Publikation

Massively Parallel Rule-Based Interpreter Execution on GPUs Using Thread Compaction

Marcel Köster, Julian Groß, Antonio Krüger

In: International Journal of Parallel Programming (IJPP) International Journal of Parallel Programming. 48 3 Seiten 1-17 Springer 6/2020.

Abstrakt

Interpreters are well researched in the field of compiler construction and program generation. They are typically used to realize program execution of different programming languages without a compilation step. However, they can also be used to model complex rule-based simulations: The interpreter applies all rules one after another. These can be iteratively applied on a globally updated state in order to get the final simulation result. Many simulations for domain-specific problems already leverage the parallel processing capabilities of Graphics Processing Units (GPUs). They use hardware-specific tuned rule implementations to achieve maximum performance. However, every interpreter-based system requires a high-level algorithm that detects active rules and determines when they are evaluated. A common approach in this context is the use of different interpreter routines for every problem domain. Executing such functions in an efficient way mainly involves dealing with hardware peculiarities like thread divergences, ALU computations and memory operations. Furthermore, the interpreter is often executed on multiple states in parallel these days. This is particularly important for heuristic search or what-if analyses, for instance. In this paper, we present a novel and easy-to-implement method based on thread compaction to realize generic rule-based interpreters in an efficient way on GPUs. It is optimized for many states using a specially designed memory layout. Benchmarks on our evaluation scenarios show that the performance can be significantly increased in comparison to existing commonly-used implementations.

Projekte

Weitere Links

paper_koester_mpie_2020.pdf (pdf, 705 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence