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7 changes: 6 additions & 1 deletion README.md
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Expand Up @@ -4,9 +4,14 @@ The RAPIDS Accelerator for Apache Spark provides a set of plugins for
[Apache Spark](https://spark.apache.org) that leverage GPUs to accelerate processing
via the [RAPIDS](https://rapids.ai) libraries and [UCX](https://www.openucx.org/).



![TPCxBB Like query results](./docs/img/tpcxbb-like-results.png "TPCxBB Like Query Results")

The chart above shows results from running ETL queries based off of the



[TPCxBB benchmark](http://www.tpc.org/tpcx-bb/default.asp). These are **not** official results in
any way. It uses a 10TB Dataset (scale factor 10,000), stored in parquet. The processing happened on
a two node DGX-2 cluster. Each node has 96 CPU cores, 1.5TB host memory, 16 V100 GPUs, and 512 GB
Expand Down Expand Up @@ -60,4 +65,4 @@ Currently, we are working with XGBoost to try to provide this integration out of

You may need to disable RMM caching when exporting data to an ML library as that library
will likely want to use all of the GPU's memory and if it is not aware of RMM it will not have
access to any of the memory that RMM is holding.
access to any of the memory that RMM is holding.