Efficient lists intersection by CPU-GPU cooperative computing

Lists intersection is an important operation in modern web search engines. Many prior studies have focused on the single-core or multi-core CPU platform or many-core GPU. In this paper, we propose a CPU-GPU cooperative model that can integrate the computing power of CPU and GPU to perform lists intersection more efficiently. In the so-called synchronous mode, queries are grouped into batches and processed by GPU for high throughput. We design a query-parallel GPU algorithm based on an element-thread mapping strategy for load balancing. In the traditional asynchronous model, queries are processed one-by-one by CPU or GPU to gain perfect response time. We design an online scheduling algorithm to determine whether CPU or GPU processes the query faster. Regression analysis on a huge number of experimental results concludes a regression formula as the scheduling metric. We perform exhaustive experiments on our new approaches. Experimental results on the TREC Gov and Baidu datasets show that our approaches can improve the performance of the lists intersection significantly.