Dealing With Big Data Outside Of The Cloud: GPU Accelerated Sort

The demands placed on systems to analyse corpus data increase with input size, and the traditional approaches to processing this data are increasingly having impractical run-times. We show that the use of desktop GPUs presents a significant opportunity to accelerate a number of stages in the normal corpus analysis pipeline. This paper contains our exploratory work and findings into applying high-performance computing technology and methods to the problem of sorting large numbers of concordance lines.

[1]  Paul Rayson,et al.  Experiences with Parallelisation of an Existing NLP Pipeline: Tagging Hansard , 2014, LREC.

[2]  Yangdong Deng,et al.  IP routing processing with graphic processors , 2010, 2010 Design, Automation & Test in Europe Conference & Exhibition (DATE 2010).

[3]  Philippe Gaborit,et al.  High-Speed Private Information Retrieval Computation on GPU , 2008, 2008 Second International Conference on Emerging Security Information, Systems and Technologies.

[4]  Wessam Hassanein,et al.  Analyzing and enhancing the parallel sort operation on multithreaded architectures , 2010, The Journal of Supercomputing.

[5]  Margo McCall,et al.  IEEE Computer Society , 2019, Encyclopedia of Software Engineering.

[6]  Philippas Tsigas,et al.  GPU-Quicksort: A practical Quicksort algorithm for graphics processors , 2010, JEAL.