Amanda Bienz

Amanda Bienz serves as an Assistant Professor in the Department of Computer Science at the University of New Mexico (UNM), where she leads the Scalable Solvers Lab and acts as faculty advisor for Women in Computing. Her academic roles include teaching operating systems and parallel computing courses while spearheading efforts to restructure New Mexico's CS4ALL curriculum for statewide computer science education expansion.

Her research centers on overcoming communication bottlenecks in high-performance computing systems, specifically targeting the performance gap between emerging exascale hardware and real-world applications. Key focus areas include developing portable communication optimizations, enhancing MPI collective operations, creating topology-aware message passing extensions, and benchmarking heterogeneous architectures. Her work directly addresses critical challenges in scaling parallel applications through innovations in sparse solvers, neighborhood collectives, and node-aware communication strategies for GPU-accelerated systems.

Analysis of her 2022-2024 publications reveals consistent emphasis on communication optimization across diverse HPC domains. Her research demonstrates particular expertise in irregular communication patterns, locality-aware algorithms, and performance modeling for heterogeneous architectures. Significant contributions include novel approaches to sparse dynamic data exchange, compressed linear algebra algorithms, and persistent communication techniques that reduce synchronization overhead in large-scale simulations.

Scientific Awards:

• NSF CAREER Award for "Towards Exascale Performance of Parallel Applications"

Dr. Bienz actively mentors students through the Scalable Solvers Lab, welcoming new researchers interested in high-performance computing. Her NSF CAREER grant provides substantial research funding supporting both technical innovation and educational initiatives. The CS4ALL curriculum restructuring project demonstrates her commitment to broadening computer science access throughout New Mexico's K-12 education system.

The Scalable Solvers Lab develops open-source tools including the Raptor algebraic multigrid solver and MPI-Advance communication library. Current projects focus on benchmarking heterogeneous architectures (Summit/Lassen supercomputers), optimizing FFT implementations, and creating node-aware communication strategies for conjugate gradient methods. The lab maintains active GitHub repositories with substantial community engagement, including contributions to CUDA-aware MPI implementations and halo exchange libraries for multi-GPU systems.