Each CUDA-capable GPU node includes local DDR3 SDRAM as well as a 16-lane PCI Express? gen2 interface to the system backplane, providing maximum data throughput direct to GPU memory.
每个CUDAGPU节点包括本地的DDR3SDRAM以及一个16通道PCI二代系统的背板接口，直接向GPU内存提供最大的数据流量。
The GPU memory could be considered to be a "cache" of data that the GPU is currently operating on, but many GPU algorithms are designed to operate on more data than can fit in the "cache".
的GPU内存可以被认为是一个“缓存”，GPU目前经营上的数据，但许多GPU算法的设计有更多的数据比操作可以在“缓存”。
A high-end PC GPU and a low-end mobile GPU can be literally hundreds of times performance difference apart. Same is true even on a single platform.
毫不夸张的说，高端PC和低端移动设备的GPU性能可能相差几百倍，甚至在同一个平台上也相差这么大。
Our algorithm takes full advantage of GPU and effectively balances the workload between CPU and GPU.
算法不仅充分发挥了GPU的性能并且有效地均衡了GPU和CPU之间的负载。
Modern Graphics Processing Unit (or GPU) has powerful parallel computing capability, and thus using GPU to accelerate the real time rendering of highly detailed surfaces is an effective approach.
GPU处理具有较高的并行度，利用GPU对细节复杂模型的绘制进行加速是一个有效加速途径。
This paper abstracts a stream execution model of GPU, describes its stream execution process, and implements the discrete cosine transform on GPU.
该文抽象出图形处理器的流执行模型，描述图形处理器流处理机制的执行过程，在图形处理器上实现了二维离散余弦变换。
In the developed cloth simulation system, the entire simulation flow is done on the GPU after some initialization work on the CPU, which avoids extra cost of data transfer between the CPU and the GPU.
在所实现的织物模拟系统中，CPU完成初始化工作之后，整个模拟过程都在GPU上完成，避免了CPU与GPU之间数据传递的额外开销。
On balance, CPU core performance is currently still somewhat more important, but GPU performance is steadily gaining performance as more applications are able to offload specific workloads to the GPU.
在CPU核心性能的平衡，是目前仍较为重要的，但GPU性能不断提高，随着越来越多的的应用程序能够卸载的特定工作负载到GPU。
Many GPU based applications in 3d geometric modeling system have been proposed along with the augmented programmable performance of Graphics Processing Unit (GPU).
图形处理器(GPU)可编程性能的不断提高使得在三维几何造型系统中出现了越来越多的基于GPU的应用。
In recent years, GPU has been used for general purpose computation increasingly, the GPU for video compression has broad application prospects.
近年来GPU逐渐被用于通用计算，将GPU用于视频图像压缩领域具有广阔的应用前景。
Abstract a novel strategy is proposed for fully GPU Implementation of subdivision surfaces, using multi-pass general-purpose computation on GPU to accomplish refinement.
摘要：本文提出一种完全在GPU上实现的细分曲面绘制策略。
As you probably know by now, a GPU can run many things simultaneously and we use to call the parts of the GPU pipelines, but nowadays the term shader is more appropriate.
正如你可能已经知道，支持GPU可以运行许多事情同时，我们要求使用的部分GPU的管道，但现在这个词着色更合适。
As you probably know by now, a GPU can run many things simultaneously and we use to call the parts of the GPU pipelines, but nowadays the term shader is more appropriate.
正如你可能已经知道，支持GPU可以运行许多事情同时，我们要求使用的部分GPU的管道，但现在这个词着色更合适。
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