Understanding Memory Aliasing for Speed and Correctness — Theano v0.4.1 documentation. The aggressive reuse of memory is one of the ways through which Theano makes code fast, and it is important for the correctness and speed of your program that you understand how Theano might alias buffers. This section describes the principles based on which Theano handles memory, and explains when you might want to alter the default behaviour of some functions and methods for faster performance. The Memory Model: Two Spaces There are some simple principles that guide Theano’s handling of memory. The main idea is that there is a pool of memory managed by Theano, and Theano tracks changes to values in that pool. The distinction between Theano-managed memory and user-managed memory can be broken down by some Theano functions (e.g. shared, get_value and the constructors for In and Out) by using a borrow=True flag.
The rest of this section is aimed at helping you to understand when it is safe to use the borrow=True argument and reap the benefits of faster code. Take home message: Retrieving. Parallel Random Number Generation using OpenMP, OpenCL and PGI Accelerator Directives. May 2010 Federico Dal Castello, Advanced System Technology, STMicroelectronics, Italy Douglas Miles, The Portland Group In the article Tuning a Monte Carlo Algorithm on GPUs, Mat Colgrove explored an implementation of Monte Carlo Integration on NVIDIA GPUs using PGI Accelerator directives and CUDA Fortran.
The article showed how the required random number generator could be accelerated in the CUDA Fortran version by calling the CUDA C Mersenne Twister random number generator included in the NVIDIA CUDA SDK. The result was a speed-up of the random number generation by a factor of 23 over the serial version running on a single host core. To further explore this topic, we created OpenMP, OpenCL and PGI Accelerator directive-based versions of the Mersenne Twister algorithm, all derived from the source code available in the NVIDIA SDKs. OpenMP Implementation of Mersenne Twister Implementing the Mersenne Twister algorithm in OpenMP was very straightforward. Static uint32_t state[MT_NN]; to. PyCUDA | Andreas Klöckner's web page. PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist–so what’s so special about PyCUDA? Object cleanup tied to lifetime of objects.
This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it won’t detach from a context before all memory allocated in it is also freed.Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia’s C-based runtime.Completeness. PyCUDA puts the full power of CUDA’s driver API at your disposal, if you wish.Automatic Error Checking. See the PyCUDA Documentation.
If you’d like to get an impression what PyCUDA is being used for in the real world, head over to the PyCUDA showcase. Having trouble with PyCUDA? Git clone --recursive You may also browse the source. Prerequisites: CUDA. CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model created by NVIDIA and implemented by the graphics processing units (GPUs) that they produce.[1] CUDA gives program developers direct access to the virtual instruction set and memory of the parallel computational elements in CUDA GPUs. Using CUDA, the GPUs can be used for general purpose processing (i.e., not exclusively graphics); this approach is known as GPGPU. Unlike CPUs, however, GPUs have a parallel throughput architecture that emphasizes executing many concurrent threads slowly, rather than executing a single thread very quickly. CUDA provides both a low level API and a higher level API. The initial CUDA SDK was made public on 15 February 2007, for Microsoft Windows and Linux.
Example of CUDA processing flow 1. Background[edit] The GPU, as a specialized processor, addresses the demands of real-time high-resolution 3D graphics compute-intensive tasks. Advantages[edit] Limitations[edit] GPU Computing. Major chip manufacturers are developing next-generation microprocessor designs that are heterogeneous/hybrid in nature, integrating homogeneous x86-based multicore CPU components and GPU components.
The MAGMA (Matrix Algebra on GPU and Multicore Architectures) project’s goal is to develop innovative linear algebra algorithms and to incorporate them into a library that is • similar to LAPACK in functionality, data storage, and interface but targeting the • next-generation of highly parallel, and heterogeneous processors. This will allow scientists to effortlessly port any of their LAPACK-relying software components and to take advantage of the new architectures. MAGMA is designed to run on homogeneous x86-based multicores and take advantage of GPU components (if available). The transition from small tasks (of small block size) to large tasks is done in a recursive fashion where the intermediate for the transition tasks are executed in parallel using dynamic scheduling.
GPU Computing. Supercomputing on Graphics Cards - An Introduction to OpenCL and the C++ Bindings (Winter 09/10) These are the resources for an OpenCL course I gave in the winter semester of 2009/2010. Note: They are retained on this website for posterity, a newer version of the lecture is available here! Scientists often use numerical techniques to explore natural phenomena and these methods typically require huge amounts of calculation power. However, most scientific algorithms are serial by design and do not fully utilise the processing power of the computers they run on. Modern systems contain many processors and there is a huge untapped resource of numerical calculation power in the form of a graphics card or GPU.
This course will provide an introduction to OpenCL in the context of C++ and its scientific applications. Pystream - Project Hosting on Google Code. GPULib Product Page. Overview GPULib enables users to access high performance computing with minimal modification to their existing programs. By providing bindings between Interactive Data Language (IDL) and large function libraries, GPULib can accelerate new applications or be incorporated into existing applications with minimal effort. No knowledge of GPU programming or memory management is required. GPULib is built on top of NVIDIA's Compute Unified Device Architecture (CUDA) platform. CUDA is supported by a wide range of NVIDIA products, including GeForce, Quadro, and Tesla cards. Note: By default, GPULib supports only IDL 8.2 and CUDA 5.0. Features Available with Both Free Trial and Paid GPULib Licensing Additional Features Available Only with Paid GPULib Licensing 1D, 2D, and 3D FFTsBatched FFTsMAGMA linear algebra routines GPU accelerated LAPACK libraryAbility to load and execute pre-compiled custom kernelsLoad and execute custom CUDA code Advantages Performance Results Speed increases due to GPULib.
OpenCL. OpenCL SVM. Get sample source code for this resource (MNIST classifier). Get the MNIST dataset from Get the Support Vector Machine demonstration (demonstration below): 1. Introduction Support Vector Machines (SVMs) is a statistical learning tool [1] considered to be the state-of-the art classifiers for many applications today, including medical research [2] and text categorization [3]. SVM training and classification depends on computing distances (kernels) both to train the SVM and to use the learned parameters to classify data later on. These computations can be executed in parallel using OpenCL, which enhances even further the usability of the technique to problems with large training sets and vectors comprising large number of features. As an example, a software has been developed to classify the MNIST handwritten database [5].
Basically, SVMs focuses on the most difficult examples to classify and uses them as a boundary for classification. 2. 3. 3.1 Training 4. 5. Welcome to PyOpenCL’s documentation! — PyOpenCL v0.92 documentation. PyOpenCL gives you easy, Pythonic access to the OpenCL parallel computation API. What makes PyOpenCL special? Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code.Completeness. PyOpenCL puts the full power of OpenCL’s API at your disposal, if you wish.
Every obscure get_info() query and all CL calls are accessible.Automatic Error Checking. All errors are automatically translated into Python exceptions.Speed. Here’s an example, to give you an impression: (You can find this example as examples/demo.py in the PyOpenCL source distribution.) Bogdan Opanchuk’s reikna offers a variety of GPU-based algorithms (FFT, random number generation, matrix multiplication) designed to work with pyopencl.array.Array objects.Gregor Thalhammer’s gpyfft provides a Python wrapper for the OpenCL FFT library clFFT from AMD. Opencl/kernels/12/medianFilterRGBA.cl. OpenClooVision - OpenCL computer vision library for .NET / C# Dr. Dobb's | A Gentle Introduction to OpenCL | July 31, 2011. / - opencl-book-samples - Source code to the example programs from the OpenCL Programming Guide book.