most citedMinos: Systematically Classifying Performance and Power Characteristics of GPU Workloads on HPC Clusters

2 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

cs.DC20262 cited

Minos: Systematically Classifying Performance and Power Characteristics of GPU Workloads on HPC Clusters

Rutwik Jain, Yiwei Jiang, Matthew D. Sinclair +1

As large-scale HPC compute clusters increasingly adopt accelerators such as GPUs to meet the voracious demands of modern workloads, these clusters are increasingly becoming power c…

cs.AR20261 cited

Wattchmen: Watching the Wattchers -- High Fidelity, Flexible GPU Energy Modeling

Brandon Tran, Matthias Maiterth, Woong Shin +2

Modern GPU-rich HPC systems are increasingly becoming energy-constrained. Thus, understanding an application's energy consumption becomes essential. Unfortunately, current GPU ener…

cs.OS2025

ARMS: Adaptive and Robust Memory Tiering System

Sujay Yadalam, Konstantinos Kanellis, Michael Swift +1

Memory tiering systems seek cost-effective memory scaling by adding multiple tiers of memory. For maximum performance, frequently accessed (hot) data must be placed close to the ho…

cs.OS2025

From Good to Great: Improving Memory Tiering Performance Through Parameter Tuning

Konstantinos Kanellis, Sujay Yadalam, Fanchao Chen +2

Memory tiering systems achieve memory scaling by adding multiple tiers of memory wherein different tiers have different access latencies and bandwidth. For maximum performance, fre…

cs.OS2025

TUNA: Tuning Unstable and Noisy Cloud Applications

Johannes Freischuetz, Konstantinos Kanellis, Brian Kroth +1

Autotuning plays a pivotal role in optimizing the performance of systems, particularly in large-scale cloud deployments. One of the main challenges in performing autotuning in the…