Projects

pycuda

PyCuda is licensed to you under the MIT/X Consortium license: Copyright (c) 2008 Andreas Klöckner Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction ... [More] , including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHAN [Less]

playdoh

Playdoh is a pure Python library for distributing computations across the free computing units (CPUs and GPUs) available in a small network of multicore computers. Playdoh supports independent (embarassingly) parallel problems as well as loosely coupled tasks such as global optimizations, Monte ... [More] Carlo simulations and numerical integration of partial differential equations. It is designed to be lightweight and easy to use and should be of interest to scientists wanting to turn their lab computers into a small cluster at no cost. [Less]

pystream

PyStream combines the power and convenience of Python with the high performance of modern Graphics Processing Units (GPUs). The focus of PyStream is on NVIDIA GPUs, such as the GeForce 8800 and Tesla series, that support the Compute Unified Device Architecture (CUDA) toolkit. With PyStream, the ... [More] CUDA libraries, including the CUDA BLAS and FFT libraries, can be called from directly from Python. Data can be moved back and forth seamlessly between the GPU and Python objects (NumPy arrays) on the CPU. Initial development of PyStream was done by Tech-X Corporation. Tech-X Corporation has shifted its efforts to a new GPU related project, called GPULib, that has a higher level API than PyStream and also supports other languages other than Python. Because of this change, PyStream is no longer being actively developed. However, PyStream will remain available under the BSD license. [Less]

pygwa

PyGWA is library providing a set of tools for general purpose GPU computing. It is intended to be (relatively) easy to use and support various devices / frameworks but currently it supports AMD Stream SDK only and is heavily influenced by its 'streaming' philosophy. Other frameworks (hopefully) will be supported in the future.

xeres

A 3D game engine designed for simple to use.

sailfish-cuda

Sailfish is an Open Source fluid dynamics solver for CUDA devices. It uses the Lattice Boltzmann method to simulate incompressible and weakly compressible flows in 2D and 3D. The project uses Python to maintain a rapid pace of development and high level of code readability. pycuda and numpy ... [More] allow it to achieve high performance without compromising the flexibility offered by Python. [Less]

cuda-ndarray

This project have been merged into theano. Their is no more development here. This project provides a type with an interface as similar as possible to numpy's ndarray whose storage is allocated on a GPU device. >>> import numpy, cuda_ndarray >>> a = ... [More] numpy.asarray(numpy.random.rand(4,5,6), dtype='float32') #allocate mem on host >>> cuda_a = cuda_ndarray.CudaNdarray(a) #copy 'a' to gpu >>> cuda_b = cuda_a + cuda_a #compute on GPU >>> numpy_copy_of_2a = numpy.asarray(cuda_b) #copy result to hostPretty early in development. What works: 2D image convolution (we're focusing on this) reshaping viewing matrix multiplication addition (proof of concept binary operation in 'number protocol') exp (proof of concept method) See the Issues page for things that are (will be) in the works. You pretty much have to be a developer to use this package at the moment--there is only a little code documentation. No installer beyond a Makefile. Email a developer if you have a question. No email list is set up for this project. In case you are wondering why we are developing such a bare-bones type, it's because we're using it under the hood for theano via theano-cuda-ndarray... a really nice way to leverage the GPU in your python code without having to think about it. [Less]

cudamat

CUDAMatThe aim of the cudamat project is to make it easy to perform basic matrix calculations on CUDA-enabled GPUs from Python. cudamat provides a Python matrix class that performs calculations on a GPU. At present, some of the operations our GPU matrix class supports include: Easy conversion to ... [More] and from instances of numpy.ndarray. Limited slicing support. Matrix multiplication and transpose. Elementwise addition, subtraction, multiplication, and division. Elementwise application of exp, log, pow, sqrt. Summation along rows or columns. Conversion of CUDA errors into Python exceptions. The current feature set of cudamat is biased towards features needed for implementing some common machine learning algorithms. We have included implementations of feedforward neural networks and restricted Boltzmann machines in the examples that come with cudamat. Example: import numpy as np import cudamat as cm cm.cublas_init() # create two random matrices and copy them to the GPU a = cm.CUDAMatrix(np.random.rand(32, 256)) b = cm.CUDAMatrix(np.random.rand(256, 32)) # perform calculations on the GPU c = cm.dot(a, b) d = c.sum(axis = 0) # copy d back to the host (CPU) and print print d.asarray()DocumentationAn overview of the main features of CUDAMat can be found in the technical report: CUDAMat: A CUDA-based matrix class for Python, Volodymyr Mnih, UTML TR 2009-004. DownloadYou can obtain the latest release from the repository by typing: hg clone https://cudamat.googlecode.com/hg/ cudamat You can also download one of the releases from the "Downloads" section. CUDAMat has the following prerequisites: CUDA Numpy nose [Less]

pygpu

Haven't you ever dreamt of writing code in a very high level language and have that code execute at speeds rivaling that of lower-level languages? PyGPU is a compiler that lets you write image processing programs in Python that execute on the graphics processing unit (GPU) present in modern ... [More] graphics cards. This enables image processing algorithms to take advantage of the performance of the GPU. In some applications, performance increases approach an order of magnitude, compared to CPUs. [Less]