Estimation-of-the-number-of-needed-GPUs.md

+W-SLDA Toolkit provides a script that can be used to estimate the number of GPUs that you need to run your code efficiently: [tools/td-memory.py](https://gitlab.fizyka.pw.edu.pl/wtools/wslda/-/blob/public/tools/td-memory.py). The user must edit the `# SETTINGS` section and run the code. Example:
+
+```python
+# SETTINGS
+NX = 128
+NY = 128
+NZ = 16
+codedim=2 # dimensonality of code
+nwf=70141 # provide here number if you know it, otherwise the code will use simple estimate
+mem_per_gpu = 16.0 # in GB
+min_mem_utilization = 2.0 # in GB
+```
+Note that the number of wave-functions to be evolved is typically printed by `st-wslda` code when writing them to files. Optionally you can leave `nwf=None`, and then the script will use an estimate for this number.  
+Running the script:
+```bash
+[gabrielw@wutdell tools]$ python td-memory.py 
+MINIMAL NUMBER OF GPUs=24
+```
+and plot like this will show up:
+![td-memory](uploads/d8896d2246152182974245985c6d819b/td-memory.png)
+
+To obtain a good performance of the code, it is recommended that the memory utilization of each GPU card is about 50% or more of its capacity. In the given example, it is recommended to run the code with the number of GPUs less than 50. 
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