运行TensorFlow-GPU提示缺少cusolver64_100.dll，，cusparse64_100.dll，，curand64_100.dll丢失的解决方法。下载后放在安装目录下面，如C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin。。亲测CUDA10可用，其他版本不保证

cuda_opencl开发可以参考. ＣＰＵＧＰＵ异构混合并行系统以其强劲计算能力高性价比和低能耗等特点成为新型高性能计算平台但其 复杂体系结构为并行计算研究提出了巨大挑战 ＣＰＵＧＰＵ协同并行计算属于新兴研究领域是一个开放的课题 根据所用计算资源的规模将ＣＰＵＧＰＵ协同并行计算研究划分为三类尔后从立项依据研究内容和研究方法等方 面重点介绍了几个混合计算项目并指出了可进一步研究的方向以期为领域科学家进行协同并行计算研究提供一定 参考

全英文. 大规模并行处理器编程实战(第2版). 我相信语言不是大家学习的障碍. 相当不错，吐血推荐！！！！

I am from the days when computer engineers and scientists had to write assembly language on IBM mainframes to develop high-performance programs. Programs were written on punch cards and compilation was a one-day process; you dropped o your punch-code written program and picked up the results the next day. If there was an error, you did it again. In those days, a good programmer had to understand the underlying machine hardware to produce good code. I get a little nervous when I see computer science students being taught only at a high abstraction level and languages like Ruby. Although abstraction is a beautiful thing to develop things without getting bogged down with unnecessary details, it is a bad thing when you are trying to develop super high performance code. Since the introduction of the rst CPU, computer architects added incredible features into CPU hardware to \forgive" bad programming skills; while you had to order the sequence of machine code instructions by hand two decades ago, CPUs do that in hardware for you today (e.g., out of order processing). A similar trend is clearly visible in the GPU world. Most of the techniques that were taught asperformance improvement techniquesin GPU programming ve years ago (e.g., thread divergence, shared memory bank conicts, and reduced usage of atomics) are becoming less relevant with the improved GPU architectures because GPU architects are adding hardware features that are improving these previous ineciencies so much that it won’t even matter if a programmer is sloppy about it within another 5{10 years. However, this is just a guess. What GPU architects can do depends on their (i)transistor budget, as well as (ii) their customers’ demands. When I saytransistor budget, I am referring to how many transistors the GPU manufacturers can cram into an Integrated Circuit (IC), aka a \chip." When I saycustomer demands, I mean that even if they can implement a feature, the applications that their customers are using might not

cudnn5.1 cuda8.0_win10x64 支持caffe gpu编译。。。。

GPU高性能编程CUDA实战.pdf GPU高性能运算之CUDA.pdf CUDA平台下多核GPU高性能并行编程研究.pdf

不可多得的GPU编程指南，高清完整版，之前淘宝买的，赚点积分

数据传输测试，先从主机传输到设备，再在设备内传输，再从设备传输到主机。 H-->D D-->D D-->H

基于CUDA平台GPU加速的共轭梯度法求解器。示例中提供了线性方程组。

该压缩文件包括四本比较经典的CUDA编程入门书籍.1)CUDA并行程序设计 GPU编程指南 2）CUDA范例精解 通用GPU编程 3）GPU高性能编程CUDA实战 4）GPU高性能运算之CUDA