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 work-load, 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 [9, 8, 12]. 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-κ 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 shows promising gains in query throughput. Copyright is held by the International World Wide Web Conference Committee (IW3C2).