数据库应用系统是数据密集型计算机应用，它的核心是数据库的设计。
Database application system is a computer application of type of dense data, whose kernel is the design of database.
网格的目标是实现异构资源共享，及用来解决大规模计算或数据密集型计算等问题。
The goal of Grid is for realizing different kinds of resource-sharing and for solving calculating on a large scale or such problems as the data are calculate intensively.
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。
Abstract CUDA is a parallel computing architecture introduced by NVIDIA, it mainly used for large scale data-intensive computing.
设计了利用因特网上多个集群的空闲资源来处理生物医学领域数据密集型计算的新药研发网格(DDG)。
Utilizing the idle resources donated by the clusters that scatter over the Internet, drug discovery grid (DDG) can implement efficient data-intensive biologic applications.
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。
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.
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。
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.
支持Dryad(大伸缩量，数据密集型的并行计算)(即将提供)。
Support for Dryad (large scale, data-intensive parallel programming) (soon).
伴随着这个，又有了使用的数据分析新方法，例如，马尔科夫链，蒙特卡洛模拟这些大型计算机密集型算法。
Accompanying this have been new approaches to data analysis using, for example, Markov Chain Monte Carlo simulations that are hugely computer intensive.
网络技术的进步为数据密集或计算密集型的应用提供了大规模、分布式的处理能力。
Advances in network technology provide large scale distributed computing capacity for those applications that deal with heavy computation task.
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。
Recently data intensive applications have been focused as one of the most important applications for high performance computing.
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。
Data-intensive scientific and engineering applications often require the efficient transfer of terabytes or even petabytes of data in wide-area, distributed computing environments.
海量遥感图像快速处理是遥感图像处理与分析的重要任务之一，它既是数据密集型，也是计算密集型的工作。
Huge quantity remote sensing image processing is one of the important tasks of remote sensing image processing and analysis, it is not only data intensive work, but also computation intensive work.
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.
由于光网络具有大容量、低延时、动态控制以及任意粒度带宽等特性，把光网络与分布式计算系统结合，为数据密集型应用提供很好的应用环境。
On the other hand, optical networking can offer huge capacity and relatively low latency, as well as dynamic control and allocation of bandwidth at various granularities.
由于光网络具有大容量、低延时、动态控制以及任意粒度带宽等特性，把光网络与分布式计算系统结合，为数据密集型应用提供很好的应用环境。
On the other hand, optical networking can offer huge capacity and relatively low latency, as well as dynamic control and allocation of bandwidth at various granularities.
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