Parallel Logic Programming Systems on Scalable Architectures

Abstract

Parallel logic programming (PLP) systems are sophisticated examples of symbolic computing systems. PLP systems address problems such as allocating dynamic memory, scheduling irregular computations, and managing different types of implicit parallelism. Most PLP systems have been developed for bus-based architectures. However, the complexity of PLP systems and the large amount of data they process raise the question of whether logic programming systems can still achieve good performance on modern scalable architectures, such as distributed shared-memory (DSM) systems. In this work we use execution-driven simulation of a cache-coherent DSM architecture to investigate the performance of Andorra-I, a state-of-the-art PLP system, on a modern multiprocessor. The results of this simulation show that Andorra-I exhibits reasonable running time performance, but it does not scale well. Our detailed analysis of cache misses and their sources expose several opportunities for improvements in Andorra-I. Based on this analysis, we modify Andorra-I using a set of simple techniques that led to significantly better running time and scalability. These results suggest that Andorra-I can and should perform well on modern multiprocessors. Furthermore, as Andorra-I shares its main data structures with several PLP systems, we conclude that the methodology and techniques used in our work can greatly benefit these other PLP systems.