9837

Comparison and Analysis of GPU Energy Efficiency For CUDA and OpenCL

Joe Jackson
Roanoke College
Roanoke College, 2013
@article{jackson2013comparison,

   title={Comparison and Analysis of GPU Energy Efficiency For CUDA and OpenCL},

   author={Jackson, Joe},

   year={2013}

}

Download Download (PDF)   View View   Source Source   

705

views

The use of GPUs for processing large sets of parallelizable data has increased sharply in recent years. As the concept of GPU computing is still relatively young, parameters other than computation time, such as energy efficiency, are being overlooked. Two parallel computing platforms, CUDA and OpenCL, provide developers with an interface that they can use to work directly with GPUs. CUDA is designed specifically for NVIDA GPUs, while OpenCL can be used with any GPUs, as well as CPUs and FPGAs. In this paper, we analyze the energy efficiency of the two platforms using large matrix multiplication applications as our basis of comparison. We found that CUDA expends less energy over a shorter time than OpenCL when given the same computational workload.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

TwitterAPIExchange Object
(
    [oauth_access_token:TwitterAPIExchange:private] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
    [oauth_access_token_secret:TwitterAPIExchange:private] => o29ji3VLVmB6jASMqY8G7QZDCrdFmoTvCDNNUlb7s
    [consumer_key:TwitterAPIExchange:private] => TdQb63pho0ak9VevwMWpEgXAE
    [consumer_secret:TwitterAPIExchange:private] => Uq4rWz7nUnH1y6ab6uQ9xMk0KLcDrmckneEMdlq6G5E0jlQCFx
    [postfields:TwitterAPIExchange:private] => 
    [getfield:TwitterAPIExchange:private] => ?cursor=-1&screen_name=hgpu&skip_status=true&include_user_entities=false
    [oauth:protected] => Array
        (
            [oauth_consumer_key] => TdQb63pho0ak9VevwMWpEgXAE
            [oauth_nonce] => 1472373173
            [oauth_signature_method] => HMAC-SHA1
            [oauth_token] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
            [oauth_timestamp] => 1472373173
            [oauth_version] => 1.0
            [cursor] => -1
            [screen_name] => hgpu
            [skip_status] => true
            [include_user_entities] => false
            [oauth_signature] => MEhD87MCpbbCqpAxjkBUKFw/BiA=
        )

    [url] => https://api.twitter.com/1.1/users/show.json
)
Follow us on Facebook
Follow us on Twitter

HGPU group

1969 peoples are following HGPU @twitter

HGPU group © 2010-2016 hgpu.org

All rights belong to the respective authors

Contact us: