The CUDA driver and Toolkit installation are required before running the precompiled examples or compiling the example source code.
必需安装CUDA驱动和CUDA工具包，此后才可运行预编译的例程或编译样例源代码。
Please note that the CUDA Debugger for Linux has been tested only on 32-bit Red hat Enterprise Linux (RHEL) 5.x but may work on other distros as well.
注意：Linux平台下的CUDA调试程序仅在32位的Linux红帽企业版5 .x (RHEL)上测试通过，可能也支持Linux其他已发行版本。
If you decide to install the nvidia proprietary graphics card drivers (you can do this with a few clicks), CUDA (the nvidia hardware acceleration API) and OpenCL support is installed too.
如果你决定安装Nvidia专有图形卡驱动程序(实现这个，只需点击数下即可)，那么CUDA (Nvidia硬件加速api)和OpenCL支持也会同时安装。
The IPN250 combines a single GT240 96-core CUDA GPU with an Intel Core2 Duo host processor operating at 2.26GHz and 8 GBytes of DDR3 SDRAM to deliver up to 390 GFLOPS of performance per card slot.
IPN250结合了一个GT240 96核心CUDAGPU，英特尔酷睿2.26GHz双核处理器和8G DDR3SDRAM，可以提供每卡高达390GFLOPS的运算性能。
Appendix B lists the mathematical functions supported in CUDA.
附录b列举cuda中支持的数学函数。
Is this a CUDA thread synchronization issue or something else?
这是CUDA线程同步问题还是其他什么？
How to query the current performance state of your GPU with CUDA?
如何查询你的GPU使用CUDA的当前性能状态？
In this paper, we implement an efficient matrix multiplication on GPU using NVIDIA's CUDA.
本文使用NVIDIA的CUDA在GPU上实现了一个高效的矩阵乘法。
Using CUDA C language, using CUDA texture memory, image stretching parallel implementation.
说明：使用CUDAC语言，利用CUDA纹理内存，实现图像拉伸的并行实现。
This document is divided into the following chapters: chapter 1 is an introduction to CUDA and GPU.
本文档分为以下几个章节：第1章是CUDA和GPU的简介。
Achieve a highly paralleled algorithm to calculate the simplification error of triangular meshes by using CUDA.
利用CUDA实现了高度并行化的网格模型简化误差计算算法。
After experiments, comparing CPU 's computing power can be found, CUDA' s ability to process data in parallel is very strong.
在经过实验之后，对比CPU的计算能力可以发现，CUDA在并行处理数据的能力非常强大。
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。
The core part of ray tracing computation will be modified to adapt the advantages and limitations of CUDA so as to amplify the power of parallelization.
光线追踪的核心计算部分则根据CUDA优势与限制进行适应性改造，发挥尽可能大的并行能力。
Therefore, in order to improve solving efficiency of packing problem fundamentally, we design parallel algorithm with the structure of CUDA based on GPU.
因此，为了从根本上提高布局问题的求解效率，本文采用基于GPU结构的CUDA技术设计并行算法。
Aiming at the low rate of route planning due to huge, complex 3d data, this paper proposes a 3d data field route planning based on Compute Unified Device Architecture (CUDA).
针对数据量庞大、复杂的三维数据场环境下航路规划速度偏低的问题，提出一种基于统一计算设备架构(CUDA)的三维数据场航路规划方法。
Aiming at the low rate of route planning due to huge, complex 3d data, this paper proposes a 3d data field route planning based on Compute Unified Device Architecture (CUDA).
针对数据量庞大、复杂的三维数据场环境下航路规划速度偏低的问题，提出一种基于统一计算设备架构(CUDA)的三维数据场航路规划方法。
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