Instead, we focus on generating well-sampled structural ensembles of proteins using massively parallel simulations (Fig. ). Parallel simulations leverages accessible hardware at commodity scale to run multiple replicates in an asynchronous manner, generating multiple random walks in conformational space. Assuming these replicates sample the same underlying thermodynamic configuration (i.e. are all consistent in atom count, construct, force field, etc.), then it is easy to integrate these replicates into a single unified structural landscape. Indeed, prior work has demonstrated that multiple random walks are capable of exploring the same states as a single long trajectory, but require less wall time (Fig. ) . Many architectures now exist to run simulations in parallel , and one can easily run multiple replicates using high-performance computing (HPC) resources. However, parallel simulation datasets shine when leveraging modern commodity hardware.