2 citations · 3 across the 4 of their papers we have counts for
5 papers
Mosaic: GPU Sharing with Latency Guarantees through Kernel-Level Interference Prediction
Foteini Strati, Ethan Graham, Leo Stephan +2
GPUs are increasingly in demand for AI workloads, yet often remain substantially underutilized, motivating workload colocation. However, colocation introduces interference that can…
Sailor: Automating Distributed Training over Dynamic, Heterogeneous, and Geo-distributed Clusters
Foteini Strati, Zhendong Zhang, George Manos +7
The high GPU demand of ML training makes it hard to allocate large homogeneous clusters of high-end GPUs in a single availability zone. Leveraging heterogeneous GPUs available with…
Understanding GPU Resource Interference One Level Deeper
Paul Elvinger, Foteini Strati, Natalie Enright Jerger +1
GPUs are vastly underutilized, even when running resource-intensive AI applications, as GPU kernels within each job have diverse resource profiles that may saturate some parts of a…
SmartPQ: An Adaptive Concurrent Priority Queue for NUMA Architectures
Christina Giannoula, Foteini Strati, Dimitrios Siakavaras +2
Concurrent priority queues are widely used in important workloads, such as graph applications and discrete event simulations. However, designing scalable concurrent priority queues…
DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving
Foteini Strati, Sara Mcallister, Amar Phanishayee +2
Distributed LLM serving is costly and often underutilizes hardware accelerators due to three key challenges: bubbles in pipeline-parallel deployments caused by the bimodal latency…