  Emerging ML/AI hardware accelerators, like the 850,000 processor Cerebras
Wafer-Scale Engine (WSE), hold great promise to scale up the capabilities of
evolutionary computation. However, challenges remain in maintaining visibility
into underlying evolutionary processes while efficiently utilizing these
platforms' large processor counts. Here, we focus on the problem of extracting
phylogenetic information from digital evolution on the WSE platform. We present
a tracking-enabled asynchronous island-based genetic algorithm (GA) framework
for WSE hardware. Emulated and on-hardware GA benchmarks with a simple
tracking-enabled agent model clock upwards of 1 million generations a minute
for population sizes reaching 16 million. This pace enables quadrillions of
evaluations a day. We validate phylogenetic reconstructions from these trials
and demonstrate their suitability for inference of underlying evolutionary
conditions. In particular, we demonstrate extraction of clear phylometric
signals that differentiate wafer-scale runs with adaptive dynamics enabled
versus disabled. Together, these benchmark and validation trials reflect strong
potential for highly scalable evolutionary computation that is both efficient
and observable. Kernel code implementing the island-model GA supports drop-in
customization to support any fixed-length genome content and fitness criteria,
allowing it to be leveraged to advance research interests across the community.
