April 5th, 2010
April 5th, 2010
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.
March 31st, 2010
After a very successful launch of the first volume of the Game Engine Gems series at GDC 2010, Jones and Bartlett Publishers is now accepting proposals for the second volume. The paper submission period for Game Engine Gems 2 is now open through June 15, 2010. To submit a proposal, please visit the official website.
As with the first volume, the theme of the book includes everything having to do with game engine design and implementation. Specific topics of interest include rendering techniques, shaders, OpenGL / DirectX, physics / collision detection, mathematics, programming techniques, engine architecture, visibility determination, audio, user interface, input devices, memory management, artificial intelligence, resource organization, and cross-platform considerations. This list is not exhaustive, and the editors are happy to evaluate any idea that pertains to making game engines.
March 26th, 2010
This year’s GPU Technology Conference (GTC 2010) will take place on Monday, Sept. 20 to Thursday, Sept. 23 at the San Jose Convention Center in San Jose, California.
Building on last year’s inaugural conference, GTC 2010 will feature an even broader and deeper selection of technical sessions, interactive tutorials, technology previews, and industry and academic presentations.
Three concurrent GPU-focused summits will occur under one roof:
- Emerging Companies Summit: A showcase for innovative startups to demonstrate products and network with VC’s and other investors.
- GPU Developers Summit: Sessions, tutorials, and presentations for developers, engineers, and scientists.
- NVIDIA Research Summit: A unique opportunity for students, professors, and researchers to present their findings and collaborate.
For more information:
March 23rd, 2010
The SpeedIT Tools library provides a set of accelerated solvers for sparse linear systems of equations. Manifold acceleration, e.g. more than an order of magnitude, is achieved with a single reasonably priced NVIDIA Graphics Processing Unit (GPU) that supports CUDA and proprietary advanced optimization techniques. The library can be used in a wide spectrum of domains arising from problems with underlying 2D and 3D geometry, such as computational fluid dynamics, electro-magnetics, thermodynamics, materials, acoustics, computer vision and graphics, robotics, semiconductor devices and structural engineering. The library can be also used for problems without defined geometry such as quantum chemistry, statistics, power networks and other graphs and chemical process simulation. All computations are performed with single or double floating point precision. Two version of SpeedIT toolkit have been released: The classic version provides a conjugate gradient solver, and the extreme edition provides optimized CG, BiCGSTAB, diagonal preconditioner, memory management, and heuristic-based analysis of input matrices.
March 23rd, 2010
Version 1.2 of Thrust, an open-source template library for developing CUDA applications, has been released. Modeled after the C++ Standard Template Library (STL), Thrust brings a familiar abstraction layer to the realm of GPU computing. This version adds several new features, including:
The Thrust web page provides a quick-start guide, online documentation, many examples and introductory slides. Thrust is open-source software distributed under the OSI-approved Apache License v2.0.
March 23rd, 2010
Palix Technologies has introduced a new Computational Fluid Dynamics (CFD) product called ANDSolver that has been designed from the ground up to use Graphics Processing Units (GPUs) for fast and efficient aerodynamic analysis. Although developing and running applications to use multiple CPUs is a well established practice for high performance science and engineering simulations, a newer trend towards using GPUs for computation promises faster results with lower hardware acquisition and operating costs. ANDSolver delivers on that promise with up to a 10x speedup compared to a typical quad core CPU. This level of performance is unique in that it is achieved on unstructured meshes which have traditionally not been considered amenable to GPUs because of the memory access patterns. However, based on an innovative algorithm design to maximize the performance of the NVIDIA CUDA architecture, the ease and flexibility of unstructured meshing can now be used on high-performance, cost-effective GPUs.
A limited number of additional registrants will be accepted prior to our first production release in Q2 2010. More information can be found at http://www.palixtech.com for our current beta testing program.
