4 projects found that use cuda as the programming language.


Logo Somoclu 1.3.1

by peterwittek - April 10, 2014, 06:41:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2197 views, 407 downloads, 2 subscriptions

About: Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes.

Changes:
  • Initial Windows support through GCC on Windows.
  • Better I/O separation for the Python, R, and MATLAB interfaces.
  • Bug fixes: major MPI initialization bug fixed.

Logo Caffe 0.99

by sergeyk - March 19, 2014, 08:56:24 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1269 views, 194 downloads, 1 subscription

About: Caffe aims to provide computer vision scientists with a clean, modifiable implementation of state-of-the-art deep learning algorithms. We believe that Caffe is the fastest available GPU CNN implementation. Caffe also provides seamless switching between CPU and GPU, which allows one to train models with fast GPUs and then deploy them on non-GPU clusters. Even in CPU mode, computing predictions on an image takes only 20 ms (in batch mode).

Changes:

Initial Announcement on mloss.org.


Logo Theano 0.6

by jaberg - December 3, 2013, 20:32:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 11313 views, 2137 downloads, 1 subscription

About: A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Dynamically generates CPU and GPU modules for good performance. Deep Learning Tutorials illustrate deep learning with Theano.

Changes:

Theano 0.6 (December 3th, 2013)

Highlight:

* Last release with support for Python 2.4 and 2.5.
* We will try to release more frequently.
* Fix crash/installation problems.
* Use less memory for conv3d2d.

0.6rc4 skipped for a technical reason.

Highlights (since 0.6rc3):

* Python 3.3 compatibility with buildbot test for it.
* Full advanced indexing support.
* Better Windows 64 bit support.
* New profiler.
* Better error messages that help debugging.
* Better support for newer NumPy versions (remove useless warning/crash).
* Faster optimization/compilation for big graph.
* Move in Theano the Conv3d2d implementation.
* Better SymPy/Theano bridge: Make an Theano op from SymPy expression and use SymPy c code generator.
* Bug fixes.

Too much changes in 0.6rc1, 0.6rc2 and 0.6rc3 to list here. See https://github.com/Theano/Theano/blob/master/NEWS.txt for details.


Logo GPUML GPUs for kernel machines 4

by balajivasan - February 26, 2010, 18:12:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4605 views, 785 downloads, 1 subscription

About: GPUML is a library that provides a C/C++ and MATLAB interface for speeding up the computation of the weighted kernel summation and kernel matrix construction on GPU. These computations occur commonly in several machine learning algorithms like kernel density estimation, kernel regression, kernel PCA, etc.

Changes:

Initial Announcement on mloss.org.