Lattice Boltzmann method (LBM) is adopted to compute two and three-dimensional lid driven cavity flows to examine the influence of memory management on the computational performance using Graphics Processing Unit (GPU). Both single and multi-relaxation time LBM are adopted. The computations are conducted on nVIDIA GeForce Titan, Tesla C2050 and GeForce GTX 560Ti. The performance using global memory deteriorates greatly when multi relaxation time (MRT) LBM is used, which is due to the scheme requesting more information from the global memory than its single relaxation time (SRT) LBM counterpart. On the other hand, adopting on chip memory the difference using MRT and SRT is not significant. Also, performance of LBM streaming procedure using offset reading surpasses offset writing ranging from 50% to 100% and this applies to both SRT and MRT LBM. Finally, comparisons using different GPU platforms indicate that Titan as expected outperforms other devices, and attains 227 and 193 speedup over its Intel Core i7-990 CPU counterpart and four times faster than GTX 560Ti and Tesla C2050 for three dimensional cavity flow simulations respectively with single and double precisions.

SICStus Prolog has evolved for nearly 25 years. This is an appropriate point in time for revisiting the main language and design decisions, and try to distill some lessons. SICStus Prolog was conceived in a context of multiple, conflicting Prolog dialect camps and a fledgling standardization effort. We reflect on the impact of this effort and role model implementations on our development. After summarizing the development history, we give a guided tour of the system anatomy, exposing some designs that were not published before. We give an overview of our new interactive development environment, and describe a sample of key applications. Finally, we try to identify key good and not so good design decisions.

Representation sharing can reduce the memory footprint of a program by sharing one representation between duplicate terms. The most common implementation of representation sharing in functional programming systems is known as hash-consing. In the context of Prolog, representation sharing has been given little attention. Some current techniques that deal with representation sharing are reviewed. The new contributions are (1) an easy implementation of input sharing for findall/3; and (2) a description of a sharer module that introduces representation sharing at runtime. Their realization is shown in the context of the Warren Abstract Machine (WAM) as implemented by hProlog. Both can be adapted to any WAM-like Prolog implementation. The sharer works independently of the garbage collector, but it can be made to cooperate with the garbage collector. Benchmark results show that the sharer has a cost comparable to the heap garbage collector, that its effectiveness is highly application-dependent, and that its policy must be tuned to the collector.

Consider a two-level storage system operating with the least recently used (LRU) or the first-in, first-out (FIFO) replacement strategy. Accesses to the main storage are described by the independent reference model (IRM). Using the FKG inequality, we prove that the miss ratio for LRU is smaller than or equal to the miss ratio for FIFO.