GPGPU

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 does not seem to be suitable for acceleration on the GPU. We 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)

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. (http://www.gremedy.com)

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 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.( http://cg.in.tu-clausthal.de/publications.shtml#shader_maker )

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 www.vision4ce.com for more information.

In conjunction with The 2008 International Conference on Computational Science and Its Applications (ICCSA 2008, UCHPC '08 is a technical session on UnConventional High Performance Computing. This session focuses on uses of hardware for HPC that was not originally intended for HPC. UCHPC invites papers on all aspects of unconventional HPC and its related areas describing either proven and tested solutions or novel ideas and concepts. Topics for submissions include but are not limited to the following areas: cluster solutions; performance issues and scalability; innovative use of hardware and software; and HPC on GPUs, Cell BE, FPGAs and other hardware. Please see the Call for Papers for more information.

Slides from the 2007 AstroGPU conference, held at the Institute for Advanced Study in Princeton last November, have been posted to the AstroGPU Website.

This paper reviews the latest developments of displacement mapping algorithms implemented on the vertex, geometry, and fragment shaders of graphics cards. Displacement mapping algorithms are classified as per-vertex and per-pixel methods. Per-pixel approaches are further categorized as safe algorithms that aim at correct solutions in all cases, to unsafe techniques that may fail in extreme cases but are usually much faster than safe algorithms, and to combined methods that exploit the robustness of safe and the speed of unsafe techniques. The paper discusses the possible roles of vertex, geometry, and fragment shaders to implement these algorithms. Then the particular GPU based bump, parallax, relief, sphere, horizon mapping, cone stepping, local ray tracing, pyramidal and view-dependent displacement mapping methods, as well as their numerous variations are reviewed providing also implementation details of the shader programs. The paper presents these methods using uniform notation and also points out when different authors referred to similar concepts differently. In addition to basic displacement mapping, self-shadowing and silhouette processing are also reviewed. Based on the authors' experiences gained having re-implemented these methods, their performance and quality are compared, and the advantages and disadvantages are fairly presented. (Displacement Mapping on the GPU - State of the Art László Szirmay-Kalos and Tamás Umenhoffer. Computer Graphics Forum. 2008.)

This article by D’Souza et al. explores large scale Agent-Based Model(ABM) simulation on the GPU. Agent-based modeling is a technique which has become increasingly popular for simulating complex natural phenomena such as swarms and biological cell colonies. An ABM describes a dynamic system by representing it as a collection of communicating, concurrent objects. Current ABM simulation toolkits and algorithms use discrete event simulation techniques and are executed serially on a CPU. This limits the size of the models that can be handled efficiently. In this paper we present a series of efficient data-parallel algorithms for simulating ABMs. These include methods for handling environment updates, agent interactions and replication. Important techniques presented in this work include a novel stochastic allocator which enables parallel agent replication in O(1) average time and an iterative method to handle collision among agents in the spatial domain. These techniques have been implemented on a modern GPU (GeForce 8800GTX), resulting in a substantial performance increase. The authors believe that their system is the first completely GPU-based ABM simulation framework. (D’Souza R., Lysenko, M., Rahmani, K., SugarScape on steroids: simulating over a million agents at interactive rates. Proceedings of the Agent2007 conference, Chicago, IL. 2007.)

Abstract: "The porting of two- and three-dimensional Euler solvers from a conventional CPU implementation to the novel target platform of the Graphics Processing Unit (GPU) is described. The motivation for such an effort is the impressive performance that GPUs offer: typically 10 times more floating point operations per second than a modern CPU, with over 100 processing cores and all at a very modest financial cost. Both codes were found to generate the same results on the GPU as the FORTRAN versions did on the CPU. The 2D solver ran up to 29 times quicker on the GPU than on the CPU; the 3D solver 16 times faster." (Tobias Brandvik and Graham Pullan, Acceleration of a 3D Euler Solver Using Commodity Graphics Hardware. 46th AIAA Aerospace Sciences Meeting and Exhibit. January, 2008.)

Abstract: "N-body algorithms are applicable to a number of common problems in computational physics including gravitation, electrostatics, and fluid dynamics. Fast algorithms (those with better than O(N²) performance) exist, but have not been successfully implemented on GPU hardware for practical problems. In the present work, we introduce not only best-in-class performance for a multipole-accelerated treecode method, but a series of improvements that support implementation of this solver on highly-data-parallel graphics processing units (GPUs). The greatly reduced computation times suggest that this problem is ideally suited for the current and next generations of single and cluster CPU-GPU architectures. We believe that this is an ideal method for practical computation of largescale turbulent flows on future supercomputing hardware using parallel vortex particle methods. (Mark J. Stock and Adrin Gharakhani, "Toward efficient GPU-accelerated N-body simulations," in 46th AIAA Aerospace Sciences Meeting and Exhibit, AIAA 2008-608, January 2008, Reno, Nevada.)

Neoptica, a computer graphics and parallel programming model startup founded by Matt Pharr and Craig Kolb, was acquired by Intel on October 19th. Beyond3D has posted a short writeup about the acquisition. Several of Neoptica's employees have in the past been involved in GPGPU development.

From the introduction: "Processors architecture is evolving towards more software-exposed parallelism through two features: more cores and wider SIMD ISA. At the same time, graphics processors (GPUs) are gradually adding more general purpose programming features. Several software development challenges arise from these trends. First, how do we mitigate the increased software development complexity that comes with exposing parallelism to the developer? Second, how do we provide portability across (increasing) core counts and SIMD ISA? Ct is a deterministic parallel programming model intended to leverage the best features of emerging general-purpose GPU (GPGPU) programming models while fully exploiting CPU flexibility. A key distinction of Ct is that it comprises a top-down design of a complete data parallel programming model, rather than being driven bottomup by architectural limitations, a flaw in many GPGPU programming models." (Flexible Parallel Programming for Terascale Architectures with Ct)

novel scientific and engineering applications of GPUs. Papers submitted for special sessions will be peer-reviewed with the same criteria used for the contributed papers. Submission deadline is 7 January 2008. (WCCI-2008 Special Session Computational Intelligence on Consumer Games and Graphics Hardware CIGPU-2008)

This Ph.D. thesis by Jansen describes a GPGPU development system that is embedded in the C++ programming language using ad-hoc polymorphism (i.e. operator overloading). While this technique is already known from the Sh library and the RapidMind Development Platform, GPU++ uses a more generic class interface and requires no knowledge of GPU programming at all. Furthermore, there is no separation between the different computation units of the CPU and GPU - the appropriate computation frequency is automatically chosen by the GPU++ system using several optimization algorithms. ("GPU++: An Embedded GPU Development System for General-Purpose Computations". Thomas Jansen. Ph.D. Thesis, University of Munich, Germany.