Results 231 to 240 of about 55,999 (288)

<i>ScaleSC</i>: a superfast and scalable single-cell RNA-seq data analysis pipeline powered by GPU. [PDF]

Bioinform Adv
Hu W, Zhang H, Sun YH, Cao S, Gagnon J, Moroishi Y, Chen Y, Ouyang Z, Zhang B.   +8 more
europepmc   +1 more source

Domain-Specific Acceleration of Gravity Forward Modeling via Hardware-Software Co-Design. [PDF]

Micromachines (Basel)
Yang Y, Sun D, Ma Z, Gu W.
europepmc   +1 more source

MedPTQ: a practical pipeline for real post-training quantization in 3D medical image segmentation. [PDF]

J Med Imaging (Bellingham)
Qu C, Zhao R, Yu Y, Liu B, Yao T, Zhu J, Landman BA, Tang Y, Huo Y.   +8 more
europepmc   +1 more source

Physics-guided GNN-transformer model for multi-scale fatigue life prediction of concrete track slabs in high-speed railways. [PDF]

Sci Rep
Su X, Lou P, Zha Z.
europepmc   +1 more source

Code-Based Versus AutoML Methods for Pill Recognition in Clinical Settings: Comparative Performance Study.

JMIR Med Inform
Ashraf AR, Rádli R, Vörösházi Z, Fittler A.   +3 more
europepmc   +1 more source

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Evolution of the Graphics Processing Unit (GPU)

IEEE Micro, 2021
Graphics processing units (GPUs) power today’s fastest supercomputers, are the dominant platform for deep learning, and provide the intelligence for devices ranging from self-driving cars to robots and smart cameras. They also generate compelling photorealistic images at real-time frame rates.
William J. Dally, Stephen W. Keckler, David B. Kirk   +2 more
openaire   +1 more source

Fermi GF100 graphics processing unit (GPU)

2010 IEEE Hot Chips 22 Symposium (HCS), 2010
This article consists of a collection of slides from the author's conference presentation on the NVIDIA Fermi GF family of GPU products. Some of the specific topics discussed include: the special features, system specifications, and system design for these products; system architectures; applications for use; platforms supported; processing ...
Craig M. Wittenbrink, Emmett Kilgariff, Arjun Prabhu   +2 more
openaire   +1 more source

Accelerating geostatistical simulations using graphics processing units (GPU)

Computers & Geosciences, 2012
Geostatistical simulations have become a widely used tool for modeling of oil and gas reservoirs and the assessment of uncertainty. One important current issue is the development of high-resolution models in a reasonable computational time. A possible solution is based on taking advantage of parallel computational strategies. In this paper we present a
Pejman Tahmasebi, Muhammad Sahimi, Grégoire Mariethoz, Ardeshir Hezarkhani   +3 more
openaire   +1 more source

Tensor Voting Accelerated by Graphics Processing Units (GPU)

18th International Conference on Pattern Recognition (ICPR'06), 2006
This paper presents a new GPU-based tensor voting implementation which achieves significant performance improvement over the conventional CPU-based implementation. Although the tensor voting framework has been used for many vision problems, it is computationally very intensive when the number of input tokens is very large.
null Changki Min, G. Medioni
openaire   +1 more source

Wavefront phase recovery using graphic processing units (GPUs)

SPIE Proceedings, 2004
We have developed a Shack-Hartmann sensor simulation, moving the complex amplitude of the electromagnetic field using Fast Fourier Transforms. The Shack-Hartmann sensor takes as input the atmospheric wavefront frames generated by the Roddier algorithm, and provides, as output, the subpupil images. The centroids and the wavefront phase maps are computed
Fernando L. Rosa, Jose G. Marichal-Hernandez, Jose M. Rodriguez-Ramos   +2 more
openaire   +1 more source