Parallel waveform extraction algorithms for the Cherenkov Telescope Array Real-Time Analysis

Andrea Zoli, Andrea Bulgarelli, Adriano De Rosa, Alessio Aboudan, Valentina Fioretti, Giovanni De Cesare, Ramin Marx
INAF/IASF Bologna, Bologna, Italy
arXiv:1509.01953 [astro-ph.IM], (7 Sep 2015)

   title={Parallel waveform extraction algorithms for the Cherenkov Telescope Array Real-Time Analysis},

   author={Zoli, Andrea and Bulgarelli, Andrea and Rosa, Adriano De and Aboudan, Alessio and Fioretti, Valentina and Cesare, Giovanni De and Marx, Ramin and Consortium, for the CTA},






Download Download (PDF)   View View   Source Source   



The Cherenkov Telescope Array (CTA) is the next generation observatory for the study of very high-energy gamma rays from about 20 GeV up to 300 TeV. Thanks to the large effective area and field of view, the CTA observatory will be characterized by an unprecedented sensitivity to transient flaring gamma-ray phenomena compared to both current ground (e.g. MAGIC, VERITAS, H.E.S.S.) and space (e.g. Fermi) gamma-ray telescopes. In order to trigger the astrophysics community for follow-up observations, or being able to quickly respond to external science alerts, a fast analysis pipeline is crucial. This will be accomplished by means of a Real-Time Analysis (RTA) pipeline, a fast and automated science alert trigger system, becoming a key system of the CTA observatory. Among the CTA design key requirements to the RTA system, the most challenging is the generation of alerts within 30 seconds from the last acquired event, while obtaining a flux sensitivity not worse than the one of the final analysis by more than a factor of 3. A dedicated software and hardware architecture for the RTA pipeline must be designed and tested. We present comparison of OpenCL solutions using different kind of devices like CPUs, Graphical Processing Unit (GPU) and Field Programmable Array (FPGA) cards for the Real-Time data reduction of the Cherenkov Telescope Array (CTA) triggered data.
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] => 1477074171
            [oauth_signature_method] => HMAC-SHA1
            [oauth_token] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
            [oauth_timestamp] => 1477074171
            [oauth_version] => 1.0
            [cursor] => -1
            [screen_name] => hgpu
            [skip_status] => true
            [include_user_entities] => false
            [oauth_signature] => /VpIvgELxLcY1mRvAd+zvnzi1qI=

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

HGPU group © 2010-2016 hgpu.org

All rights belong to the respective authors

Contact us: