Name
Abstract | ||
|---|---|---|
Handling very large datasets has been a key problem addressed in real-time distributed rendering research. With the advent of the programmable Graphics Processing Unit (GPU), it is now possible and even profitable to move many application-specific computations to be carried out by the GPU. It has been shown that modern GPUs outperform the standard PC-platform CPUs on a broad class of computations by over a factor of seven. Given the low costs and high processing speeds of GPUs, there is a trend towards using clusters of CPU/GPU systems. Configuring and programming these clusters for efficient distribution of data and computations is a major challenge. What are the computations that can be offloaded from the CPU to a GPU? The answer to this question is not simple as it depends on the following four factors: GPU's processing capacity, GPU's internal bandwidth, GPU-CPU communication bandwidth and the external network bandwidth. All these factors are subject to change with every generation of hardware. But additions and alternatives to the traditional data-parallel architectures are now needed to exploit the full capability of such clusters using functional parallelism. In this paper, we present a number of architectural configurations that could be adapted on such clusters. Specifically, we demonstrate use of one such architecture: application of a GPU-based pipelined architecture to our work on real-time processing and rendering of large-point datasets which demands complex computations. We have also introduced a list of application and system parameters that are necessary to determine an optimal distribution of computation on the GPUs of a graphics cluster. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/IPDPS.2005.232 | international parallel and distributed processing symposium |
Keywords | Field | DocType |
very large dataset handling,modern gpus,gpu internal bandwidth,computer graphic equipment,functionality distribution,processing capacity,gpu system,high processing speed,graphics cluster,parallel architectures,gpu-based pipelined architecture,data-parallel architectures,rendering (computer graphics),real-time distributed rendering,gpu-cpu communication bandwidth,internal bandwidth,application parameter,system parameter,parallel rendering,network bandwidth,data handling,very large databases,external network bandwidth,efficient distribution ofdata,gpu processing capacity,programmable graphics processing unit,real-time processing,real-time systems,pipeline processing,hardware,bandwidth,computer applications,parallel processing,computer architecture,distributed computing,real time,central processing unit,real time processing,profitability,graphics,real time systems | Graphics,Central processing unit,Parallel rendering,Computer science,Parallel computing,Bandwidth (signal processing),Graphics processing unit,Rendering (computer graphics),Group method of data handling,Computation | Conference |
ISBN | Citations | PageRank |
0-7695-2312-9 | 7 | 0.56 |
References | Authors | |
18 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ramgopal Rajagopalan | 1 | 7 | 1.58 |
| Dhrubajyoti Goswami | 2 | 107 | 16.04 |
| Sudhir P. Mudur | 3 | 201 | 45.52 |