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. 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] CUDA has several advantages over traditional general-purpose computation on GPUs (GPGPU) using graphics APIs:

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. 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. If you know of a piece of software you feel that should be on this list, please let me know, or, even better, send a patch!

Staged event-driven architecture SEDA employs dynamic control to automatically tune runtime parameters (such as the scheduling parameters of each stage) as well as to manage load (like performing adaptive load shedding). Decomposing services into a set of stages also enables modularity and code reuse, as well as the development of debugging tools for complex event-driven applications. See also[edit] References[edit] Bibliography[edit] External links[edit] Apache ServiceMix provides a Java SEDA wrapper, combining it with related message architectures (JMS, JCA & straight-through flow).Criticism about how SEDA premises (threads are expensive) are no longer validJCyclone: Java open source implementation of SEDAMule ESB is another open-source Java implementationSEDA: An Architecture for Highly Concurrent Server Applications describing the PhD thesis by Matt Welsh from Harvard UniversityA Retrospective on SEDA by Matt Welsh, July 26, 2010

CUDA CUDA (англ. Compute Unified Device Architecture) — программно-аппаратная архитектура параллельных вычислений, которая позволяет существенно увеличить вычислительную производительность благодаря использованию графических процессоров фирмы Nvidia. Программная архитектура[править | править исходный текст] 22 марта 2010 года nVidia выпустила CUDA Toolkit 3.0, который содержал поддержку OpenCL.[1] Оборудование[править | править исходный текст] Платформа CUDA Впервые появились на рынке с выходом чипа NVIDIA восьмого поколения G80 и стала присутствовать во всех последующих сериях графических чипов, которые используются в семействах ускорителей GeForce, Quadro и NVidia Tesla. Первая серия оборудования, поддерживающая CUDA SDK, G8x, имела 32-битный векторный процессор одинарной точности, использующий CUDA SDK как API (CUDA поддерживает тип double языка Си, однако сейчас его точность понижена до 32-битного с плавающей запятой). Преимущества[править | править исходный текст]

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. 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.

Signal processing and the evolution of NAND flash memory Fueled by rapidly accelerating demand for performance-intensive computing devices, the NAND flash memory market is one of the largest and fastest-growing segments of the semiconductor industry, with annual sales of nearly $20 billion. During the past decade, the cost per bit of NAND flash has declined by a factor of 1,000, or a factor of 2 every 12 months, far exceeding Moore’s Law expectations. This rapid price decline has been driven by aggressive process geometry scale-down and by an increase in the number of bits stored in each memory cell from one to two and three bits per cell. As a consequence, the endurance of flash memory – defined as the number of Program and Erase (P/E) cycles that each memory cell can tolerate throughout its lifetime – is severely degraded due to process and array impairments, resulting in a nonlinear increase in the number of errors in flash memory. Getting past errors The most commonly used ECCs for flash memory are Bose-Chaudhuri-Hocquenghem (BCH) codes.

CUDA Zone -- The resource for CUDA developers Что такое CUDA? CUDA – это архитектура параллельных вычислений от NVIDIA, позволяющая существенно увеличить вычислительную производительность благодаря использованию GPU (графических процессоров). На сегодняшний день продажи CUDA процессоров достигли миллионов, а разработчики программного обеспечения, ученые и исследователи широко используют CUDA в различных областях, включая обработку видео и изображений, вычислительную биологию и химию, моделирование динамики жидкостей, восстановление изображений, полученных путем компьютерной томографии, сейсмический анализ, трассировку лучей и многое другое. Параллельные вычисления с CUDA Направление вычислений эволюционирует от «централизованной обработки данных» на центральном процессоре до «совместной обработки» на CPU и GPU. Говоря о потребительском рынке, стоит отметить, что почти все основные приложения для работы с видео уже оборудованы, либо будут оснащены поддержкой CUDA-ускорения, включая продукты от Elemental Technologies, MotionDSP и LoiLo.

OpenClooVision - OpenCL computer vision library for .NET / C# Apache Cassandra Apache Cassandra is an open source distributed database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple datacenters,[1] with asynchronous masterless replication allowing low latency operations for all clients. Cassandra also places a high value on performance. In 2012, University of Toronto researchers studying NoSQL systems concluded that "In terms of scalability, there is a clear winner throughout our experiments. Cassandra achieves the highest throughput for the maximum number of nodes in all experiments Tables may be created, dropped, and altered at runtime without blocking updates and queries.[6] History[edit] Releases after graduation include Licensing and support[edit] Apache Cassandra is an Apache Software Foundation project, so it has an Apache License (version 2.0). Main features[edit] Decentralized Scalability

Developer Newsletter 47: April 2009 The page you are looking for has been removed, or never existed. We’re sorry for any inconvenience. Please explore CUDA, NVIDIA GameWorks, and Professional Graphics sections of our site. 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

Amazon DynamoDB Overview[edit] DynamoDB differs from other Amazon services by allowing developers to purchase a service based on throughput, rather than storage. Although the database will not scale automatically, administrators can request more throughput and DynamoDB will spread the data and traffic over a number of servers using solid-state drives, allowing predictable performance.[1] It offers integration with Hadoop via Elastic MapReduce. In September 2013, Amazon made available a local development version of DynamoDB so developers can test DynamoDB-backed applications locally.[3] Language bindings[edit] References[edit] External links[edit]