Welcome to the InfoSci Platform
CUDA or OpenCL: Which is Better? A Detailed Performance Analysis

CUDA or OpenCL: Which is Better? A Detailed Performance Analysis

Mayank Bhura, Pranav H. Deshpande, K. Chandrasekaran
Copyright: © 2016 |Pages: 13
ISBN13: 9781466687370|ISBN10: 1466687371|EISBN13: 9781466687387
DOI: 10.4018/978-1-4666-8737-0.ch015
Cite Chapter Cite Chapter

MLA

Bhura, Mayank, et al. "CUDA or OpenCL: Which is Better? A Detailed Performance Analysis." Research Advances in the Integration of Big Data and Smart Computing, edited by Pradeep Kumar Mallick, IGI Global, 2016, pp. 267-279. https://doi.org/10.4018/978-1-4666-8737-0.ch015

APA

Bhura, M., Deshpande, P. H., & Chandrasekaran, K. (2016). CUDA or OpenCL: Which is Better? A Detailed Performance Analysis. In P. Mallick (Ed.), Research Advances in the Integration of Big Data and Smart Computing (pp. 267-279). IGI Global. https://doi.org/10.4018/978-1-4666-8737-0.ch015

Chicago

Bhura, Mayank, Pranav H. Deshpande, and K. Chandrasekaran. "CUDA or OpenCL: Which is Better? A Detailed Performance Analysis." In Research Advances in the Integration of Big Data and Smart Computing, edited by Pradeep Kumar Mallick, 267-279. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-8737-0.ch015

Export Reference

Mendeley
Favorite

Abstract

Usage of General Purpose Graphics Processing Units (GPGPUs) in high-performance computing is increasing as heterogeneous systems continue to become dominant. CUDA had been the programming environment for nearly all such NVIDIA GPU based GPGPU applications. Still, the framework runs only on NVIDIA GPUs, for other frameworks it requires reimplementation to utilize additional computing devices that are available. OpenCL provides a vendor-neutral and open programming environment, with many implementations available on CPUs, GPUs, and other types of accelerators, OpenCL can thus be regarded as write once, run anywhere framework. Despite this, both frameworks have their own pros and cons. This chapter presents a comparison of the performance of CUDA and OpenCL frameworks, using an algorithm to find the sum of all possible triple products on a list of integers, implemented on GPUs.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.