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Initializing backend runtime for device #2. Please be patient...

2024-09-05 12:51:49

This error message indicates that a device (e.g. GPU) was initialized with an out-of-memory situation, causing the OpenCL command to fail to allocate memory (). This is usually related to the GPU's lack of available memory, possibly due to the following reasons:clEnqueueNDRangeKernel()CL_MEM_OBJECT_ALLOCATION_FAILURE

Cause analysis

  1. Insufficient GPU memory: Your GPU doesn't have enough memory to handle tasks called for by Hashcat or other tools. For example, when you're trying to crack a very large hash file or using very high crack strength, the video memory requirements increase significantly.

  2. Other processes are taking up GPU memory: There may be other processes running on your GPU that are taking up a lot of video memory and Hashcat is unable to allocate enough memory.

  3. GPU driver issues: In some cases, GPU driver versions are incompatible or have bugs, which may cause memory management problems. Updating the driver may help resolve this issue.

  4. Memory allocation limitations: Some OpenCL implementations may have size limits on individual memory allocations. If your task requires more than a single block of memory for this limit, memory allocation will also fail.

Resolution

  1. Reduced Attack Parameters:

    • Reduce the batch size (or and parameters) to reduce the memory footprint.--workload-profile-n-u

    • Use smaller hash lists or split large files into smaller parts.

  2. Shut down other GPU-hogging processes:

    • Confirm that no other processes are using the GPU. You can use Task Manager (Windows) or (Linux/Windows, for NVIDIA GPUs) to check your current video memory usage.nvidia-smi

  3. Adjust device selection:

    • Select different devices (if you have more than one GPU) by using parameters.-d

  4. To update or reinstall the GPU driver:

    • Make sure your GPU drivers are up to date, especially if you're using a newer GPU or operating system version.

  5. Use a lower hashing algorithm pattern:

    • Using a lower mode, if applicable, may reduce memory requirements.

  6. Add more physical memory:

    • If you have multiple GPUs, consider adding physical memory or swapping to a GPU with more memory.

By trying the above methods, you should be able to find a suitable solution to deal with the problem of low memory.

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