1 citations · 2 across the 11 of their papers we have counts for
9 papers · 1 filter
LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs
Liad Gerstman, Aditya Dhakal, Dejan Milojicic +1
Graph Neural Networks (GNNs) are widely used across domains such as natural sciences, social network analysis, chip design, and recommendation systems. However, as graph sizes grow…
Energy-Aware Scheduling for Serverless LLM Serving on Shared GPUs
Tianyu Wang, Gourav Rattihalli, Aditya Dhakal +2
As LLM inference becomes a major cloud workload, its growing energy footprint makes cluster-wide energy optimization increasingly important. Serverless LLM serving helps platforms…
ObjectCache: Layerwise Object-Storage Retrieval for KV Cache Reuse
Yu Zhu, Aditya Dhakal, Yunming Xiao +2
Prefix KV caching has become a key mechanism in LLM serving: it reduces time to first token (TTFT) by avoiding redundant computation across requests that share a prefix (i.e., the…
MARLIN: Multi-Agent Game-Theoretic Reinforcement Learning for Sustainable LLM Inference in Cloud Datacenters
H. Moore, S. Qi, D. Milojicic +2
Large Language Models (LLMs) have become increasingly prevalent in cloud-based platforms, propelled by the introduction of AI-based consumer and enterprise services. LLM inference…
Sustainable Graph Analytics Workload Scheduling with Evolutionary Reinforcement Learning in Edge-Cloud Systems
P. Ramicetty, H. Moore, S. Qi +5
Graph analytics powers modern intelligent systems such as smart cities, cyber-physical infrastructure, IoT security, and large-scale social networks. As these workloads scale in co…
Sustainable Carbon-Aware and Water-Efficient LLM Scheduling in Geo-Distributed Cloud Datacenters
Hayden Moore, Sirui Qi, Ninad Hogade +3
In recent years, Large Language Models (LLM) such as ChatGPT, CoPilot, and Gemini have been widely adopted in different areas. As the use of LLMs continues to grow, many efforts ha…