For more information go to http://www.clusterchimps.org

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)

• 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.

With tutorials, hands-on labs, and sessions that span a range of topics from HPC to multimedia, you’ll have the opportunity to expand your view of what heterogeneous computing currently offers and where it is going. You’ll hear from industry innovators and academic pioneers who are exploring different ways of approaching problems, and utilizing new paradigms in computing to help identify solutions. You’ll meet AMD experts with deep knowledge of hardware architectures and the software techniques that best leverage those platforms. And you’ll connect with other software professionals who share your passion for the future of technology.

Learn more at developer.amd.com/afds.

SANTA CLARA, CA — (Marketwire) — 02/28/2011 – NVIDIA today announced the latest version of the NVIDIA® CUDA® Toolkit for developing parallel applications using NVIDIA GPUs.

The NVIDIA CUDA 4.0 Toolkit was designed to make parallel programming easier, and enable more developers to port their applications to GPUs. This has resulted in three main features:

• NVIDIA GPUDirect™ 2.0 Technology – Offers support for peer-to-peer communication among GPUs within a single server or workstation. This enables easier and faster multi-GPU programming and application performance.
• Unified Virtual Addressing (UVA) – Provides a single merged-memory address space for the main system memory and the GPU memories, enabling quicker and easier parallel programming.
• Thrust C++ Template Performance Primitives Libraries – Provides a collection of powerful open source C++ parallel algorithms and data structures that ease programming for C++ developers. With Thrust, routines such as parallel sorting are 5X to 100X faster than with Standard Template Library (STL) and Threading Building Blocks (TBB).

“Unified virtual addressing and faster GPU-to-GPU communication makes it easier for developers to take advantage of the parallel computing capability of GPUs,” said John Stone, senior research programmer, University of Illinois, Urbana-Champaign.

“Having access to GPU computing through the standard template interface greatly increases productivity for a wide range of tasks, from simple cashflow generation to complex computations with Libor market models, variable annuities or CVA adjustments,” said Peter Decrem, director of Rates Products at Quantifi. ”The Thrust C++ library has lowered the barrier of entry significantly by taking care of low-level functionality like memory access and allocation, allowing the financial engineer to focus on algorithm development in a GPU-enhanced environment.”

The CUDA 4.0 architecture release includes a number of other key features and capabilities, including:

• MPI Integration with CUDA Applications – Modified MPI implementations automatically move data from and to the GPU memory over Infiniband when an application does an MPI send or receive call.
• Multi-thread Sharing of GPUs – Multiple CPU host threads can share contexts on a single GPU, making it easier to share a single GPU by multi-threaded applications.
• Multi-GPU Sharing by Single CPU Thread – A single CPU host thread can access all GPUs in a system. Developers can easily coordinate work across multiple GPUs for tasks such as “halo” exchange in applications.
• New NPP Image and Computer Vision Library – A rich set of image transformation operations that enable rapid development of imaging and computer vision applications.
• New and Improved Capabilities
  • Auto performance analysis in the Visual Profiler
  • New features in cuda-gdb and added support for MacOS
  • Added support for C++ features like new/delete and virtual functions
  • New GPU binary disassembler

A release candidate of CUDA Toolkit 4.0 will be available free of charge beginning March 4, 2011, by enrolling in the CUDA Registered Developer Program at: www.nvidia.com/paralleldeveloper. The CUDA Registered Developer Program provides a wealth of tools, resources, and information for parallel application developers to maximize the potential of CUDA.

For more information on the features and capabilities of the CUDA Toolkit and on GPGPU applications, please visit:www.nvidia.com/cuda.

http://code.google.com/p/openclcc/

OpenCLcc takes a text file with the OpenCL kernel code as input and calls the OpenCL run-time to compile it, echoing errors to the console.

• Multi-GPU support
• Tested on Fermi architecture (GTX460 and Tesla C2050)
• Automated submission of the domain to the GPU cards (using decomposePar from OpenFOAM)
• Optimized submission of computational tasks to the best GPU card in the system for any number of computational threads
• Plugin picks the most powerful GPU card for a single thread cases

The OpenFOAM SpeedIT plugin is available at http://speedit.vratis.com.