Major web search engines answer thousands of queries per second requesting information about billions of web pages. The data sizes and query loads are growing at an exponential rate. To manage the heavy workload, we consider techniques for utilizing a Graphics Processing Unit (GPU). We investigate new approaches to improve two important operations of search engines-lists intersection and index compression. For lists intersection, we develop techniques for efficient implementation of the binary search algorithm for parallel computation. We inspect some representative real-world datasets and find that a sufficiently long inverted list has an overall linear rate of increase. Based on this observation, we propose Linear Regression and Hash Segmentation techniques for contracting the search range. For index compression, the traditional d-gap based compression schemata are not well-suited for parallel computation, so we propose a Linear Regression Compression schema which has an inherent parallel structure. We further discuss how to efficiently intersect the compressed lists on a GPU. Our experimental results show significant improvements in the query processing throughput on several datasets.

Intersection of inverted lists is a frequently used operation in search engine systems. Efficient CPU and GPU intersection algorithms for large problem size are well studied. We propose an efficient GPU algorithm for high performance intersection of inverted index lists on CUDA platform. This algorithm feeds queries to GPU in batches, thus can take full advantage of GPU processor cores even if problem size is small. We also propose an input preprocessing method which alleviate load imbalance effectively. Our experimental results based on a real world test set show that the batched algorithm is much faster than the fastest CPU algorithm and plain GPU algorithm.

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