Most of the performance differences are in how these systems handle representation, I/O, and execution scheduling. In other words, implementation. The challenge in closing the gap is that the architectures across systems are quite different so you can't just add performance that is largely derived from fundamental architecture.

Columnar MPP needs to be carefully defined to answer the question. Some important data models that fit in these architectures operate on data types that are not meaningfully order-able at a mathematical level i.e. you can't sort them. A lot of columnar implementations, and virtually all in open source, assume sortability as a property of the represented data types.

tl;dr: Columnar MPP sometimes is not far outside what you can express with Parquet/Spark/MapReduce/etc, just much faster, but there are data models supported in some advanced Columnar MPP systems that are not usefully expressible with that stack. It depends on the platform and the use case.