N-Body Simulation Using GP-GPU: Evaluating Host/Device Memory Transference Overhead

Sergio M. Martin, Fernando G. Tinetti, Nicanor B. Casas, Graciela E. De Luca, Daniel A. Giulianelli
Universidad Nacional de La Matanza, Florencio Varela 1903 – San Justo, Argentina
XIX Congreso Argentino de Ciencia de la Computacion (CACIC 2013), 2013

   title={N-Body Simulation Using GP-GPU: Evaluating Host/Device Memory Transference Overhead},

   author={Martin, Sergio M and Tinetti, Fernando G and Casas, Nicanor B and De Luca, Graciela E and Giulianelli, Daniel A},



Download Download (PDF)   View View   Source Source   



N-Body simulation algorithms are amongst the most commonly used within the field of scientific computing. Especially in computational astrophysics, they are used to simulate gravitational scenarios for solar systems or galactic collisions. Parallel versions of such N-Body algorithms have been extensively designed and optimized for multicore and distributed computing schemes. However, N-Body algorithms are still a novelty in the field of GP-GPU computing. Although several N-body algorithms have been proved to harness the potential of a modern GPU processor, there are additional complexities that this architecture presents that could be analyzed for possible optimizations. In this article, we introduce the problem of host to device (GPU) – and vice versa – data transferring overhead and analyze a way to estimate its impact in the performance of simulations.
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] => 1477621979
            [oauth_signature_method] => HMAC-SHA1
            [oauth_token] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
            [oauth_timestamp] => 1477621979
            [oauth_version] => 1.0
            [cursor] => -1
            [screen_name] => hgpu
            [skip_status] => true
            [include_user_entities] => false
            [oauth_signature] => aXfr4aroc/8+nblb/+qFWUrPc4M=

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

HGPU group

2036 peoples are following HGPU @twitter

HGPU group © 2010-2016 hgpu.org

All rights belong to the respective authors

Contact us: