9102

Geometric Algebra Computing Technology for Accelerated Processing Units

Patrick Charrier, Dietmar Hildenbrand
University of Technology Darmstadt
EmbeddedWorld conference, 2013

@article{charrier2013geometric,

   title={Geometric Algebra Computing Technology for Accelerated Processing Units},

   author={Charrier, Patrick and Hildenbrand, Dietmar},

   year={2013}

}

Download Download (PDF)   View View   Source Source   

789

views

Development on embedded devices, even on today’s hardware, limits us to a minimum of third party-library dependencies due to hardware memory and power restrictions. In setups requiring intense geometric operations on limited hardware, such as in robotics, this problem can often lead to a tedious reimplementation of matrix, vector, and quaternion operations. Furthermore, certain unnecessary floating point operations are hard to avoid, because C++-features like expression template libraries such as eigen [2] can possibly not be used, because of strict C enforcement. Memory accesses are often the most limiting factor in today’s applications due to high memory latency. Yet traditional programming techniques unfortunately steer into the wrong direction by not easing data-oriented programming, which is often cumbersome to implement in C or C++. Many of the restrictions above are in a similar form the case on modern heterogeneous architectures such as AMD’s embedded Accelerated Processing Units or in GPGPU written in OpenCL/CUDA. Our technology based on Geometric Algebra and a Domain Specific language called CLUCalc will especially excel under these conditions. The focus of this work is Gaalop Precompiler, a new technology combining the advanced processing power of Accelerated Processing Units (APU) with the geometric intuitiveness of a new mathematical concept named Geometric Algebra [6]. The combination of both not only promises a more compact and maintainable code for graphics, vision, robotics and other scientific and engineering applications, but also automatically exploits parallelism on GPU or combined computing unit (APU) through OpenCL [8] or CUDA [9]. C/C++ CPU targeting is also supported. It is presented in the following, after a short introduction on Geometric Algebra.
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] => 1481155756
            [oauth_signature_method] => HMAC-SHA1
            [oauth_token] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
            [oauth_timestamp] => 1481155756
            [oauth_version] => 1.0
            [cursor] => -1
            [screen_name] => hgpu
            [skip_status] => true
            [include_user_entities] => false
            [oauth_signature] => TGkB1FpH+eAolmYQPmYYSAIKo3I=
        )

    [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: