在安装好SYCL和ComputeCpp环境之后，按照codeplay发布的Hello SYCL步骤，想要通过测试最简单的向量加法来测试本地开发环境。结果总是Build完成之后遇到Runtime error， 具体参考Issue #230。不得不说同行们的效率还是挺高，在较短的时间内给与了回复，不仅指出了出错原因，也指明了当前Hello SYCL存在的问题。为community维护的社区程序员点赞！
现将此次问题总结如下：
在做网络可视化的时候，经常会用到tensorboard。tensorflow程序运行在远程服务器，tensorboard启动后访问地址为：0.0.0.0:6006。这样没法用自己机器上的浏览器访问tensorboard。
方法
1)使用ssh命令登录时候，添加-L选项重定向port 6006到本地端口16006
1 | $ ssh -L 16006:127.0.0.1:6006 olivier@my_server_ip |
2)启动tensorboard命令使用默认端口号：
1 | tensorboard --logdir=/logpath |
3)在本地浏览器栏输入地址：
http://127.0.0.1:16006
The use of Graphics Processing Units for rendering is well known, but their power for general parallel computation has only recently been explored. Parallel algorithms running on GPUs can often achieve up to 100x speedup over similar CPU algorithms, with many existing applications for physics simulations, signal processing, financial modeling, neural networks, and countless other fields.
This course will cover programming techniques for the GPU. The course will introduce NVIDIA’s parallel computing language, CUDA. Beyond covering the CUDA programming model and syntax, the course will also discuss GPU architecture, high performance computing on GPUs, parallel algorithms, CUDA libraries, and applications of GPU computing.
Problem sets will cover performance optimization and specific GPU applications such as numerical mathematics, medical imaging, finance, and other fields.
The use of Graphics Processing Units for rendering is well known, but their power for general parallel computation has only recently been explored. Parallel algorithms running on GPUs can often achieve up to 100x speedup over similar CPU algorithms, with many existing applications for physics simulations, signal processing, financial modeling, neural networks, and countless other fields.
This course will cover programming techniques for the GPU. The course will introduce NVIDIA’s parallel computing language, CUDA. Beyond covering the CUDA programming model and syntax, the course will also discuss GPU architecture, high performance computing on GPUs, parallel algorithms, CUDA libraries, and applications of GPU computing.
Problem sets will cover performance optimization and specific GPU applications such as numerical mathematics, medical imaging, finance, and other fields.
The use of Graphics Processing Units for rendering is well known, but their power for general parallel computation has only recently been explored. Parallel algorithms running on GPUs can often achieve up to 100x speedup over similar CPU algorithms, with many existing applications for physics simulations, signal processing, financial modeling, neural networks, and countless other fields.
This course will cover programming techniques for the GPU. The course will introduce NVIDIA’s parallel computing language, CUDA. Beyond covering the CUDA programming model and syntax, the course will also discuss GPU architecture, high performance computing on GPUs, parallel algorithms, CUDA libraries, and applications of GPU computing.
Problem sets will cover performance optimization and specific GPU applications such as numerical mathematics, medical imaging, finance, and other fields.
The use of Graphics Processing Units for rendering is well known, but their power for general parallel computation has only recently been explored. Parallel algorithms running on GPUs can often achieve up to 100x speedup over similar CPU algorithms, with many existing applications for physics simulations, signal processing, financial modeling, neural networks, and countless other fields.
This course will cover programming techniques for the GPU. The course will introduce NVIDIA’s parallel computing language, CUDA. Beyond covering the CUDA programming model and syntax, the course will also discuss GPU architecture, high performance computing on GPUs, parallel algorithms, CUDA libraries, and applications of GPU computing.
Problem sets will cover performance optimization and specific GPU applications such as numerical mathematics, medical imaging, finance, and other fields.
The use of Graphics Processing Units for rendering is well known, but their power for general parallel computation has only recently been explored. Parallel algorithms running on GPUs can often achieve up to 100x speedup over similar CPU algorithms, with many existing applications for physics simulations, signal processing, financial modeling, neural networks, and countless other fields.
This course will cover programming techniques for the GPU. The course will introduce NVIDIA’s parallel computing language, CUDA. Beyond covering the CUDA programming model and syntax, the course will also discuss GPU architecture, high performance computing on GPUs, parallel algorithms, CUDA libraries, and applications of GPU computing.
Problem sets will cover performance optimization and specific GPU applications such as numerical mathematics, medical imaging, finance, and other fields.
The use of Graphics Processing Units for rendering is well known, but their power for general parallel computation has only recently been explored. Parallel algorithms running on GPUs can often achieve up to 100x speedup over similar CPU algorithms, with many existing applications for physics simulations, signal processing, financial modeling, neural networks, and countless other fields.
This course will cover programming techniques for the GPU. The course will introduce NVIDIA’s parallel computing language, CUDA. Beyond covering the CUDA programming model and syntax, the course will also discuss GPU architecture, high performance computing on GPUs, parallel algorithms, CUDA libraries, and applications of GPU computing.
Problem sets will cover performance optimization and specific GPU applications such as numerical mathematics, medical imaging, finance, and other fields.