The present and future of deep learning in radiology
Abstract The advent of Deep Learning (DL) is poised to dramatically change the delivery of
healthcare in the near future. Not only has DL profoundly affected the healthcare industry it …
healthcare in the near future. Not only has DL profoundly affected the healthcare industry it …
Sentiment analysis using deep learning approaches: an overview
Nowadays, with the increasing number of Web 2.0 tools, users generate huge amounts of
data in an enormous and dynamic way. In this regard, the sentiment analysis appeared to be …
data in an enormous and dynamic way. In this regard, the sentiment analysis appeared to be …
[图书][B] Handbook of virtual environments: Design, implementation, and applications
KS Hale, KM Stanney - 2014 - books.google.com
This second edition of a bestseller presents systematic and extensive coverage of the
primary areas of research and development within VE technology. It brings together a …
primary areas of research and development within VE technology. It brings together a …
A performance study of general-purpose applications on graphics processors using CUDA
Graphics processors (GPUs) provide a vast number of simple, data-parallel, deeply
multithreaded cores and high memory bandwidths. GPU architectures are becoming …
multithreaded cores and high memory bandwidths. GPU architectures are becoming …
Dynamic warp formation and scheduling for efficient GPU control flow
WWL Fung, I Sham, G Yuan… - 40th Annual IEEE/ACM …, 2007 - ieeexplore.ieee.org
Recent advances in graphics processing units (GPUs) have resulted in massively parallel
hardware that is easily programmable and widely available in commodity desktop computer …
hardware that is easily programmable and widely available in commodity desktop computer …
Performance and scalability of GPU-based convolutional neural networks
D Strigl, K Kofler, S Podlipnig - 2010 18th Euromicro …, 2010 - ieeexplore.ieee.org
In this paper we present the implementation of a framework for accelerating training and
classification of arbitrary Convolutional Neural Networks (CNNs) on the GPU. CNNs are a …
classification of arbitrary Convolutional Neural Networks (CNNs) on the GPU. CNNs are a …
Understanding GPU power: A survey of profiling, modeling, and simulation methods
RA Bridges, N Imam, TM Mintz - ACM Computing Surveys (CSUR), 2016 - dl.acm.org
Modern graphics processing units (GPUs) have complex architectures that admit
exceptional performance and energy efficiency for high-throughput applications. Although …
exceptional performance and energy efficiency for high-throughput applications. Although …
地震叠前时间偏移的一种图形处理器提速实现方法
李博， 刘国峰， 刘洪 - 地球物理学报, 2009 - dzkx.org
新近发展的图形处理器(GPU, Graphic Processing Unit) 通用计算技术, 现已日趋实用成型,
并获得诸多应用领域的广泛关注. 对油气勘探专项资料处理技术的运用而言, 概因GPU …
并获得诸多应用领域的广泛关注. 对油气勘探专项资料处理技术的运用而言, 概因GPU …
Accelerating k-means on the graphics processor via cuda
M Zechner, M Granitzer - 2009 First International Conference …, 2009 - ieeexplore.ieee.org
In this paper an optimized k-means implementation on the graphics processing unit (GPU) is
presented. NVIDIApsilas compute unified device architecture (CUDA), available from the …
presented. NVIDIApsilas compute unified device architecture (CUDA), available from the …
[PDF][PDF] Automated dynamic analysis of CUDA programs
Recent increases in the programmability and performance of GPUs have led to a surge of
interest in utilizing them for general-purpose computations. Tools such as NVIDIA's Cuda …
interest in utilizing them for general-purpose computations. Tools such as NVIDIA's Cuda …