cudaMap: a GPU accelerated program for gene expression connectivity mapping

Darragh G McArt, Peter Bankhead, Philip D Dunne, Manuel Salto-Tellez, Peter Hamilton, Shu-Dong Zhang
Centre for Cancer Research and Cell Biology (CCRCB), Queen’s University, Belfast (QUB), Belfast, Northern Ireland, UK
BMC Bioinformatics, 14:305, 2013


   title={cudaMap: a GPU accelerated program for gene expression connectivity mapping},

   author={McArt, Darragh G and Bankhead, Peter and Dunne, Philip D and Salto-Tellez, Manuel and Hamilton, Peter and Zhang, Shu-Dong},

   journal={BMC bioinformatics},





   publisher={BioMed Central Ltd}


Download Download (PDF)   View View   Source Source   Source codes Source codes




BACKGROUND: Modern cancer research often involves large datasets and the use of sophisticated statistical techniques. Together these add a heavy computational load to the analysis, which is often coupled with issues surrounding data accessibility. Connectivity mapping is an advanced bioinformatic and computational technique dedicated to therapeutics discovery and drug re-purposing around differential gene expression analysis. On a normal desktop PC, it is common for the connectivity mapping task with a single gene signature to take > 2h to complete using sscMap, a popular Java application that runs on standard CPUs (Central Processing Units). Here, we describe new software, cudaMap, which has been implemented using CUDA C/C++ to harness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce processing times for connectivity mapping. RESULTS: cudaMap can identify candidate therapeutics from the same signature in just over thirty seconds when using an NVIDIA Tesla C2050 GPU. Results from the analysis of multiple gene signatures, which would previously have taken several days, can now be obtained in as little as 10 minutes, greatly facilitating candidate therapeutics discovery with high throughput. We are able to demonstrate dramatic speed differentials between GPU assisted performance and CPU executions as the computational load increases for high accuracy evaluation of statistical significance. CONCLUSION: Emerging ‘omics’ technologies are constantly increasing the volume of data and information to be processed in all areas of biomedical research. Embracing the multicore functionality of GPUs represents a major avenue of local accelerated computing. cudaMap will make a strong contribution in the discovery of candidate therapeutics by enabling speedy execution of heavy duty connectivity mapping tasks, which are increasingly required in modern cancer research. cudaMap is open source and can be freely downloaded from http://purl.oclc.org/NET/cudaMap.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: