PyTorch支持多种compute platform,主要包括CPU和GPU(通过CUDA加速)。 对于GPU,PyTorch进一步支持不同版本的CUDA。 根据用户自身硬件情况筛选合适的compute platform: 无独立显卡(GPU):如果你的电脑没有独立显卡,那么只能选择CPU作为compute platform。 有独立显卡(GPU):如果你的电脑有NVIDIA独立显卡,那么你可以选择GPU作为...
sudo cp cuda/include/cudnn.h /usr/local/cuda-10.2/include #解压后的文件夹名字为cuda-10.2 sudo cp cuda/lib64/libcudnn* /usr/local/cuda-10.2/lib64sudo chmod a+r /usr/local/cuda-10.2/include/cudnn.h /usr/local/cuda-10.2/lib64/libcudnn* sudo cp cuda/include/cudnn.h /usr/local/cu...
print(a.device, t1 - t0, c.norm(2)) #首次使用cuda,会需要初始化,耗时会长一些 device = torch.device('cuda') a = a.to(device) b = b.to(device) t0 = time.time() c = torch.matmul(a, b) t2 = time.time() print(a.device, t2 - t0, c.norm(2)) #第二次使用cuda就没有初始...
NVIDIA Nsight Compute ‣ Added support for new CUDA asynchronous allocator attributes in the Memory Pools resources view. ‣ Added a topology chart and link properties table in the NVLink section. ‣ The selected metric column is scrolled into view on the Source page when a new metric is...
这是翻译自博客Analysis-Driven Optimization: Preparing for Analysis with NVIDIA Nsight Compute的文章,主要分为三个part一步步的讲述了如何使用Nsight Compute对cuda进行优化 我将会翻译这三篇文章,并添加一些自己的理解 如有不合适的部分,欢迎指正 代码仓库 ...
Nsight Compute is an interactive profiler for CUDA and NVIDIA OptiX that provides performance metrics and API debugging.
Use Nsight Compute to interactively profile and analyze individual CUDA kernels, optimizing them based on your findings. Combine the use of Nsight Systems and Nsight Compute into an effective optimization workflow for many GPU-accelerated machine learning applications. Enroll Now >...
The TCC device simply shows up as a standard CUDA device. For some Tesla-based GPUs, the default mode is not TCC. See below for more information.Do not kill a process that is executing code on a TCC device and paused on a breakpoint, except through the normal Stop Debugging command (...
Provides a safety critical certifiable alternative and facilitates the transition to Vulkan SC based Compute from OpenCL™ /CUDA®. Designed from the ground up for real time and safety certification. Contains no open-source components and no 3rdparty software. ...
The number of registers is limited, and will vary from platform to platform. When the limit is exceeded, register variables will be spilled to memory, causing changes in performance. For each architecture, there is a recommended maximum number of registers to use (see the "CUDA Programming ...