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UID:submissions.supercomputing.org_SC24_sess533_post166@linklings.com
SUMMARY:Trackable Agent-Based Evolution Models at Wafer Scale
DESCRIPTION:Matthew Andres Moreno and Connor Yang (University of Michigan)
 , Emily Dolson (Michigan State University), and Luis Zaman (University of 
 Michigan)\n\nEmerging ML/AI hardware accelerators, like the 850,000 proces
 sor Cerebras Wafer-Scale Engine (WSE), hold great promise to scale up the 
 capabilities of evolutionary computation. However, challenges remain in ma
 intaining visibility into underlying evolutionary processes while efficien
 tly utilizing these platforms' large processor counts. Here, we focus on t
 he problem of extracting phylogenetic history. We present a tracking-enabl
 ed asynchronous island-based genetic algorithm (GA) framework for WSE hard
 ware. Emulated and on-hardware GA benchmarks with a simple tracking-enable
 d agent model clock upwards of 1 million generations per minute for popula
 tion sizes reaching 16 million. We validate phylogenetic reconstructions f
 rom these trials and demonstrate their suitability for inference of underl
 ying evolutionary conditions. In particular, we demonstrate extraction of 
 clear phylometric signals that differentiate adaptive dynamics. Kernel cod
 e implementing the island-model GA supports drop-in customization to suppo
 rt any fixed-length genome content and fitness criteria, benefiting furthe
 r explorations within the evolutionary biology and evolutionary computatio
 n communities.\n\nRegistration Category: Tech Program Reg Pass, Exhibits R
 eg Pass\n\nSession Chairs: Ayesha Afzal (Friedrich-Alexander University, E
 rlangen-Nuremberg; Erlangen National High Performance Computing Center); S
 ally Ellingson (University of Kentucky); and Alan Sussman (University of M
 aryland)\n\n
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