4 papers · 1 filter
ViBE: Co-Optimizing Workload Skew and Hardware Variability for MoE Serving
Seokjin Go, Marko Scrbak, Ephrem Wu +2
In distributed Mixture-of-Experts (MoE) inference, input-dependent token routing interacts with GPU performance variability to create persistent stragglers under synchronized execu…
Equinox: Decentralized Scheduling for Hardware-Aware Orbital Intelligence
Ansel Kaplan Erol, Divya Mahajan
Earth-observation satellites are emerging as distributed edge platforms for time-critical tasks, yet orbital scheduling remains challenged by intermittent energy harvesting and tem…
Characterizing the Efficiency of Distributed Training: A Power, Performance, and Thermal Perspective
Seokjin Go, Joongun Park, Spandan More +5
The rapid scaling of Large Language Models (LLMs) has pushed training workloads far beyond the limits of single-node analysis, demanding a deeper understanding of how these models…
Characterizing Compute-Communication Overlap in GPU-Accelerated Distributed Deep Learning: Performance and Power Implications
Seonho Lee, Jihwan Oh, Junkyum Kim +3
This paper provides an in-depth characterization of GPU-accelerated systems, to understand the interplay between overlapping computation and communication which is commonly employe…