We describe a framework for modeling optimization problems for solution on a grid computer. The framework is easy to adapt to multiple grid engines, and can seamlessly integrate evolving mechanisms from particular computing platforms. It facilitates the widely used master/worker model of computing and is shown to be exible and powerful enough for a large variety of optimization applications. In particular, we summarize a number of new features of the GAMS modeling system that provide a lightweight, portable and powerful framework for optimization on a grid. We provide downloadable examples of its use for embarrasingly parallel nancial applications, decomposition and iterative algorithms and for solving very di cult mixed integer programs to optimality. Computational results are provided for a number of di erent grid engines, including multi-core machines, a pool of machines controlled by the Condor resource manager and the grid engine from Sun Microsystems. 1

We describe a framework for modeling optimization problems for solution on a grid computer. The framework is easy to adapt to multiple grid engines, and can seamlessly integrate evolving mechanisms from particular computing platforms. It facilitates the widely used master/worker model of computing and is shown to be flexible and powerful enough for a large variety of optimization applications. In particular, we summarize a number of new features of the GAMS modeling system that provide a lightweight, portable and powerful framework for optimization on a grid. We provide downloadable examples of its use for embarrasingly parallel financial applications, decomposition of complementarity problems, and for solving very difficult mixed integer programs to optimality. Computational results are provided for a number of different grid engines, including multi-core machines, a pool of machines controlled by the Condor resource manager and the grid engine from Sun Microsystems.

In this report, we propose new concurrent data structures and load balancing strategies for Branch-and-Bound (B&B)/A* algorithms in two models of parallel programming : shared and distributed memory. For the shared memory model (SMM), we present a general methodology which allows concurrent manipulations for most tree data structures, and show its usefulness for implementation on multiprocessors with global shared memory. Some priority queues which are suited for basic operations performed by B&B algorithms are described : the Skew-heaps, the funnels and the Splay-trees. We also detail a specific data structure, called treap and designed for A* algorithm. These data structures are implemented on a parallel machine with shared memory : KSR1. For the distributed memory model (DMM), we show that the use of partial cost in the B&B algorithms is not enough to balance nodes between the local queues. Thus, we introduce another notion of priority, called potentiality, between nodes that take...

Abstract. A * (A-star) is a heuristic search algorithm used in various domains, such as robotics, digital games, DNA alignment, among others. In spite of its large use, A * can be a computationally expensive depending on the characteristics of the state space and heuristics used. Aiming at improving its performance, in this paper we propose a parallel implementation of a bidirectional version of A*. Named PNBA * (Parallel New Bidirectional A*) the proposed algorithm combines the benefits of bidirectional search and parallel execution in the development of an efficient A * based search algorithm. Experiments performed in different domains show that PNBA * is more efficient than the original A * and NBA*, the bidirectional version of A * it is based on. 1.