Abstract CUDA is a parallel computing architecture introduced by NVIDIA, it mainly used for large scale data-intensive computing.
摘要CUDA是一种由NVIDIA推出的并行计算架构，非常适合大规模数据密集型计算。
Data Grid is widely used in data-intensive computing, which provide rapid information retrieval and data access support for large amounts of distributed storage data resources.
数据网格广泛应用于数据密集型计算，为大数据量、分布存储数据资源提供快速的信息检索和数据访问的支持。
While the storage computing in the grid is well suited for data-intensive storage, it is not economically suited for storing objects as small as 1 byte.
虽然网格中的存储计算非常适合数据密集型存储，但是存储一个字节大小的对象从经济上来说不合适。
Recently data intensive applications have been focused as one of the most important applications for high performance computing.
数据密集型程序有着广泛的应用，已经成为高性能计算中最重要的应用程序之一。
The paper introduces the research trend for the access bottleneck between the data intensive computing architecture and memory.
文章介绍了计算密集型体系解决存储器访问瓶颈的研究趋势。
Data-intensive scientific and engineering applications often require the efficient transfer of terabytes or even petabytes of data in wide-area, distributed computing environments.
数据密集型的科学与工程应用(如计算力学数值模拟、气象预测)需要在广域、分布式的计算环境中快速安全的传输海量的数据。
Considering most GIS buffer analysis is data intensive computing, the task was decomposed according to the map layer and geographic spatial area.
考虑到大多数地理信息系统中缓冲区分析数据的特点，提出了基于图层和地理空间区域的任务分解方法。
Compared with CPU, GPU was designed for compute-intensive, highly parallel computing, which enabled more transistors to be used for data processing, rather than data caching or flow control.
与CPU相比，GPU专为高度并行化和密集型的计算而设计，它能使更多的晶体管用于数据处理，而非数据缓存或流控制。
Compared with CPU, GPU was designed for compute-intensive, highly parallel computing, which enabled more transistors to be used for data processing, rather than data caching or flow control.
与CPU相比，GPU专为高度并行化和密集型的计算而设计，它能使更多的晶体管用于数据处理，而非数据缓存或流控制。
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