9,138 research outputs found
Performance Analysis of a Novel GPU Computation-to-core Mapping Scheme for Robust Facet Image Modeling
Though the GPGPU concept is well-known
in image processing, much more work remains to be done
to fully exploit GPUs as an alternative computation
engine. This paper investigates the computation-to-core
mapping strategies to probe the efficiency and scalability
of the robust facet image modeling algorithm on GPUs.
Our fine-grained computation-to-core mapping scheme
shows a significant performance gain over the standard
pixel-wise mapping scheme. With in-depth performance
comparisons across the two different mapping schemes,
we analyze the impact of the level of parallelism on
the GPU computation and suggest two principles for
optimizing future image processing applications on the
GPU platform
GPU-powered Simulation Methodologies for Biological Systems
The study of biological systems witnessed a pervasive cross-fertilization
between experimental investigation and computational methods. This gave rise to
the development of new methodologies, able to tackle the complexity of
biological systems in a quantitative manner. Computer algorithms allow to
faithfully reproduce the dynamics of the corresponding biological system, and,
at the price of a large number of simulations, it is possible to extensively
investigate the system functioning across a wide spectrum of natural
conditions. To enable multiple analysis in parallel, using cheap, diffused and
highly efficient multi-core devices we developed GPU-powered simulation
algorithms for stochastic, deterministic and hybrid modeling approaches, so
that also users with no knowledge of GPUs hardware and programming can easily
access the computing power of graphics engines.Comment: In Proceedings Wivace 2013, arXiv:1309.712
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