We present a framework for a large-scale distributed eScience Articial Intelligence search. Our approach is generic and can be used for many dierent problems. Unlike many other approaches, we do not require dedicated machines, homogeneous infrastructure or the ability to communicate between nodes. We give special consideration to the robustness of the framework, minimising the loss of eort even after total loss of infrastructure, and allowing easy verication of every step of the distribution process. In contrast to most eScience applications, the input data and specication of the problem is very small, being easily given in a paragraph of text. The unique challenges our framework tackles are related to the combinatorial explosion of the space that contains the possible solutions and the robustness of long-running computations. Not only is the time required to nish the computations unknown, but also the resource requirements may change during the course of the computation. We demonstrate the applicability of our framework by using it to solve a challenging and hitherto open problem in computational mathematics. The results demonstrate that our approach easily scales to computations of a size that would have been impossible to tackle in practice just a decade ago.
The availability of commodity multi-core andmulti-processor machines and the inherent parallelism inconstraint programming search offer significant opportunities for constraint programming. They also present a fundamental challenge: how to exploit parallelism transparently to speed up constraint programs. This paper shows how to parallelize constraint programs transparently without changes to the code. The main technical idea consists of automatically lifting a sequential exploration strategy into its parallel counterpart, allowing workers to share and steal subproblems. Experimental results showthat the parallel implementationmay produces significant speedups on multi-core machines. Search in constraint programming is a time consuming task. Search can be speeded up by exploring subtrees of a search tree in parallel. This paper presents distributed search engines that achieve parallelism by distribution across networked computers. The main point of the paper is a simple design of the parallel search engine. Simplicity comes as an immediate consequence of clearly separating search, concurrency, and distribution. The obtained distributed search engines are simple yet offer substantial speedup on standard network computers.