HJ-OpenCL: Reducing the Gap Between the JVM and Accelerators

Abstract

Recently there has been increasing interest in supporting execution of Java Virtual Machine (JVM) applications on accelerator architectures, such as GPUs. Unfortunately, there is a large gap between the features of the JVM and those commonly supported by accelerators. Examples of important JVM features include exceptions, dynamic memory allocation, use of arbitrary composite objects, file I/O, and more. Recent work from our research group tackled the first feature in that list, JVM exception semantics[14]. This paper continues along that path by enabling the acceleration of JVM parallel regions that include object references and dynamic memory allocation. The contributions of this work include 1) serialization and deserialization of JVM objects using a format that is compatible with OpenCL accelerators, 2) advanced code generation techniques for converting JVM bytecode to OpenCL kernels when object references and dynamic memory allocation are used, 3) runtime techniques for supporting dynamic memory allocation on OpenCL accelerators, and 4) a novel redundant data movement elimination technique based on inter-parallel-region dataflow analysis using runtime bytecode inspection. Experimental results presented in this paper show performance improvements of up to 18.33× relative to parallel Java Streams for GPU-accelerated parallel regions, even when those regions include object references and dynamic memory allocation. In our evaluation, we fully characterize where accelerators or the JVM see performance wins and point out opportunities for future work.