March 20th, 2010
We present our effort in developing an open-source GPU (graphics processing units) code library for the MATLAB Image Processing Toolbox (IPT). We ported a dozen of representative functions from IPT and based on their inherent characteristics, we grouped these functions into four categories: data independent, data sharing, algorithm dependent and data dependent. For each category, we present a detailed case study, which reveals interesting insights on how to efficiently optimize the code for GPUs and highlight performance-critical hardware features, some of which have not been well explored in existing literature. Our results show drastic speedups for the functions in the data-independent or data-sharing category by leveraging hardware support judiciously; and moderate speedups for those in the algorithm-dependent category by careful algorithm selection and parallelization. For the functions in the last category, fine-grain synchronization and data-dependency requirements are the main obstacles to an efficient implementation on GPUs.
(J. Kong, et. al., “Accelerating MATLAB Image Processing Toolbox Functions on GPUs”, Proceedings of the Third Workshop on General-Purpose Computation on Graphics Processing Units (GPGPU-3), Pittsburgh, PA. Apr. 2010. Source code is available here.)
March 20th, 2010
NVIDIA has released version 3.0 of the CUDA Toolkit, providing developers with tools to prepare for the upcoming Fermi-based GPUs. Highlights of this release include:
- Support for the new Fermi architecture, with:
- Native 64-bit GPU support
- Multiple Copy Engine support
- ECC reporting
- Concurrent Kernel Execution
- Fermi HW debugging support in cuda-gdb
- Fermi HW profiling support for CUDA C and OpenCL in Visual Profiler
- C++ Class Inheritance and Template Inheritance support for increased programmer productivity
- A new unified interoperability API for Direct3D and OpenGL, with support for:
- OpenGL texture interop
- Direct3D 11 interop support
- CUDA Driver / Runtime Buffer Interoperability, which allows applications using the CUDA Driver API to also use libraries implemented using the CUDA C Runtime such as CUFFT and CUBLAS.
- Read the rest of this entry »
March 9th, 2010
Heterogeneous systems, systems with multiple processors tailored for specialized tasks, are challenging programming environments. While it may be possible for domain experts to optimize a high performance application for a very specific and well documented system, it may not perform as well or even function on a different system. Developers who have less experience with either the application domain or the system architecture may devote a significant effort to writing a program that merely functions correctly. We believe that a comprehensive analysis and modeling framework is necessary to ease application development and automate program optimization on heterogeneous platforms.
This paper reports on an empirical evaluation of 25 CUDA applications on four GPUs and three CPUs, leveraging the Ocelot dynamic compiler infrastructure which can execute and instrument the same CUDA applications on either target. Using a combination of instrumentation and statistical analysis, we record 37 different metrics for each application and use them to derive relationships between program behavior and performance on heterogeneous processors. These relationships are then fed into a modeling framework that attempts to predict the performance of similar classes of applications on different processors. Most significantly, this study identifies several non-intuitive relationships between program characteristics and demonstrates that it is possible to accurately model CUDA kernel performance using only metrics that are available before a kernel is executed.
(Andrew Kerr, Gregory Diamos and Sudakhar Yalamanchili: “Modeling GPU-CPU Workloads and Systems”. Proceedings of the Third Workshop on General-Purpose Computation on Graphics Processing Units (GPGPU-3), Pittsburgh, PA. Apr. 2010. PDF Link.)
Yellow Dog Enterprise Linux for CUDA (YDEL for CUDA) is an open source, Linux operating system built for faster, easier, and more reliable GPU Computing. YDEL for CUDA, released and supported by Fixstars, goes beyond the basic Linux OS and integrates support for GPUs, NVIDIA CUDA, and GPU development tools.
From the YDEL for CUDA website:
Key benefits of Yellow Dog Enterprise Linux for CUDA:
- YDEL for CUDA users can experience up to a 9% performance improvement in some applications.
- Comprehensive support is offered to paid subscriptions with our skilled team able to assist you with both Linux and CUDA.
- YDEL’s unparalleled integrations means everything you need to write and run CUDA applications is included and configured.
- YDEL includes multiple versions of CUDA and can easily switch between them via a setting in a configuration file or an environment variable.
- Never worry about updates affecting your system, Fixstars offers YDEL users greater reliability with our strenuous test procedures that validate GPU computing functionality and performance.
For more information, visit the YDEL for CUDA website.
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