Fast computation of database operations using graphics processors
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Super-Scalar RAM-CPU Cache Compression
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Inverted files for text search engines
ACM Computing Surveys (CSUR)
GPUTeraSort: high performance graphics co-processor sorting for large database management
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Performance of compressed inverted list caching in search engines
Proceedings of the 17th international conference on World Wide Web
On efficient posting list intersection with multicore processors
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
GPU-WAH: applying GPUs to compressing bitmap indexes with word aligned hybrid
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Advances in Engineering Software
Efficient compressed inverted index skipping for disjunctive text-queries
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Improving on-demand learning to rank through parallelism
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
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Web search engines are facing formidable performance challenges as they need to process thousands of queries per second over billions of documents. To deal with this heavy workload, current engines use massively parallel architectures of thousands of machines that require large hardware investments. We investigate new ways to build such high-performance IR systems based on Graphical Processing Units (GPUs). GPUs were originally designed to accelerate computer graphics applications through massive on-chip parallelism. Recently a number of researchers have studied how to use GPUs for other problem domains including databases and scientific computing [2,3,5], but we are not aware of previous attempts to use GPUs for large-scale web search. Our contribution here is to design a basic system architecture for GPU-based high-performance IR, and to describe how to perform highly efficient query processing within such an architecture. Preliminary experimental results based on a prototype implementation suggest that significant gains in query processing performance might be obtainable with such an approach.