Abstract

Abstract
Statistical relational learning (SRL) constructs probabilistic models from relational databases. A key capability of SRL is the learning of arcs (in the Bayes net sense) connecting entries in different rows of a relational table, or in different tables. Nevertheless, SRL approaches currently are constrained to use the existing database schema. For many database applications, users find it profitable to define alternative "views" of the database, in effect defining new fields or tables. Such new fields or tables can also be highly useful in learning. We provide SRL with the capability of learning new views.

Abstract
Andorra-I is an experimental parallel Prolog system that transparently exploits both dependent and-parallelism and or-parallelism. It constitutes the first implementation of the Basic Andorra model, a parallel execution model for logic programs in which determinate goals are executed before other goals. This model, besides combining two of the most important forms of implicit parallelism in logic programs, also provides a form of implicit coroutining. This means that Andorra-I not only supports standard Prolog but also provides the capabilities of flat committed-choice languages. In this paper, we discuss the preprocessor which enables Andorra-I to support full Prolog, including cut, commit, meta-predicates and side-effect predicates. The main functions of the preprocessor are to perform a mode analysis of the program by abstract interpretation, to use this information to recognize procedures containing pruning operators that are potentially ``noisy'', to protect calls to such ``sensitive'' predicates by restricting certain conjunctions to be executed sequentially, and finally to generate for each predicate a routine which will determine when calls to that predicate become executable through becoming clause determinate.

Abstract
Andorra-I is an experimental parallel Prolog system that transparently exploits both dependent and-parallelism and or-parallelism. It constitutes the first implementation of the Basic Andorra model, a parallel execution model for logic programs in which determinate goals are executed before other goals. This model, besides combining two of the most important forms of implicit parallelism in logic programs, also provides a form of implicit coroutining. This means that Andorra-I not only supports standard Prolog but also provides the capabilities of flat committed-choice languages. In this paper, we discuss the main issues involved in the implementation of the Andorra-I engine, covering both the sequential version which runs on uniprocessors and the parallel version which runs on shared-memory multiprocessors such as Sequent Symmetry. We then present performance data for our implementation. This data shows that Andorra-I, an interpreter, has a single-processor performance similar to the comparable sequential system, C-Prolog, while on multiple processors Andorra-I is able to obtain good speedups from both and-parallelism and or-parallelism. In suitable cases, the speedup obtained from exploiting both forms of parallelism combined is better than that obtainable from exploiting either kind alone.

Abstract

Abstract
Modern Java Compilers, such as Sun's HotSpot compilers, implement a number of optimizations, ranging from high-level program transformations to low-level architecure dependent operations such as instruction scheduling. In a Just-in-Time (JIT) environment, the impact of each optimization must be weighed against its cost in terms of total runtime. Towards better understanding the usefulness of individual optimizations, we study the main optimizations available on Sun HotSpot compilers for a wide range of scientific and non-scientific benchmarks, weighing their cost and benefits in total runtime. We chose the HotSpot technology because it is state of the art and its source code is available. © J.UCS.