5 projects found that use cuda as the programming language.


Logo Somoclu 1.7.4

by peterwittek - June 6, 2017, 15:48:11 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 27544 views, 5001 downloads, 3 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. Apart from a command line interface, Python, Julia, R, and MATLAB are supported.

Changes:
  • New: Verbosity parameter in the command-line, Python, MATLAB, and Julia interfaces.
  • Changed: Calculation of U-matrix parallelized.
  • Changed: Moved feeding data to train method in the Python interface.
  • Fixed: The random seed was set to 0 for testing purposes. This is now changed to a wall-time based initialization.
  • Fixed: Sparse matrix reader made more robust.
  • Fixed: Compatibility with kohonen 3 resolved.
  • Fixed: Compatibility with Matplotlib 2 resolved.

Logo Theano 0.9.0

by jaberg - April 10, 2017, 20:30:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 33088 views, 5560 downloads, 3 subscriptions

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 0.9.0 (20th of March, 2017)

Highlights (since 0.8.0):

* Better Python 3.5 support
* Better numpy 1.12 support
* Conda packages for Mac, Linux and Windows
* Support newer Mac and Windows versions
* More Windows integration:

    * Theano scripts (``theano-cache`` and ``theano-nose``) now works on Windows
    * Better support for Windows end-lines into C codes
    * Support for space in paths on Windows

* Scan improvements:

    * More scan optimizations, with faster compilation and gradient computation
    * Support for checkpoint in scan (trade off between speed and memory usage, useful for long sequences)
    * Fixed broadcast checking in scan

* Graphs improvements:

    * More numerical stability by default for some graphs
    * Better handling of corner cases for theano functions and graph optimizations
    * More graph optimizations with faster compilation and execution
    * smaller and more readable graph

* New GPU back-end:

    * Removed warp-synchronous programming to get good results with newer CUDA drivers
    * More pooling support on GPU when cuDNN isn't available
    * Full support of ignore_border option for pooling
    * Inplace storage for shared variables
    * float16 storage
    * Using PCI bus ID of graphic cards for a better mapping between theano device number and nvidia-smi number
    * Fixed offset error in ``GpuIncSubtensor``

* Less C code compilation
* Added support for bool dtype
* Updated and more complete documentation
* Bug fixes related to merge optimizer and shape inference
* Lot of other bug fixes, crashes fixes and warning improvements

Logo CURFIL 1.1

by hanschul - August 18, 2014, 13:54:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3099 views, 708 downloads, 1 subscription

About: CURFIL uses NVIDIA CUDA to accelerate random forest training and prediction for RGB and RGB-D images. It focuses on image labelling tasks, such as image segmentation or classification applications. CURFIL allows to search for optimal hyper-parameter configurations (e.g. using the hyperopt) package) by massively decreasing training time.

Changes:

Initial Announcement on mloss.org.


Logo Caffe 0.9999

by sergeyk - August 9, 2014, 01:57:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 13878 views, 2246 downloads, 2 subscriptions

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:

LOTS of stuff: https://github.com/BVLC/caffe/releases/tag/v0.9999


Logo GPUML GPUs for kernel machines 4

by balajivasan - February 26, 2010, 18:12:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7742 views, 1430 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.