Skip lists: a probabilistic alternative to balanced trees
Communications of the ACM
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Experiments on Adaptive Set Intersections for Text Retrieval Systems
ALENEX '01 Revised Papers from the Third International Workshop on Algorithm Engineering and Experimentation
Improving the performance of list intersection
Proceedings of the VLDB Endowment
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Intersection of sorted inverted lists is an important operation in the web search engines. Various algorithms to improve the performance of this operation have been introduced in the literature [1, 3, 5]. Previous research works mainly focused on single-core or multi-core CPU platform and did not consider the query traffic problem arises in the actual systems. Modern graphics processing units (GPUs) give a new way to solve the problem. Wu et al. [6] presented a CPU-GPU cooperative model which can dynamically switch between the asynchronous mode and the synchronous mode. Under light query traffic, asynchronous mode is triggered, each newly arriving query is serviced by an independent thread. Under heavy query traffic, synchronous mode is triggered, all active threads are blocked and a single thread takes control of query processing. Queries are grouped into batches at CPU end, and each batch is processed by GPU threads in parallel. We summarize that putting the operations on GPU has two advantages: The massive on-chip parallelism of GPU may greatly reduce the processing time of lists intersection; A great part of work on CPU is offloaded to GPU. Overall the GPU will significantly increase throughput and reduce average response time in the synchronous mode. In this paper we consider techniques for improving the performance of the GPU batched algorithm proposed in [6] assuming sufficient queries at the CPU end.