9438

On-Demand Generating and Scheduling Optimised Parallel Applications on Heterogeneous Platforms

K. A. Hawick, D. P. Playne
Computer Science, Massey University, North Shore 102-904, Auckland, New Zealand
Technical Report CSTN-165, 2013
@inproceedings{CSTN-165,

   author={K. A. Hawick and D. P. Playne},

   title={On-Demand Generating and Scheduling Optimised Parallel Applications on Heterogeneous Platforms},

   booktitle={Proc. 12th Int. Conf. on Software Engineering Research and Practice (SERP’13)},

   year={2013},

   number={CSTN-165},

   address={Las Vegas, USA},

   month={22-25 July},

   organization={WorldComp},

   institution={Computer Science, Massey University},

   keywords={eScience; computational science; on-demand code generation; simulation; code reuse; GPUs; multi-core}

}

Download Download (PDF)   View View   Source Source   

595

views

Scheduling applications tasks across heterogeneous clusters is a growing problem, particularly when new upgraded components are added to a parallel computing system that may have originally been homogeneous. We describe how automatic and just-in-time source code generation techniques can be used to make the best parallel decomposition for whatever resource is available in a heterogeneous system consisting of graphical processing unit accelerators and multi-cored conventional CPUs. We show how a high level domain specific language approach to our set of target simulation applications can be used to cater for a variety of different GPU and CPU models and scheduling circumstances. We present some performance and resource utilisation data illustrating the scheduling issue for heterogeneous systems in computational science. We discuss the future outlook for this approach in eScience more generally.
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] => 1472360752
            [oauth_signature_method] => HMAC-SHA1
            [oauth_token] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
            [oauth_timestamp] => 1472360752
            [oauth_version] => 1.0
            [cursor] => -1
            [screen_name] => hgpu
            [skip_status] => true
            [include_user_entities] => false
            [oauth_signature] => Bq5tm1Fwy6AEoNPatrjO03R3LDE=
        )

    [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: