Leveraging HPC resources with distributed RDataFrame

The declarative approach to data analysis provides high-level abstractions for users to operate on their datasets in a much more ergonomic fashion compared to imperative interfaces. ROOT offers such a tool with RDataFrame, which has been tested in production environments and used in real-world analyses with optimal results. Its programming model acts by creating a computation graph with the operations issued by the user and executing it lazily only when the final results are queried. It has always been oriented towards parallelisation, with native support for multi-thread execution on a single machine. Recently, RDataFrame has been extended with a Python layer that is capable of steering and executing the RDataFrame computation graph over a set of distributed resources. In addition, such a layer requires minimal code changes for an RDataFrame application to run distributedly. The new tool effectively allows running a C++ event loop based on RDataFrame while leveraging common industry tools like Dask to schedule the usage of resources. This work presents results and insights gathered through the distributed RDataFrame tool running a physics analysis connecting multiple nodes with a Dask scheduler that requests resources from a Slurm cluster.