Bin recycling strategy for improving the histogram precision on GPU
| Autor: | Miguel Cárdenas-Montes, Miguel A. Vega-Rodríguez, Juan José Rodríguez-Vázquez |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Theoretical computer science
Computer science Computation General Physics and Astronomy Context (language use) Function (mathematics) Correlation function (quantum field theory) computer.software_genre 01 natural sciences Bin Hardware and Architecture Histogram 0103 physical sciences Data mining General-purpose computing on graphics processing units 010306 general physics 010303 astronomy & astrophysics Implementation computer |
| Zdroj: | Computer Physics Communications. 204:55-63 |
| ISSN: | 0010-4655 |
| DOI: | 10.1016/j.cpc.2016.03.006 |
| Popis: | Histogram is an easily comprehensible way to present data and analyses. In the current scientific context with access to large volumes of data, the processing time for building histogram has dramatically increased. For this reason, parallel construction is necessary to alleviate the impact of the processing time in the analysis activities. In this scenario, GPU computing is becoming widely used for reducing until affordable levels the processing time of histogram construction. Associated to the increment of the processing time, the implementations are stressed on the bin-count accuracy. Accuracy aspects due to the particularities of the implementations are not usually taken into consideration when building histogram with very large data sets. In this work, a bin recycling strategy to create an accuracy-aware implementation for building histogram on GPU is presented. In order to evaluate the approach, this strategy was applied to the computation of the three-point angular correlation function, which is a relevant function in Cosmology for the study of the Large Scale Structure of Universe. As a consequence of the study a high-accuracy implementation for histogram construction on GPU is proposed. |
| Databáze: | OpenAIRE |
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