16550

Scheduling of Linear Algebra Kernels on Multiple Heterogeneous Resources

Olivier Beaumont, Terry Cojean, Lionel Eyraud-Dubois, Abdou Guermouche, Suraj Kumar
Inria Bordeaux Sud-Ouest, LaBRI, UMR 5800, University of Bordeaux
hal-01361992, (September 9, 2016)
@inproceedings{beaumont2016scheduling,

   title={Scheduling of Linear Algebra Kernels on Multiple Heterogeneous Resources},

   author={Beaumont, Olivier and Cojean, Terry and Eyraud-Dubois, Lionel and Guermouche, Abdou and Kumar, Suraj},

   booktitle={International Conference on High Performance Computing, Data, and Analytics (HiPC)},

   year={2016}

}

Download Download (PDF)   View View   Source Source   

152

views

In this paper, we consider task-based dense linear algebra applications on a single heterogeneous node which contains regular CPU cores and a set of GPU devices. Efficient scheduling strategies are crucial in this context in order to achieve good and portable performance. HeteroPrio, a resource-centric dynamic scheduling strategy has been introduced in a previous work and evaluated for the special case of nodes with exactly two types of resources. However, this restriction can be limiting, for example on nodes with several types of accelerators, but not only this. Indeed, an interesting approach to increase resource usage is to group several CPU cores together, which allows to use intra-task parallelism. We propose a generalization of HeteroPrio to the case with several classes of heterogeneous workers. We provide extensive evaluation of this algorithm with Cholesky factorization, both through simulation and actual execution, compared with HEFT-based scheduling strategy, the state of the art dynamic scheduling strategy for heterogeneous systems. Experimental evaluation shows that our approach is efficient even for highly heterogeneous configurations and significantly outperforms HEFT-based strategy.
VN:F [1.9.22_1171]
Rating: 3.6/5 (5 votes cast)
Scheduling of Linear Algebra Kernels on Multiple Heterogeneous Resources, 3.6 out of 5 based on 5 ratings

* * *

* * *

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] => 1474978147
            [oauth_signature_method] => HMAC-SHA1
            [oauth_token] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
            [oauth_timestamp] => 1474978147
            [oauth_version] => 1.0
            [cursor] => -1
            [screen_name] => hgpu
            [skip_status] => true
            [include_user_entities] => false
            [oauth_signature] => RqRrhVPfwLtqKCnn435q2t7hynM=
        )

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

HGPU group

1998 peoples are following HGPU @twitter

HGPU group © 2010-2016 hgpu.org

All rights belong to the respective authors

Contact us: