Extensive sampling for complete models of individual brains
Highlights•Trade-off between sampling individual variation versus experimental
variation.•Different studies have allocated resources differently.•We argue that wide …
variation.•Different studies have allocated resources differently.•We argue that wide …
On-board computer for cubesats: State-of-the-art and future trends
Over the past three decades, the acceptance of higher risk thresholds within the space
industry has facilitated the widespread integration of commercial off-the-shelf (COTS) …
industry has facilitated the widespread integration of commercial off-the-shelf (COTS) …
Deep image: Scaling up image recognition
We present a state-of-the-art image recognition system, Deep Image, developed using end-
to-end deep learning. The key components are a custom-built supercomputer dedicated to …
to-end deep learning. The key components are a custom-built supercomputer dedicated to …
[图书][B] CUDA application design and development
R Farber - 2011 - books.google.com
As the computer industry retools to leverage massively parallel graphics processing units
(GPUs), this book is designed to meet the needs of working software developers who need …
(GPUs), this book is designed to meet the needs of working software developers who need …
Dual: Acceleration of clustering algorithms using digital-based processing in-memory
Today's applications generate a large amount of data that need to be processed by learning
algorithms. In practice, the majority of the data are not associated with any labels …
algorithms. In practice, the majority of the data are not associated with any labels …
Where is the data? Why you cannot debate CPU vs. GPU performance without the answer
C Gregg, K Hazelwood - (IEEE ISPASS) IEEE International …, 2011 - ieeexplore.ieee.org
General purpose GPU Computing (GPGPU) has taken off in the past few years, with great
promises for increased desktop processing power due to the large number of fast computing …
promises for increased desktop processing power due to the large number of fast computing …
Speeding up k-means algorithm by gpus
Cluster analysis plays a critical role in a wide variety of applications; but it is now facing the
computational challenge due to the continuously increasing data volume. Parallel …
computational challenge due to the continuously increasing data volume. Parallel …
Parallel data mining techniques on graphics processing unit with compute unified device architecture (CUDA)
L Jian, C Wang, Y Liu, S Liang, W Yi, Y Shi - The Journal of …, 2013 - Springer
Abstract Recent development in Graphics Processing Units (GPUs) has enabled
inexpensive high performance computing for general-purpose applications. Compute …
inexpensive high performance computing for general-purpose applications. Compute …
Accelerating K-Means clustering with parallel implementations and GPU computing
K-Means clustering is a popular unsupervised machine learning method which has been
used in diverse applications including image processing, information retrieval, social …
used in diverse applications including image processing, information retrieval, social …
Using the stability of objects to determine the number of clusters in datasets
We introduce a novel method for assessing the robustness of clusters found by partitioning
algorithms. First, we show how the stability of individual objects can be estimated based on …
algorithms. First, we show how the stability of individual objects can be estimated based on …