9 citations · 24 across the 16 of their papers we have counts for
16 papers
Slasher: Power Flexibility for Cloud Datacenters
Liuzixuan Lin, Fiodar Kazhamiaka, Alok Gautam Kumbhare +10
Datacenters consume many megawatts of power, and regularly encounter scenarios that require modulating their power draw. These scenarios include datacenter infrastructure failures,…
AtumAI: A Principled Framework for Agentic Generation of Datacenter Control-Plane Policies
Qiushi Lin, Chaojie Zhang, Íñigo Goiri +3
The efficiency of a datacenter rests on its control plane policies. Designing these policies is increasingly hard: the hardware-software stack grows fast, the design space is vast…
Agentic Coding in the Wild: Characterizing GitHub Copilot Traces at Production Scale
Banruo Liu, Haoran Qiu, Íñigo Goiri +3
AI coding agents like GitHub Copilot, Claude Code, and Codex interleave multi-step LLM inference with tool execution, creating a workload different from chatbots. We present the fi…
Designing Datacenter Power Delivery Hierarchies for the AI Era
Grant Wilkins, Fiodar Kazhamiaka, Alok Gautam Kumbhare +2
Demand for AI accelerators is rapidly increasing rack power density, with projections approaching 1MW per deployment by 2027. This poses a major challenge for datacenter power deli…
StreamWise: Serving Multi-Modal Generation in Real-Time at Scale
Haoran Qiu, Gohar Irfan Chaudhry, Chaojie Zhang +4
Advances in multi-modal generative models are enabling new applications, from storytelling to automated media synthesis. Most current workloads generate simple outputs (e.g., image…
Sherlock: Reliable and Efficient Agentic Workflow Execution
Yeonju Ro, Haoran Qiu, Íñigo Goiri +6
With the increasing adoption of large language models (LLM), agentic workflows, which compose multiple LLM calls with tools, retrieval, and reasoning steps, are increasingly replac…
Rearchitecting Datacenter Lifecycle for AI: A TCO-Driven Framework
Jovan Stojkovic, Chaojie Zhang, Íñigo Goiri +1
The rapid rise of large language models (LLMs) has been driving an enormous demand for AI inference infrastructure, mainly powered by high-end GPUs. While these accelerators offer…
Murakkab: Resource-Efficient Agentic Workflow Orchestration in Cloud Platforms
Gohar Irfan Chaudhry, Esha Choukse, Haoran Qiu +4
Agentic workflows commonly coordinate multiple models and tools with complex control logic. They are quickly becoming the dominant paradigm for AI applications. However, serving th…
Energy Use of AI Inference, Efficiency Pathways, and Test-Time Scaling
Felipe Oviedo, Fiodar Kazhamiaka, Esha Choukse +5
As AI inference scales to billions of queries, estimates of per-query energy use are increasingly important for capacity planning, efficiency interventions, and policy. Yet many pu…
Power Stabilization for AI Training Datacenters
Esha Choukse, Brijesh Warrier, Scot Heath +54
Large Artificial Intelligence (AI) training workloads spanning several tens of thousands of GPUs present unique power management challenges. These arise due to the high variability…
TAPAS: Thermal- and Power-Aware Scheduling for LLM Inference in Cloud Platforms
Jovan Stojkovic, Chaojie Zhang, Íñigo Goiri +5
The rising demand for generative large language models (LLMs) poses challenges for thermal and power management in cloud datacenters. Traditional techniques often are inadequate fo…
Towards Resource-Efficient Compound AI Systems
Gohar Irfan Chaudhry, Esha Choukse, Íñigo Goiri +3
Compound AI Systems, integrating multiple interacting components like models, retrievers, and external tools, have emerged as essential for addressing complex AI tasks. However, cu…
Coach: Exploiting Temporal Patterns for All-Resource Oversubscription in Cloud Platforms
Benjamin Reidys, Pantea Zardoshti, Íñigo Goiri +16
Cloud platforms remain underutilized despite multiple proposals to improve their utilization (e.g., disaggregation, harvesting, and oversubscription). Our characterization of the r…
Cloud abstractions for AI workloads
Marco Canini, Theophilus A. Benson, Ricardo Bianchini +4
AI workloads, often hosted in multi-tenant cloud environments, require vast computational resources but suffer inefficiencies due to limited tenant-provider coordination. Tenants l…
Workload Intelligence: Punching Holes Through the Cloud Abstraction
Lexiang Huang, Anjaly Parayil, Jue Zhang +13
Today, cloud workloads are essentially opaque to the cloud platform. Typically, the only information the platform receives is the virtual machine (VM) type and possibly a decoratio…
Splitwise: Efficient generative LLM inference using phase splitting
Pratyush Patel, Esha Choukse, Chaojie Zhang +4
Recent innovations in generative large language models (LLMs) have made their applications and use-cases ubiquitous. This has led to large-scale deployments of these models, using…