Date of Award
4-20-2012
Document Type
Thesis open access
Abstract
As hardware architectures shift towards more heterogeneous platforms with different vari- eties of multi- and many-core processors and graphics processing units (GPUs) by various manufacturers, programmers need a way to write simple and highly optimized code without worrying about the specifics of the underlying hardware. To meet this need, I have designed a virtual machine and bytecode around the goal of optimized execution on highly variable, heterogeneous hardware, instead of having goals such as small bytecodes as was the ob- jective of the Java R Virtual Machine. The approach used here is to combine elements of the Dalvik R virtual machine with concepts from the OpenCL R heterogeneous computing platform, along with an annotation system so that the results of complex compile time analysis can be available to the Just-In-Time compiler. The annotation format is flexible so that the set of annotations can be expanded as the field of heterogeneous computing continues to grow. An initial implementation of this virtual machine was written in the Scala programming language and makes use of the Java bindings for OpenCL to execute code segments on a GPU. The implementation consists of an assembler that converts an assembly version of the bytecode into its binary representation and an interpreter that runs programs from the assembled binary. Because the bytecode contains valuable optimization information, decisions can be made at runtime to choose how best to execute code segments. To demonstrate this concept, the interpreter uses this information to produce OpenCL ker- nel code for specified bytecode blocks and then builds and executes these kernels to improve performance. This hybrid interpreter/Just-In-Time compiler serves as an initial implemen- tation of a virtual machine that provides optimized code tailored to the available hardware on which the application is running.
Recommended Citation
Seitz, Kerry A., "The Design and Implementation of a Bytecode for Optimization on Heterogeneous Systems" (2012). Computer Science Honors Theses. 28.
https://digitalcommons.trinity.edu/compsci_honors/28
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.