Some useful tools

本文介绍了几款实用的工具,包括MySQL WorkBench这一数据库管理工具,以及Google Closure Compiler和TBCompressor2.4.2,这两款工具非常适合压缩CSS和JS文件。

Some useful tools

MySQL WorkBench, a database management  tool

Google Closure Compiler   and TBCompressor 2.4.2  ,it is wonderful for zip your CSS and JS

### Understanding and Handling CUDA Kernel Errors Reported Asynchronously During API Calls CUDA kernel errors reported asynchronously during API calls typically occur when a CUDA kernel fails on the device side, but the error is not immediately reported. This behavior is due to the asynchronous nature of CUDA execution, where the host continues executing subsequent API calls before the kernel completes. The error is then reported at a later API call, making debugging challenging because the stack trace may not point to the actual source of the error. One common manifestation of this behavior is the `CUDA error: device-side assert triggered` error, which indicates that an assertion in the CUDA kernel failed during execution. This error is often encountered in deep learning frameworks like PyTorch during operations such as backpropagation, where the framework relies heavily on CUDA for GPU acceleration. To debug such errors, setting the environment variable `CUDA_LAUNCH_BLOCKING=1` can be useful. This setting forces synchronous execution of CUDA kernels, allowing the error to be reported at the exact point of failure rather than at a later API call. For example, in PyTorch, this can be achieved by adding the following lines at the beginning of the script: ```python import os os.environ['CUDA_LAUNCH_BLOCKING'] = '1' ``` This configuration change helps in identifying the exact location of the error, making it easier to trace and fix the issue. Additionally, compiling with `TORCH_USE_CUDA_DSA` enables device-side assertions, which can provide more detailed information about the failure. Device-side assertions are particularly useful when debugging custom CUDA kernels or when working with complex tensor operations that might lead to out-of-bounds memory access or invalid computations. When dealing with multiple GPUs, specifying which GPU to use can also help isolate issues related to resource allocation or contention. This can be done by setting the `CUDA_VISIBLE_DEVICES` environment variable to restrict the visible devices: ```python os.environ['CUDA_VISIBLE_DEVICES'] = '0' # Use only the first GPU ``` In summary, handling CUDA kernel errors that are reported asynchronously involves understanding the asynchronous execution model of CUDA and using tools and environment settings that facilitate synchronous execution and detailed error reporting. By leveraging these techniques, developers can effectively debug and resolve issues related to CUDA kernel failures. ###
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