12158

Heterogeneous Computing for Solving System of the Linear Equations by the Conjugate Gradient Method

Eduard Bondarenko
Dept. of Applied Mathematics, Oles Honchar Dnipropetrovs’k National University, Dnipropetrovs’k, Ukraine
Dnipropetrovs’k National University, 2014
@article{bondarenko2014heterogeneous,

   title={Heterogeneous Computing for Solving System of the Linear Equations by the Conjugate Gradient Method},

   author={Bondarenko, Eduard},

   year={2014}

}

Download Download (PDF)   View View   Source Source   

489

views

The main purpose of this work is to show the advantages of using various approaches of heterogeneous programming. The results were received on the example of solving the system of the linear equations by the conjugate gradient method. High-level and low-level technologies (OpenACC and CUDA respectively) were used to accelerate computations on the GPU. The results of the work are clearly reflect benefits of using the low-level technology CUDA. In this work several types of the heterogeneous computing was considered. The main difference of each type is an amount of the data that are processed on the graphic accelerators and central processing units. To get a clearer comparative overview for the acceleration of the computations on the CPU the OpenMP technology was used. With the exception of using GPU as acceleration unit another way to increase performance is shown in this paper.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

Recent source codes

* * *

* * *

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] => 1472051348
            [oauth_signature_method] => HMAC-SHA1
            [oauth_token] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
            [oauth_timestamp] => 1472051348
            [oauth_version] => 1.0
            [cursor] => -1
            [screen_name] => hgpu
            [skip_status] => true
            [include_user_entities] => false
            [oauth_signature] => x9t3Icn3AaHAboQoV+6b1f9NtgA=
        )

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

HGPU group © 2010-2016 hgpu.org

All rights belong to the respective authors

Contact us: