Apache beam DirectRunner vs "normal" parallel processes
You question is broad. However, I will try to provide you some inputs. It's hard to compare a DirectRunner and a DataflowRunner. 

DirectRunner launches your pipeline on your current VM and use the capability of this only VM. It's your VM, you have to set it up, patch it, take care to free disk/partition/logs file, (...)   
DataflowRunner launches the pipeline to a managed platform. Dataflow, according with its metrics and "prediction" (no ML here!) chooses to scale up or down the number of VM to execute as quickly as possible your pipeline. You can set small VM (1 vCPU for example) and Dataflow will spawn a lot of them, or bigger VM and, maybe that dataflow will spawn only 1 because it's enough for the pipeline.

Pro tips: the VM bandwidth is limited to 2Gbs per vCPU up to 8 vCPU. Take care of the network bottleneck and choose wisely the VM size (I recommend VM with 4 or 8 vCPU usually) 
On one side, you have only one VM to manage, on the other side, you only have to set parameters and let Dataflow managing and scaling your pipeline.
I don't know your growth perspective, but vertical scalability (adding more vCPU/memory on your single VM) can reach a limit a day. With Dataflow, it's elastic and you don't worry about this; in addition of server management and patching.
Finally, answer to your question "faster or slower", too hard to answer... Dataflow, if it run on several VM, will add network latency, dataflow internal management overhead, but can scale to use more vCPU in parallel at some point of time compare to your current VM. Is your pipeline can leverage of this parallelism or not? Is it solve some of your current bottleneck? Too hard to answer on my side.
I currently have a pipeline running on GCP. The entire thing is written using pandas to manipulate CSVs and do some transformations, as well as side inputs from external sources. (It makes use of bigquery and storage APIs). The thing is, it runs on a 32vCPUs/120GB RAM Compute Engine instance (VM) and it does simple parallel processing with python's multiprocessing library. We are currently thinking about switching to Dataflow, and what I'd like to know is: if I were to implement the same pipeline using Beam's DirectRunner, how should I expect the performance to compare to that of the current implementation? Would it be faster or slower and why? Will the DirectRunner use well all the machine resources or is it limited somehow?