Workshop: Data-Parallel Programming Models for Many-Core Architectures

March 7th, 2007

Data-parallel programming models are emerging as an extremely attractive model for parallel programming, driven by several factors. Through deterministic semantics and constrained synchronization mechanisms, they provide race-free parallel-programming semantics. Furthermore, data-parallel programming models free programmers from reasoning about the details of the underlying hardware and software mechanisms for achieving parallel execution and facilitate effective compilation. Finally, efforts in the GPGPU movement and elsewhere have matured implementation technologies for streaming and data-parallel programming models to the point where high performance can be reliably achieved.

This workshop gathers commercial and academic researchers, vendors, and users of data-parallel programming platforms to discuss implementation experience for a broad range of many-core architectures and to speculate on future programming-model directions. Participating institutions include AMD, Electronic Arts, Intel, Microsoft, NVIDIA, PeakStream, RapidMind, and The University of New South Wales. (Link to Call for Participation, Data-Parallel Programming Models for Many-Core Architectures)

A (Revised) Survey of General-Purpose Computation on Graphics Hardware

March 6th, 2007

With their upcoming publication in Computer Graphics Forum, Owens et al. have revised their 2005 comprehensive survey of the history and state of the art in GPGPU. It describes, summarizes and analyzes the latest research in mapping general-purpose computation to graphics hardware. The report begins with the technical motivations that underlie general-purpose computation on graphics processors (GPGPU) and describe the hardware and software developments that have led to the recent interest in this field. The authors describe the techniques used in mapping general-purpose computation to graphics hardware, and survey and categorize the latest developments in general-purpose application development on graphics hardware. (A Survey of General-Purpose Computation on Graphics Hardware. John D. Owens, David Luebke, Naga Govindaraju, Mark Harris, Jens Krüger, Aaron E. Lefohn, Timothy J. Purcell, in “Computer Graphics Forum”, Volume 26, number 1, pp 80-113. 2007. To appear.)

NVIDIA Releases CUDA for GPU Computing

February 16th, 2007

A beta of NVIDIA’s CUDA development environment, NVIDIA’s new technology for computing with GPUs, is now posted on This beta release of CUDA contains a C compiler for the GPU and an SDK with examples to get you started coding for the GPU. From the press release:

GPU Computing with CUDA is a new approach to computing where hundreds of on-chip processors simultaneously communicate and cooperate to solve complex computing problems. Applications that require mathematically intensive computing on large amounts of data are ideal targets for GPU Computing. NVIDIA NVIDIA’s CUDA technology is available in GeForce 8800 graphics products and future NVIDIA Quadro Professional Graphics solutions based on 8-series (G8X) GPUs. Developers are invited to download the beta version of the CUDA Software Developers Kit (SDK) and C compiler for Windows XP and Linux (RedHat Release 4 Update 3) from the NVIDIA Developer Web site at GPU Computing Forums for news, discussion and programming tips are also available at

Supercomputing ’06 GPGPU Workshop Proceedings Posted

February 2nd, 2007

The proceedings of the workshop “General-Purpose GPU Computing: Practice And Experience” held at SuperComputing 2006 are now posted. The proceedings include PDFs of the workshop presentations and posters. (

Converging Design Features in CPUs and GPUs

January 22nd, 2007

This article at HPC Wire by Matthew Papakipos, CTO of PeakStream Technologies, discusses the convergence of CPU and GPU architectures, the programming challenges architecture changes pose, and possible solutions to these challenges.

Ph.D. Dissertation: Glift Generic GPU Data Structures, by Aaron Lefohn

January 18th, 2007

This Ph.D. dissertation by Aaron Lefohn at the University of California, Davis describes the Glift GPU data structure abstraction and its application to both GPU-based data-parallel and interactive rendering algorithms. The applications include octree 3D painting, adaptive shadow maps, resolution matched shadow maps, heat-diffusion depth-of-field, and a GPU-based direct solver for tridiagonal linear systems. While much of this work has been posted previously, this dissertation contains a more in-depth discussion of the Glift data structure library and introduces several GPGPU and rendering algorithms that are not yet published. This dissertation demonstrates that a data structure abstraction for GPUs can simplify the description of new and existing data structures, stimulate development of complex GPU algorithms, and perform equivalently to hand-coded implementations. The dissertation also presents a case that future interactive rendering solutions will be an inseparable mix of general-purpose, data-parallel algorithms and traditional graphics programming. (Aaron Lefohn, “Glift: Generic Data Structures for Graphics Hardware”, Ph.D. dissertation, Computer Science Department, University of California Davis, September 2006.)

Interactive Depth of Field Using Simulated Diffusion on a GPU

January 18th, 2007

This Pixar Animation Studios Technical Report by Kass, Lefohn, and Owens describes a GPU-based data-parallel direct tridiagonal linear solver. To the authors’ knowledge, this is the first reported direct, linear-time tridiagonal GPU solver. The solver is used to implement a new heat-diffusion-based depth-of-field preview algorithm; and the paper describes solving thousands of tridiagonal systems, each with hundreds of elements, on the GPU at interactive rendering rates. The alternating direction implicit solution gives rise to separable spatially varying recursive (infinite-impulse response, IIR) filters that can compute large-kernel convolutions in constant time per pixel while respecting the boundaries between in-focus and out-of-focus objects. Recursive filters have traditionally been viewed as problematic for GPUs, but using the well-established method of cyclic reduction of tridiagonal systems, the authors are able to parallelize the computation and implement an efficient solution in terms of GPGPU primitives. (Michael Kass, Aaron Lefohn, and John Owens. Interactive Depth of Field Using Simulated Diffusion on the GPU, Technical Report #06-01, Pixar Animation Studios, January 2006.)

GPGPU a Disruptive Technology for 2007

January 18th, 2007

An article by David Strom in Information Week includes “Advanced Graphics Processing” in it’s article “5 Disruptive Technologies To Watch in 2007″, and specifically mentions GPGPU and NVIDIA CUDA. “In some cases, the new graphics cards being developed by NVIDIA and ATI (now a part of AMD) will have a bigger impact on computational processing than the latest chips from Intel and AMD.”, writes Strom.

SIGGRAPH 2004 & 2005 GPGPU Course Videos Online

January 4th, 2007

Videos of all presentations in the GPGPU Tutorials held at SIGGRAPH 2004 and SIGGRAPH 2005 are online. These courses are an excellent resource for beginners in GPGPU programming. SIGGRAPH 2004 GPGPU Course (Course Web Page). SIGGRAPH 2005 GPGPU Course (Course Web Page).

New GPGPU Tutorials added to Developer Page

January 4th, 2007

We have added links to some great introductory GPGPU tutorials to the Developer Page. These tutorials, written by Dominik Göddeke from Dortmund University, cover basic GPGPU concepts, parallel reductions, and fast data transfers.

Page 85 of 110« First...102030...8384858687...90100110...Last »