9114

Deploying Graph Algorithms on GPUs: an Adaptive Solution

Da Li, Michela Becchi
Dept. of Electrical and Computer Engineering, University of Missouri, Columbia
27th IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2013

@article{li2013deploying,

   title={Deploying Graph Algorithms on GPUs: an Adaptive Solution},

   author={Li, Da and Becchi, Michela},

   year={2013}

}

Download Download (PDF)   View View   Source Source   

1229

views

Thanks to their massive computational power and their SIMT computational model, Graphics Processing Units (GPUs) have been successfully used to accelerate a wide variety of regular applications (linear algebra, stencil computations, image processing and bioinformatics algorithms, among others). However, many established and emerging problems are based on irregular data structures, such as graphs. Examples can be drawn from different application domains: networking, social networking, machine learning, electrical circuit modeling, discrete event simulation, compilers, and computational sciences. It has been shown that irregular applications based on large graphs do exhibit runtime parallelism; moreover, the amount of available parallelism tends to increase with the size of the datasets. In this work, we explore an implementation space for deploying a variety of graph algorithms on GPUs. We show that the dynamic nature of the parallelism that can be extracted from graph algorithms makes it impossible to find an optimal solution. We propose a runtime system able to dynamically transition between different implementations with minimal overhead, and investigate heuristic decisions applicable across algorithms and datasets. Our evaluation is performed on two graph algorithms: breadth-first search and single-source shortest paths. We believe that our proposed mechanisms can be extended and applied to other graph algorithms that exhibit similar computational patterns.
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] => 1481290141
            [oauth_signature_method] => HMAC-SHA1
            [oauth_token] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
            [oauth_timestamp] => 1481290141
            [oauth_version] => 1.0
            [cursor] => -1
            [screen_name] => hgpu
            [skip_status] => true
            [include_user_entities] => false
            [oauth_signature] => wgTjXjKHWidqv2ShwlTpzKSDJ3M=
        )

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

HGPU group

2081 peoples are following HGPU @twitter

HGPU group © 2010-2016 hgpu.org

All rights belong to the respective authors

Contact us: