Graph Layout on the GPU

May 25th, 2008

A graph is an ordered pair G=(V,E) where V is a set of nodes and E is a set of edges connecting nodes. Graph drawing addresses the problem of creating geometric representations of graphs. Unlike matrices or images, graphs are unstructured and hence graph layout may not seem to be suitable for acceleration on the GPU. These papers present two GPU-accelerated graph drawing algorithms which are able to quickly compute aesthetic layouts of large graphs. One is for the layout of a single graph and the other is for computing stable layouts of a sequence of graphs. Speedups of 5.5x to 17x relative to a CPU implementation are demonstrated. (Yaniv Frishman and Ayellet Tal, Multi-Level Graph Layout on the GPU, IEEE Transactions on Visualization and Computer Graphics (Proceedings Information Visualization 2007), 13(6):1310-1317, 2007)
(Yaniv Frishman and Ayellet Tal, Online Dynamic Graph Drawing, accepted to IEEE Transactions on Visualization and Computer Graphics)

gDEBugger V4.1 Adds Geometry Shaders Support and new ATI Performance Metrics Integration

May 25th, 2008

The new gDEBugger V4.1 adds Geometry Shader Support and enables developers to view allocated geometry shader objects, shader source code and properties. It also allows the developer to Edit and Continue shaders on the fly. Support for the new ATI (AMD) driver performance metrics infrastructure has been added. This integration enables users to view ATI performance metrics such as hardware utilization, vertex wait for pixel, pixel wait for vertex, overdraw and more. These performance metrics together with gDEBugger’s Performance Analysis Toolbar provide a powerful solution for locating graphics system performance bottlenecks. gDEBugger, an OpenGL and OpenGL ES debugger and profiler, traces application activity on top of the OpenGL API, letting programmers see what is happening within the graphics system implementation to find bugs and optimize OpenGL application performance. gDEBugger runs on Microsoft Windows and Linux operating systems. (

PRACE award presented to young scientistat ISC’08 for GPGPU work

May 20th, 2008

From this article: “PRACE, Partnership for Advanced Computing in Europe, awarded a prize for the best scientific paper submitted to ISC’08 by a European student or young scientist on petascaling. The authors of the award winning paper are Stefan Turek, Dominik Göddeke, Christian Becker, Sven H.M. Buijssen and Hilmar Wobker from the Institute of Applied Mathematics, Dortmund University of Technology, Germany. Their work, UCHPC : UnConventional High Performance Computing for Finite Element Simulations, was selected by the ISC’08 Award Committee, headed by Michael Resch, High Performance Computing Center Stuttgart. Achim Bachem, Chairman of the Board Forschungszentrum Jülich and PRACE coordinator presented the PRACE Award at the ISC’08 opening ceremony in Dresden on Wednesday, 18 June. Dominik Göddeke, Ph.D. student in the team of Professor Stefan Turek will receive a sponsorship for the participation in a conference relevant to Petascale computing.” Dominik has been an active GPGPU researcher for several years, and is one of the most active and helpful contributors to the forums. (PRACE award presented to young scientist at ISC’08)

GRIP – A Rugged GPU Accelerated Image Processing System

April 23rd, 2008

Vision4ce launched a new line of General-purpose Rugged Image Processing (GRIP) products at the recent SPIE Defense and Security Symposium in Orlando from 18th-20th March 2008. The GRIP-Beta showed cutting edge GPGPU-based image processing demonstrations, analog and Gigabit Ethernet video streams and the robust functionality in the Gripworkx image processing framework. The Vision4ce team with GRIP now addresses numerous rugged embedded computing challenges with a cost effective, readily available rugged solution that might normally be served by more expensive and lengthy FPGA approaches. See for more information.

