z-logo
open-access-imgOpen Access
Selecting Multiple Order Statistics with a Graphics Processing Unit
Author(s) -
Jeffrey D. Blanchard,
Erik Opavsky,
Emircan Uysaler
Publication year - 2016
Publication title -
acm transactions on parallel computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.236
H-Index - 16
eISSN - 2329-4957
pISSN - 2329-4949
DOI - 10.1145/2948974
Subject(s) - computer science , sorting , sort , sorting algorithm , order statistic , merge (version control) , set (abstract data type) , merge algorithm , graphics processing unit , merge sort , computation , data set , graphics , algorithm , statistics , mathematics , parallel computing , artificial intelligence , computer graphics (images) , information retrieval , programming language
Extracting a set of multiple order statistics from a huge data set provides important information about the distribution of the values in the full set of data. This article introduces an algorithm, bucketMultiSelect, for simultaneously selecting multiple order statistics with a graphics processing unit (GPU). Typically, when a large set of order statistics is desired, the vector is sorted. When the sorted version of the vector is not needed, bucketMultiSelect significantly reduces computation time by eliminating a large portion of the unnecessary operations involved in sorting. For large vectors, bucketMultiSelect returns thousands of order statistics in less time than sorting the vector while typically using less memory. For vectors containing 228 values of type double, bucketMultiSelect selects the 101 percentile order statistics in less than 95ms and is more than 8× faster than sorting the vector with a GPU optimized merge sort.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom