be applied to dierent,hardware combinations. We also present ways to overcome several obstacles to parallelization inherent in the design of the Java bytecode language: unstructured control,ow, the lack of