CUDPP 1.0a Adds Segmented Scan and Sparse Matrix-Vector Multiplication

April 20th, 2008

Version 1.0 alpha of CUDPP, the CUDA Data-Parallel Algorithms Library, has been released. This version adds the segmented scan algorithm and sparse matrix-vector multiplication to CUDPP’s repertoire. Other new features include an improved “plan”-based configuration interface, an improved scan algorithm for higher performance, support for more inclusive scans and more scan operators, an improved stream compaction interface. In addition, CUDPP 1.0a adds support for CUDA 2.0 and the Windows Vista and Mac OS X (10.5.2 and higher) operating systems. CUDPP works with NVIDIA CUDA versions 1.1 and higher.

Shader Maker: a simple, truly cross-platform GLSL editor

April 20th, 2008

Shader Maker is a simple, cross-platform GLSL editor. It works on Windows, Linux, and Mac OS X. Shader Maker provides the basics of a shader editor, such that students can get started with writing their own shaders as quickly as possible. This includes: syntax highlighting in the GLSL editors; vertex, fragment, and geometry shader editors; interactive editing of uniform variables; light source parameters; pre-defined simple shapes (e.g., torus); a simple OBJ loader; and more. (Shader Maker)

SHARCNET Symposium on GPU and CELL Computing

April 20th, 2008

University of Waterloo
Waterloo, Ontario, Canada
May 27th 2008

This one-day symposium will explore the use of GPUs, CELL processors, FPGAs and multi-core CPUs for large-scale scientific computing. The symposium program includes invited talks on the LANL Roadrunner CELL supercomputer, the RapidMind platform for multicore CPUs and many-core accelerators, and NVIDIA CUDA. For more information, see

gDEBugger V4.0 Adds Linux Support and a Buffer Viewer

April 2nd, 2008

The new gDEBugger V4.0 introduces gDEBugger Linux. This new exciting product adds 32-bit and 64-bit Linux Support, bringing all of gDEBugger’s debugging and profiling abilities to the Linux OpenGL developers’ world. A new Texture and Buffer Viewer has been added. This Viewer allows you to view textures, static buffers and pbuffers as images or raw data in its original format, including non-RGB data formats (float, depth, integer, luminance, etc). This version also includes significant performance improvements. gDEBugger, an OpenGL and OpenGL ES debugger and profiler, traces application activity on top of the OpenGL API to let programmers see what is happening within the graphics system implementation to find bugs and optimize OpenGL application performance. (

CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment

April 2nd, 2008

The Smith-Waterman algorithm has been available for more than 25 years. It is based on a dynamic programming approach that explores all the possible alignments between two biological sequences; as a result it returns the optimal local alignment. Unfortunately, the computational cost is very high, requiring a number of operations proportional to the product of the length of two sequences. This paper by Svetlin Manavski and Giorgio Valle describes SmithWaterman-CUDA, an open-source project to perform fast sequence alignment on the GPU. Although the software performs the optimal Smith-Waterman alignment it is faster than heuristics approaches like FASTA and BLAST. The tests on protein data banks show up to 30x speed up related to reference CPU implementations. (Svetlin A. Manavski, Giorgio Valle, CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment, BMC Bioinformatics 2008, 9(Suppl 2):S10 (26 March 2008))

Relational Joins on Graphics Processors

April 2nd, 2008

Abstract: “We present a novel design and implementation of relational join algorithms for new-generation graphics processing units (GPUs). Taking advantage of GPU features, we design a set of data-parallel primitives such as split and sort, and use these primitives to implement indexed or non-indexed nested-loop, sort-merge and hash joins. Our algorithms utilize the high parallelism as well as the high memory bandwidth of the GPU, and use parallel computation and memory optimizations to effectively reduce memory stalls. We have implemented our algorithms on a PC with an NVIDIA G80 GPU and an Intel quad-core CPU. Our GPU-based join algorithms are able to achieve a performance improvement of 2-7X over their optimized CPU-based counterparts. (Bingsheng He, Ke Yang, Rui Fang, Mian Lu, Naga K. Govindaraju, Qiong Luo, and Pedro V. Sander. Relational Joins on Graphics Processors. ACM SIGMOD 2008.)

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