The GAP (Universidad Politécnica de Valencia, Spain) and HPCA (Universidad Jaume I, Spain) research groups are proud to announce the public release of rCUDA 1.0. The rCUDA Framework enables the concurrent usage of CUDA-compatible devices remotely by employing the sockets API for communication between clients and servers. Thus, it can be useful in three different environments:

• Clusters. To reduce the number of GPUs installed in High Performance Clusters. This leads to energy savings, as well as other related savings like acquisition costs, maintenance, space, cooling, etc.
• Academia. In low performance networks, to offer access to a few high performance GPUs concurrently to all the students.
• Virtual Machines. To enable the access to the CUDA facilities on the physical machine.

The current version of rCUDA (v1.0) implements all functions in the CUDA Runtime API version 2.3, excluding OpenGL and Direct3D interoperability. rCUDA 1.0 targets the Linux OS (for 32- and 64-bit architectures) on both client and server sides. The framework is free for any purpose under the terms and conditions of the GNU GPL/LGPL (where applicable) licenses.

For additional information, visit the rCUDA web page or Antonio Peña’s webpage.

GMAC (Global Memory for ACcelerators) is a user-level library that implements an Asymmetric Distributed Shared Memory model to be used by CUDA programs. An ADSM model allows CPU code to access data hosted in accelerator (GPU) memory. In this model, a single pointer is used for data structures accessed both in the CPU and the GPU and the coherency of the data is transparently handled by the library. Moreover, the data allocated with GMAC can be accessed by all the host threads of the program. That makes your code simpler and cleaner. GMAC currently supports programs programmed with CUDA, but OpenCL support is planned.

A paper describing the Asymmetric Distributed Shared Memory model and its implementation in GMAC has been accepted in the ASPLOS XV conference. GMAC is being developed by the Operating System Group at the Universitat Politecnica de Catalunya and the IMPACT Research Group at the University of Illinois. Binary pre-compiled packages, the source code, documentation and examples are available at the project website.

(Isaac Gelado, Javier Cabezas, John Stone, Sanjay Patel, Nacho Navarro and Wen-mei Hwu,  “An Asymmetric Distributed Shared Memory Model for Heterogeneous Parallel Systems”, accepted in: Fifteenth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2010), March 2010.)

OpenNL (Open Numerical Library) is a library for solving sparse linear systems, especially designed for the Computer Graphics community. The goal of OpenNL is to be as small as possible, while offering the subset of functionalities required by this application field. The Makefiles of OpenNL can generate a single .c and .h file that make it very easy to integrate into other projects. The distribution includes an implementation of a Least Squares Conformal Maps parameterization method.  The new version 3.0 of OpenNL includes support for CUDA (with Concurrent Number Cruncher and CUSP ELL formats).

The developers of the CUDPP (CUDA Data-Parallel Primitives) Library request that users (past and current) of the CUDPP Library fill out the CUDPP Survey.  This survey will help the CUDPP Team prioritize new development and support for existing and new features.

GPULib provides a library of mathematical functions that facilitate the use of high performance computing resources available on modern graphics processing units (GPUs) by engineers, scientists, analysts, and other technical professionals with minimal modification to their existing programs. This software library executes vectorized mathematical functions on graphics processing units (GPUs) from NVIDIA, bringing high-performance numerical operations to everyday desktop computers. By providing bindings for a number of Very High Level Languages (VHLLs) including MATLAB and IDL from ITT Visual Information Solutions, GPULib can accelerate new applications or be incorporated into existing applications with minimal effort. No knowledge of GPU programming and memory management is required. For more information regarding GPULib, please visit http://GPULib.txcorp.com.

nHD is a multi-GPU 2nd order full Godunov three-dimensional uniform-mesh Euler equations solver for calorically ideal, compressible gas. nHD uses CUDA C with MPI and runs on a cluster of multi-GPU machines to accelerate computational hydrodynamics calculations.

Full Godunov method solves the hydrodynamic equations by discretizing the fluid and calculating the nonlinear evolution of the discretized distribution, using the analytic solutions for Riemann problems. Thus full Godunov method can resolve arbitrary severe shockwaves with minimum artificial dissipation and oscillation, and is the irreplaceable method for simulations of compressible fluid where shockwaves and vacuums are naturally generated from fluid motions.

nHD is open source under a BSD-style license and is available, and comments are welcome at http://code.google.com/p/astro-attic/wiki/NHDIntroduction.

Thrust (v1.1) is  an open-source template library for developing CUDA applications.  Modeled after the C++ Standard Template Library (STL), Thrust brings a familiar abstraction layer to the realm of GPU computing. Version 1.1 adds several new features, including:

• fancy iterators
• binary search algorithms
• pair and tuple types
• segmented scan (experimental)
• pinned memory support (experimental)
• and more!

To get started with Thrust, first download Thrust and then follow the online tutorial.  Refer to the online documentation for a complete list of features.  Many concrete examples and a set of introductory slides are also available. As the following code example shows, Thrust programs are concise and readable. Read the rest of this entry »

OpenMM is an open-source library that enables molecular dynamics (MD) simulations to be accelerated on high performance computer architectures, such as GPUs. This latest release adds support for:

• A complete set of C and Fortran wrappers
• Energy computations on GPUs
• Ewald summation
• A faster algorithm for handling constraints
• And more!

Download the latest version of OpenMM from http://simtk.org/home/openmm.

EM Photonics has recently released a preview beta edition of their CULAtools, an implementation of LAPACK for CUDA-enabled GPUs. This version comprises single precision LU decomposition, QR factorization, singular value decomposition and least squares. The full library, scheduled for release at NVIDIA GTC ’09, will contain much more functionality and in particular single- and double-precision computations. Please refer to the website culatools.com for details, licenses and downloads.

The MAGMA project aims to develop a dense linear algebra library similar to LAPACK but for heterogeneous/hybrid architectures, starting with current “Multicore+GPU” systems.

The MAGMA research is based on the idea that, to address the complex challenges of the emerging hybrid environments, optimal software solutions will themselves have to hybridized, combining the strengths of different algorithms within a single framework. Building on this idea, the MAGMA group aims to design linear algebra algorithms and frameworks for hybrid manycore and GPU systems that can enable applications to fully exploit the power that each of the hybrid components offers.

MAGMA v0.1 runs on CUDA-capable GPUs and multicore CPUs, and is available now.