  Similarity search is one of the most fundamental computations that are
regularly performed on ever-increasing protein datasets. Scalability is of
paramount importance for uncovering novel phenomena that occur at very large
scales. We unleash the power of over 20,000 GPUs on the Summit system to
perform all-vs-all protein similarity search on one of the largest publicly
available datasets with 405 million proteins, in less than 3.5 hours, cutting
the time-to-solution for many use cases from weeks. The variability of protein
sequence lengths, as well as the sparsity of the space of pairwise comparisons,
make this a challenging problem in distributed memory. Due to the need to
construct and maintain a data structure holding indices to all other sequences,
this application has a huge memory footprint that makes it hard to scale the
problem sizes. We overcome this memory limitation by innovative matrix-based
blocking techniques, without introducing additional load imbalance.
