16458

Exploring Task Parallelism for Heterogeneous Systems Using Multicore Task Management API

Suyang Zhu, Sunita Chandrasekaran, Peng Sun, Barbara Chapman, Marcus Winter, Tobias Schuele
Dept. of Computer Science, University of Houston
4th Workshop on Runtime and Operating Systems for the Many-core Era (ROME), 2016
@article{zhu2016exploring,

   title={Exploring Task Parallelism for Heterogeneous Systems Using Multicore Task Management API},

   author={Zhu, Suyang and Chandrasekaran, Sunita and Sun, Peng and Chapman, Barbara and Winter, Marcus and Schuele, Tobias},

   year={2016}

}

Download Download (PDF)   View View   Source Source   

424

views

Current trends in multicore platform design indicate that heterogeneous systems are here to stay. Such systems include processors with specialized accelerators supporting different instruction sets and different types of memory spaces among several other features. Unfortunately, these features increase the effort for programming and porting applications to different target platforms. To solve this problem, effective programming strategies that can exploit the rich feature set of such heterogeneous multicore architectures are required without leading to an increased learning curve for applying these strategies. In this paper, we explore the Multicore Task Management API (MTAPI), a standard for task-based parallel programming in embedded systems. MTAPI provides interfaces for decomposing a problem into a set of tasks while abstracting from the heterogeneous nature of the target platforms. This way, it allows to efficiently distribute work in systems consisting of different cores running at different speed. For evaluation purposes, we use an NVIDIA Jetson TK1 board (ARM + GPU) as our test bed. As applications, we employ codes from benchmark suites such as Rodinia and BOTS.
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] => 1480740714
            [oauth_signature_method] => HMAC-SHA1
            [oauth_token] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
            [oauth_timestamp] => 1480740714
            [oauth_version] => 1.0
            [cursor] => -1
            [screen_name] => hgpu
            [skip_status] => true
            [include_user_entities] => false
            [oauth_signature] => Gu2PFifnTGD6Ko3cqcVCdQwFOcQ=
        )

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

HGPU group

2080 peoples are following HGPU @twitter

HGPU group © 2010-2016 hgpu.org

All rights belong to the respective authors

Contact us: