datashader memory consumption on very large dataset

We are dealing with total of 140 million points from ~500 files. It is great that datashader can draw it out, but it also took 100GB of peak memory and about 40 minutes on a couple of cores getting the data into dataframe while the overall system cpu utilization is pretty spare. Is there anyway to both reduce the memory requirement and increase/parallelize the CPU usage?

Thanks

The issue names no files, tests, or entry points. Start by reproducing the reported 140-million-point workload from roughly 500 files, then measure peak memory, elapsed time, and CPU utilization; done would require a concrete way to reduce memory use and improve or parallelize CPU usage.

Written by the indexing model from the issue text.