1,876 research outputs found
Astrophysical Supercomputing with GPUs: Critical Decisions for Early Adopters
General purpose computing on graphics processing units (GPGPU) is
dramatically changing the landscape of high performance computing in astronomy.
In this paper, we identify and investigate several key decision areas, with a
goal of simplyfing the early adoption of GPGPU in astronomy. We consider the
merits of OpenCL as an open standard in order to reduce risks associated with
coding in a native, vendor-specific programming environment, and present a GPU
programming philosophy based on using brute force solutions. We assert that
effective use of new GPU-based supercomputing facilities will require a change
in approach from astronomers. This will likely include improved programming
training, an increased need for software development best-practice through the
use of profiling and related optimisation tools, and a greater reliance on
third-party code libraries. As with any new technology, those willing to take
the risks, and make the investment of time and effort to become early adopters
of GPGPU in astronomy, stand to reap great benefits.Comment: 13 pages, 5 figures, accepted for publication in PAS
Genetic Algorithm Modeling with GPU Parallel Computing Technology
We present a multi-purpose genetic algorithm, designed and implemented with
GPGPU / CUDA parallel computing technology. The model was derived from a
multi-core CPU serial implementation, named GAME, already scientifically
successfully tested and validated on astrophysical massive data classification
problems, through a web application resource (DAMEWARE), specialized in data
mining based on Machine Learning paradigms. Since genetic algorithms are
inherently parallel, the GPGPU computing paradigm has provided an exploit of
the internal training features of the model, permitting a strong optimization
in terms of processing performances and scalability.Comment: 11 pages, 2 figures, refereed proceedings; Neural Nets and
Surroundings, Proceedings of 22nd Italian Workshop on Neural Nets, WIRN 2012;
Smart Innovation, Systems and Technologies, Vol. 19, Springe
GPU acceleration of brain image proccessing
Durante los últimos años se ha venido demostrando el alto poder computacional
que ofrecen las GPUs a la hora de resolver determinados problemas.
Al mismo tiempo, existen campos en los que no es posible beneficiarse completamente
de las mejoras conseguidas por los investigadores, debido principalmente
a que los tiempos de ejecución de las aplicaciones llegan a ser extremadamente
largos. Este es por ejemplo el caso del registro de imágenes en medicina.
A pesar de que se han conseguido aceleraciones sobre el registro de imágenes,
su uso en la práctica clínica es aún limitado. Entre otras cosas, esto se debe
al rendimiento conseguido.
Por lo tanto se plantea como objetivo de este proyecto, conseguir mejorar los
tiempos de ejecución de una aplicación dedicada al resgitro de imágenes en medicina,
con el fin de ayudar a aliviar este problema
OpenCL Actors - Adding Data Parallelism to Actor-based Programming with CAF
The actor model of computation has been designed for a seamless support of
concurrency and distribution. However, it remains unspecific about data
parallel program flows, while available processing power of modern many core
hardware such as graphics processing units (GPUs) or coprocessors increases the
relevance of data parallelism for general-purpose computation.
In this work, we introduce OpenCL-enabled actors to the C++ Actor Framework
(CAF). This offers a high level interface for accessing any OpenCL device
without leaving the actor paradigm. The new type of actor is integrated into
the runtime environment of CAF and gives rise to transparent message passing in
distributed systems on heterogeneous hardware. Following the actor logic in
CAF, OpenCL kernels can be composed while encapsulated in C++ actors, hence
operate in a multi-stage fashion on data resident at the GPU. Developers are
thus enabled to build complex data parallel programs from primitives without
leaving the actor paradigm, nor sacrificing performance. Our evaluations on
commodity GPUs, an Nvidia TESLA, and an Intel PHI reveal the expected linear
scaling behavior when offloading larger workloads. For sub-second duties, the
efficiency of offloading was found to largely differ between devices. Moreover,
our findings indicate a negligible overhead over programming with the native
OpenCL API.Comment: 28 page
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