The impedance mismatch problem that occurs when relational data is being processed by object-oriented (OO) programs, also occurs when OO programs process RDF data, on the Semantic Web. The impedance mismatch problem stems from the inherent differences between RDF and the data model of OO languages. In this paper, we illustrate a solution to this problem. Essentially, we modify an OO language so that RDF individuals become first-class citizens in the language, and objects of the language become first-class citizens in RDF. Three important benefits that follow from this modification are: (1) it becomes natural to use the language as a persistent programming language, (2) the language supports implicit integration of data from multiple data sources, and (3) SPARQL queries and inference can be applied to objects during the run of a program. This demo presents such a modified programming language, namely Ruby on Semantic Web, which is an extension of the Ruby programming language. The demo includes a system, where users can run applications, written in Ruby on Semantic Web, over multiple data sources. In the demo we run code examples. The effects of the execution on the data sources and on the state of the objects in memory are presented visually, in real time.

Memory access has proven to be one of the bottlenecks in modern architectures. Improving memory locality and eliminating the amount of memory access can help release this bottleneck. We present a method for link-time profile-based optimization by reordering the global data of the program and modifying its code accordingly. The proposed optimization reorders the entire global data of the program, according to a representative execution rate of each instruction (or basic block) in the code. The data reordering is done in a way that enables the replacement of frequently-executed Load instructions, which reference the global data, with fast Add Immediate instructions. In addition, it tries to improve the global data locality and to reduce the total size of the global data area. The optimization was implemented into FDPR (Feedback Directed Program Restructuring), a post-link optimizer, which is part of the IBM AIX operating system for the IBM pSeries servers. Our results on SPECint2000 show a significant improvement of up to 11% (average 3%) in execution time, along with up to 97.9% (average 83%) reduction in memory references to the global variables via the global data access mechanism of the program.