4 citations · 7 across the 8 of their papers we have counts for
6 papers · 1 filter
PREBA: A Hardware/Software Co-Design for Multi-Instance GPU based AI Inference Servers
Gwangoo Yeo, Jiin Kim, Yujeong Choi +1
NVIDIA's Multi-Instance GPU (MIG) is a feature that enables system designers to reconfigure one large GPU into multiple smaller GPU slices. This work characterizes this emerging GP…
ElasticRec: A Microservice-based Model Serving Architecture Enabling Elastic Resource Scaling for Recommendation Models
Yujeong Choi, Jiin Kim, Minsoo Rhu
With the increasing popularity of recommendation systems (RecSys), the demand for compute resources in datacenters has surged. However, the model-wise resource allocation employed…
Hera: A Heterogeneity-Aware Multi-Tenant Inference Server for Personalized Recommendations
Yujeong Choi, John Kim, Minsoo Rhu
While providing low latency is a fundamental requirement in deploying recommendation services, achieving high resource utility is also crucial in cost-effectively maintaining the d…
PARIS and ELSA: An Elastic Scheduling Algorithm for Reconfigurable Multi-GPU Inference Servers
Yunseong Kim, Yujeong Choi, Minsoo Rhu
In cloud machine learning (ML) inference systems, providing low latency to end-users is of utmost importance. However, maximizing server utilization and system throughput is also c…
LazyBatching: An SLA-aware Batching System for Cloud Machine Learning Inference
Yujeong Choi, Yunseong Kim, Minsoo Rhu
In cloud ML inference systems, batching is an essential technique to increase throughput which helps optimize total-cost-of-ownership. Prior graph batching combines the individual…
PREMA: A Predictive Multi-task Scheduling Algorithm For Preemptible Neural Processing Units
Yujeong Choi, Minsoo Rhu
To amortize cost, cloud vendors providing DNN acceleration as a service to end-users employ consolidation and virtualization to share the underlying resources among multiple DNN se…