Use of simulation algorithms has become standard to evaluate and analyze redistricting plans.  Beyond academia, they are also frequently used in courts.  These Monte Carlo algorithms allow researchers to obtain a representative sample of redistricting plans under a specified set of redistricting criteria.  As part of the Algorithm-Assisted Redistricting Methodology (ALARM) Project (https://alarm-redist.org), my collaborators and I have developed a scalable simulation algorithm and built a suite of open-source software packages that help researchers from data ingestion to algorithm implementation and visualization of results.  I will discuss how we used this new technology to evaluate the 2020 Congressional maps across 50 states.  We find that partisan gerrymandering is widespread in the 2020 redistricting cycle, but most of the bias it creates cancels at the national level, giving Republicans two additional seats, on average.