Abstract:

This chapter demonstrates how to leverage the Thrust parallel template library to implement high-performance applications with minimal programming effort. Based on the C++ Standard Template Library (STL), Thrust brings a familiar high-level interface to the realm of GPU Computing while remaining fully interoperable with the rest of the CUDA software ecosystem. Applications written with Thrust are concise, readable, and efficient.

(Nathan Bell and Jared Hoberock: “Thrust: A Productivity-Oriented Library for CUDA”, GPU Computing Gems, Jade Edition, edited by Wen-mei W. Hwu, October 2011)

libCL is an open-source parallel algorithm library written in C++ and OpenCL. Rather than a specific domain, libCL intends to encompass a wide range of parallel algorithms and data structures. The goal is to provide a comprehensive repository for high performance visual-centric computing ranging from fundamental primitives such as sorting, searching and algebra to advanced systems of algorithms for computational research and visualization. The current distribution of libCL already contains entirely parallelized implementations of the following algorithms:

• Bounding volume hierarchy construction
• Smoothed particle hydrodynamics
• Radix sort
• Adaptive tone-mapping
• Screen-space ambient occlusion culling
• Bilateral and Recursive Gaussian

libCL emerged out of OpenCL Studio, and as such integrates well with the development environment and its visualization capabilities. libCL is Open Source and released under the Apache license.

CUDPP release 2.0 is a major new release of the CUDA Data-Parallel Primitives Library, with exciting new features. The public interface has undergone a minor redesign to provide thread safety. Parallel reductions (cudppReduce) and a tridiagonal system solver (cudppTridiagonal) have been added, and a new component library, cudpp_hash, provides fast data-parallel hash table functionality. In addition, support for 64-bit data types (double as well as long long and unsigned long long) has been added to all CUDPP algorithms, and a variety of bugs have been fixed.  For a complete list of changes, see the change log. CUDPP 2.0 is available for download now.

Odeint is a high level C++ library for solving ordinary differential equations. It is released under an open-source license and supports a variety of different methods for solving ODEs. As a special feature it supports different algebras which perform the basic mathematical operations. This allows the user to solve ordinary differential equations on modern graphic cards. A Thrust interface is implemented, so that the power of CUDA can easily be employed. Furthermore, arbitrary precision types can easily be supported.  Read the rest of this entry »

TidePowerd has released Version 2 of their GPU computing solution for the .NET framework, GPU.NET. Their platform allows developers to quickly and easily write GPU-accelerated applications completely in .NET-based languages. Some key benefits include:

• Stay in C# and treat kernel methods like any regular method
• “Boilerplate” GPU programming tasks such as memory transfer and GPU scheduling are abstracted from the developer
• Cross-platform and cross-hardware with a single binary
• Systems seamlessly adapt to new hardware without rewriting code
• Speed on par with native code

New version 2 features:

• Visual Studio Error list and IntelliSense integration
• On-device random number generation
• Double precision support

A free 30-days evaluation license is available, as well as in-depth examples and tutorials.

Jacket 1.8 and LibJacket 1.1 have been released by Accelereyes, enabling GPU support for MATLAB and easier CUDA development with C/C++/Fortran and Python.  New features include:

• Expanded support for the Signal Processing, Image Processing, and Statistics Libraries included with both Jacket and LibJacket
• Faster linear algebra for special systems (e.g. symmetric, positive definite, triangular, etc.)
• Enhanced visualizations
• New and updated examples: FDTD, Mandelbrot fractals, maximum-likelihood neural segmentation, MDS for genomics
• Built with CUDA 4.0 for peak performance

Visit http://www.accelereyes.com/ for details, downloads, whitepapers and tutorials.

TunaCode is pleased to announce the release of CUVI (CUDA Vision and Imaging Library) version 0.5 which comes with a new API and new features. This release makes it even simpler to add acceleration to existing Imaging applications, without any prior technical knowledge of GPUs. CUVI v0.5 is built from bottom up with performance and ease-of-use in mind.

CUVI version 0.5 is available for download at http://cuvilib.com and is available for Windows (Win32, x64) with planned support for Linux and Mac.

The Virtual School of Computational Science and Engineering (VSCSE) will offer a hands-on course for graduate students August 15-19:

Proven Algorithmic Techniques for Manycore Processors

This course will be delivered to a number of sites nationwide—including the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign—using high-definition video conferencing technologies. Students at all sites will be able to work with a cohort of fellow computational scientists, have access to local teaching assistants, and interact virtually with course instructors.

Registration for the weeklong course is $100. Please visit www.vscse.org for more information or hub.vscse.org to register.

Read the rest of this entry »

A new alpha release of rCUDA 3.0 (Remote CUDA), the Open Source package that allows performing CUDA calls to remote GPUs, has been released. Major improvements included in this new version are:

• Partially updated API to 4.0
• Added compatibility support with CUDA 4.0 environment
• Updated CUBLAS API to 4.0 for the most common CUBLAS routines
• Fixed some bugs
• General performance improvements

For further information, please visit the rCUDA webpage.

CUDA Template Generator is a Java application that allows generates CUDA C source file templates based on user input parameters. Features include :

• An algorithm for automatic block and thread definition, depending on array size.
• Automatic memory transfer functions for CPU->GPU->CPU communication.
• Generated C source code function template to use in your application.

Developed by Pavel Kartashev, as part of his Master’s Degree work.