Speeding up reads

I am doing some benchmarking on a Databricks cluster where I use Cobrix to read EBCDIC files and write to parquet. I have an implementation of the same process which does not use this library. Reading a 2GB EBCDIC file with Cobrix takes two minutes longer than reading the file using sc.binaryRecords() and putting the right schema in place to create a DataFrame. The file has around 1400 columns and 9000 bytes per record.

Here is the cluster config:
Spark Version: 2.4
Worker count: flexible, depending on the workload
RAM per worker: 14 GB
Cores per worker: 4
Executor count: flexible, depending on the workload
Executor memory: 7.4 GB

The benchmarks you have included in this project make me think that an increase in executor count would speed up the read throughput. However, I have currently enabled autoscaling in Databricks so it dynamically allocates executors on the fly depending on the workload.

Could you please provide some guidelines on optimising read speed? Things like configurations you have tried in your organisation's use case can really be of help to speed up the process.