document: Using graphics processors for high performance IR query processing Web search engines are facing formidable performance challenges due to data sizes and query loads . The major engines have to process tens of thousands of queries per second over tens of billions of documents . To deal with this heavy workload , such engines employ massively parallel systems consisting of thousands of machines . The significant cost of operating these systems has motivated a lot of recent research into more efficient query processing mechanisms . We investigate a new way to build such high performance IR systems using 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 such as databases and scientific computing . Our contribution here is to design a basic system architecture for GPU-based high-performance IR , to develop suitable algorithms for subtasks such as inverted list compression , list intersection , and top - $ k $ scoring , and to show how to achieve highly efficient query processing on GPU-based systems . Our experimental results for a prototype GPU-based system on $ 25.2 $ million web pages indicate that significant gains in query processing performance can be obtained .
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