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One of the unavoidable steps for the next generation of Data Science and Artificial Intelligence technologies applied to mining

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Updated on July 17, 2024

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System requirements

The only mandatory requirement for using MiningMath is a 64-bits system. Other minimum requirements are listed further:

  1. Windows 10

  2. 64-bits system (mandatory)

  3. 110 MB of space (installation) + additional space for your projects' files.

  4. Processor: processors above 2.4 GHz are recommended to improve your experience.

  5. Memory: at least 4 GB of RAM is required. 8 GB of RAM or higher is recommended to improve your experience.

  6. Microsoft Excel.

  7. OpenGL 3.2 or above. Discover yours by downloading and running the procedure available here.

  8. Visual C++ Redistributable: Installation of Visual C++ Redistributable is necessary to run this software.

Recommended Hardware

Memory should be a higher priority when choosing the machine in which MiningMath will be run on. Here’s a list of priority upgrades to improve performance with large scale datasets: 

  1. Higher Ram

  2. Higher Ram frequency

  3. Higher processing clock

Common Issues

Insufficient memory

As previously presented, RAM should be one of the most important components to prioritize when selecting a computer to run MiningMath. However, if you encounter an insufficient memory warning during the import of your block model, there are some recommendations you can consider:

1. Memory Upgrade: If possible, this is the best solution to enhance efficiency. The characteristics to observe are listed in the previous item, “Recommended Hardware.”

2. Free Up Memory: Consider closing other applications that are consuming the computer’s RAM while MiningMath is running.

3. Increase Windows Virtual Memory: This procedure involves allocating disk space to be used as RAM. To perform this procedure, we recommend this tutorial.

4. Reblock: If none of these options work, reblocking can be considered to reduce the size of the model. Check more details here.

Extra: In exceptional cases, when working with boxes, it may be viable to manipulate the block coordinates to bring them closer together, creating a smaller model box.

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