Implementation Techniques Building Scalable Cluster-Based 3D Graphics Engines

构建可扩展的基于集群的 3D 图形引擎的实现技术

• 批准号: 9907485
• 负责人: Tzi-Cker Chiueh
• 金额: $ 29.58万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-01-01 至 2004-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9907485&HistoricalAwards=false
• 关键词: Implementation; Techniques; Building; Scalable; Cluster

The most apparent architectural trends in high-performance computing in the recent years is the emergence of cluster or network of workstations (NOW) architectures, where a set of off-the-shelf high-performance desktop machines connected by a high-speed system-area interconnect can efficiently execute parallel programs and sometimes out-perform custom-built multiprocessors or multicomputers that axe orders of magnitude more expensive. The key driving force behind cluster computing is the unstoppable advances in microprocessor performance, which in turn is enabled by the economies of scale in the PC industry. We are witnessing a similar technological revolution in 3D graphics hardware arena. The performance of low-cost 3D graphics cards is improving at an astonishing rate of 80% per year within the last five years, and if continuing at this rate, is expected to out-pace high-end graphics engines such as those from Sili-con Graphics in 2003. Despite these incredible performance progresses, most of these graphics cards remain relatively inexpensive. The central research questions that this project aims to answer are whether it is feasible to build parallel 3D graphics engines that are based on the clustering architecture and 3D graphics cards and whose overall performance can scale with the number of CPUs/graphics processors, and if so, what the enabling algorithmic and architectural techniques axe. The project will address these questions by designing, implementing, and evaluating efficient software for a cluster-based parallel 3D graphics system called Sunder, which is built on top of Linux and Mesa.

近年来，高性能计算最明显的架构趋势是出现了工作站集群或网络（NOW）架构，其中一组现成的高性能台式机通过高速系统区域互连连接，可以有效地执行并行程序，有时甚至超过定制的多处理器或多计算机。集群计算背后的关键驱动力是微处理器性能的不可阻挡的进步，而这反过来又是由PC行业的规模经济所实现的。我们正在目睹3D图形硬件竞技场中类似的技术革命。在过去的五年中，低成本3D图形卡的性能以每年80%的惊人速度提高，如果继续保持这种速度，预计将超过高端图形引擎，如2003年的Sili-con Graphics。尽管这些令人难以置信的性能进步，大多数这些显卡仍然相对便宜。该项目旨在回答的中心研究问题是，构建基于集群架构和3D图形卡的并行3D图形引擎是否可行，并且其整体性能可以随CPU/图形处理器的数量而扩展，如果是这样，则启用算法和架构技术是什么。该项目将解决这些问题，设计，实施和评估高效的软件，基于集群的并行三维图形系统称为Sunder，这是建立在Linux和梅萨。