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default rmm alloc fraction to the max to avoid unnecessary fragmentation #2846
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Changing this default means a new warning now appears on startup with the default configs. For example:
It would be nice to not emit warnings with the default configs on most setups. We should consider whether this should be a warning or just an info, or maybe change the conditions under which it is a warning.
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I would like to see us to take a deep look at how we are calculating the RMM pool size etc.
We have several configs associated with this.
spark.rapids.memory.gpu.allocFraction
- 0 to 1 for the percentage of free memory allocated for the RMM pool. Percentage of free memory, but docs say available memory.spark.rapids.memory.gpu.maxAllocFraction
- 0 to 1 maximum amount of total GPU memory that can be allocated for the RMM pool. This is percentage of total memory except we also remove from it a reserved but reserved is from total, so if I have 1 GiB used already reserved is not doing anything.spark.rapids.memory.gpu.minAllocFraction
- 0 to 1 minimum amount of total GPU memory that can be allocated for the RMM pool before we throw an error saying it is too small. this is a percentage of the total memory.spark.rapids.memory.gpu.reserve
- number of bytes that have to be reserved for cuda/cudf to load kernels etc so we don't crash because RMM has everything allocated.The code that does the warnings is a bit confusing too.
So to me it feels like we should instead.
We will need to update the docs in the config section accordingly to better explain what is happening.
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OK, changed how initial allocation is calculated to take into account the reserve amount. To me this seems to make sense since the most you can allocate is
free-reserve
.