8703

Task-based Conjugate-Gradient for multi-GPUs platforms

Emmanuel Agullo, Luc Giraud, Abdou Guermouche, Stojce Nakov, Jean Roman
Universite Sciences et Technologies – Bordeaux I
hal-00767368, (19 December 2012)
@article{agullo2012task,

   title={Task-based Conjugate-Gradient for multi-GPUs platforms},

   author={Agullo, Emmanuel, Giraud, Luc and Guermouche, Abdou and Nakov, Stojce and Roman, Jean},

   year={2012}

}

Download Download (PDF)   View View   Source Source   

607

views

Whereas most today parallel High Performance Computing (HPC) software is written as highly tuned code taking care of low-level details, the advent of the manycore area forces the community to consider modular programming paradigms and delegate part of the work to a third party software. That latter approach has been shown to be very productive and efficient with regular algorithms, such as dense linear algebra solvers. In this paper we show that such a model can be efficiently applied to a much more irregular and less compute intensive algorithm. We illustrate our discussion with the standard unpreconditioned Conjugate Gradient (CG) that we carefully express as a task-based algorithm. We use the StarPU runtime system to assess the efficiency of the approach on a computational platform consisting of three NVIDIA Fermi GPUs. We show that almost optimum speed up (up to 2.89) may be reached (relatively to a mono-GPU execution) when processing large matrices and that the performance is portable when changing the low-level memory transfer mechanism.
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] => 1475082383
            [oauth_signature_method] => HMAC-SHA1
            [oauth_token] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
            [oauth_timestamp] => 1475082383
            [oauth_version] => 1.0
            [cursor] => -1
            [screen_name] => hgpu
            [skip_status] => true
            [include_user_entities] => false
            [oauth_signature] => e9hFyD6Ul6SIV026CoceSKm/8qc=
        )

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

HGPU group

2001 peoples are following HGPU @twitter

HGPU group © 2010-2016 hgpu.org

All rights belong to the respective authors

Contact us: