## Large scale parallel state space search utilizing graphics processing units and solid state disks

Technischen Universitat Dortmund

Technischen Universitat Dortmund, 2012

@article{sulewski2012large,

title={Large scale parallel state space search utilizing graphics processing units and solid state disks},

author={Sulewski, D.},

year={2012}

}

The evolution of science is a double-track process composed of theoretical insights on the one hand and practical inventions on the other one. While in most cases new theoretical insights motivate hardware developers to produce systems following the theory, in some cases the shown hardware solutions force theoretical research to forecast the results to expect. Progress in computer science rely on two aspects, processing information and storing it. Improving one side without touching the other will evidently impose new problems without producing a real alternative solution to the problem. While decreasing the time to solve a challenge may provide a solution to long term problems it will fail in solving problems which require much storage. In contrast, increasing the available amount of space for information storage will definitively allow harder problems to be solved by offering enough time. This work studies two recent developments in the hardware to utilize them in the domain of graph searching. The trend to discontinue information storage on magnetic disks and use electronic media instead and the tendency to parallelize the computation to speed up information processing are analyzed. Storing information on rotating magnetic disk has become the standard way since a couple of years and has reached a point where the storage capacity can be seen as infinite due to the possibility of adding new drives instantly with low costs. However, while the possible storage capacity increases every year, the transferring speed does not. At the beginning of this work, solid state media appeared on the market, slowly suppressing hard disks in speed demanding applications. Today, when finishing this work solid state drives are replacing magnetic disks in mobile computing, and computing centers use them as caching media to increase information retrieving speed. The reason is the huge advantage in random access where the speed does not drop so significantly as with magnetic drives. While storing and retrieving huge amounts of information is one side of the medal, the other one is the processing speed. Here the trend from increasing the clock frequency of single processors stagnated in 2006 and the manufacturers started to combine multiple cores in one processor. While a CPU is a general purpose processor the manufacturers of graphics processing units (GPUs) encounter the challenge to perform the same computation for a large number of image points. Here, a parallelization offers huge advantages, so modern graphics cards have evolved to highly parallel computing instances with several hundreds of cores. The challenge is to utilize these processors in other domains than graphics processing. One of the vastly used tasks in computer science is search. Not only disciplines with an obvious search but also in software testing searching a graph is the crucial aspect. Strategies which enable to examine larger graphs, be it by reducing the number of considered nodes or by increasing the searching speed, have to be developed to battle the rising challenges. This work enhances searching in multiple scientific domains like explicit state Model Checking, Action Planning, Game Solving and Probabilistic Model Checking proposing strategies to find solutions for the search problems. Providing an universal search strategy which can be used in all environments to utilize solid state media and graphics processing units is not possible due to the heterogeneous aspects of the domains. Thus, this work presents a tool kit of strategies tied together in an universal three stage strategy. In the first stage the edges leaving a node are determined, in the second stage the algorithm follows the edges to generate nodes. The duplicate detection in stage three compares all newly generated nodes to existing once and avoids multiple expansions. For each stage at least two strategies are proposed and decision hints are given to simplify the selection of the proper strategy. After describing the strategies the kit is evaluated in four domains explaining the choice for the strategy, evaluating its outcome and giving future clues on the topic.

May 11, 2012 by hgpu

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