David Lowenthal

David Lowenthal is a Professor at the University of Arizona specializing in parallel and distributed computing, operating systems, and run-time systems, with his office located in GS 705. His research directly addresses critical challenges in modern high-performance computing infrastructure.

His educational background includes:

• Ph.D. in Computer Science from the University of Arizona (1996)

Professor Lowenthal's research centers on optimizing large-scale computing systems through innovative scheduling algorithms, power management techniques, and network optimization strategies. Key contributions include mitigating inter-job interference in cluster environments, developing quality-of-service mechanisms for MPI applications, and pioneering coscheduling approaches like the Jigsaw scheduler. His work bridges theoretical computer science with practical system implementation to enhance resource utilization under power constraints.

Analysis of his 15 most recent publications (2016-2023) reveals three dominant research thrusts: intelligent job scheduling (particularly coscheduling and backfilling), power-aware computing for energy-constrained HPC systems, and network performance optimization in dragonfly/fat-tree topologies. These publications consistently demonstrate collaborative NSF-funded research with emphasis on real-world system implementation and performance evaluation.

No scientific awards were documented in the provided text.

While no student advisees were listed, his research program has secured substantial NSF funding through collaborative grants including "Collaborative Research: SHF: Medium: Co-Optimizing Computation and Data Transformations for Sparse Tensors" (2022) and "Collaborative Research: OAC Core: Improving Utilization of High-Performance Computing Systems via Intelligent Co-scheduling" (2021), indicating active grant management and interdisciplinary collaboration.

No specific laboratories or research teams were mentioned in the source material.