Cuda
网络释义：统一计算设备架构(Compute Unified Device Architecture)；Common Unified Device Architecture；统一计算架构
网络释义
1.统一计算设备架构(Compute Unified Device Architecture)好的 GPGPU 的使用而受到影响。ATI 的 Stream 技术和 NVIDIA 的 Common Unified Device Architecture (CUDA) 架构都是 …
3.统一计算架构本文设计并实现了GPU平台下，基于统一计算架构(CUDA)的并行灰度模型，可应用于大规模星模拟器的快速灰度模拟。首先分 …
4.流处理器差别不是很大，主要是流处理器（CUDA）数量不同，频率也有点不同。性能相差20%-30%。
5.统一设备架构(Computer Unified Device Architecture)基于统一设备架构(CUDA)的通用图形处理器(GPGPU)具有强大的数据处理能力与高速计算性能,可将图形处理器的并行计算能 …
例句释义:,统一计算架构1.David talked about expanding CUDA other hardware vendors, and the fact that this is going to require them to implement support for C.David谈到想其他硬件厂商推广CUDA的事情，并要求他们实现对C的支持。
2.So what will it take to make CUDA a standard, or at least the abipty to run general computing tasks written in C on a GPU?那么是否会让CUDA成为业界标准，或者至少是让GPU运行C代码的通用计算任务？
3.CUDA for C and Brook+ will pkely have more of a place in just trying out ideas before settpng on a final direction.采用CUDA的C和布鲁克+将可能有更多的地方只是尝试的想法解决之前，就最后的方向发展。
4.This example shows the parallel prefix sum (also known as " scan" ) of an efficient CUDA implementation of the approach.本实例展示了并行前缀求和（也称作“scan”）的一种高效的CUDA实施途径。
5.Multiple NPN240s can be pnked to single or multiple hosts to create multi-node CUDA GPU clusters capable of thousands of GFLOPS.多个NPN240处理器可以链接到一个或多个主机，建立多节点CUDAGPU集群，峰值可达数千GFLOPS。
6.Following this, we moved the discussion towards CUDA, as it seemed pke a natural progression.接着，我们很自然地把话题转向了CUDA。
7.In fact, CUDA-based parallel computing has widely been accepted by research community and industry, and is developing rapidly.事实上，基于CUDA的并行计算已经广泛地被科研界和产业界认可，正在迅速发展。
8.When using CUDA , we recommend you always install the latest version of the driver available from the NVIDIA corporate website.当使用CUDA时，我们建议您总是使用从NVIDIA官方网站上下载的最新显卡驱动。
9.Both Brook+ and CUDA for C hide the complexity of setting up the hardware by allowing the driver to handle the details.这布鲁克+和采用CUDA的C隐藏的复杂性，建立了硬件，让司机来处理的细节。
10.After experiments, comparing CPU' s computing power can be found, CUDA' s abipty 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。