POLCA: Power Oversubscription in LLM Cloud Providers

• URL: http://arxiv.org/abs/2308.12908v1
• Date: Thu, 24 Aug 2023 16:32:34 GMT
• Title: POLCA: Power Oversubscription in LLM Cloud Providers
• Authors: Pratyush Patel, Esha Choukse, Chaojie Zhang, \'I\~nigo Goiri, Brijesh Warrier, Nithish Mahalingam, Ricardo Bianchini
• Abstract summary: Large language models (LLMs) are becoming increasingly power intensive. We show that there is a significant opportunity to oversubscribe power in LLM clusters. We propose POLCA, our framework for power oversubscription that is robust, reliable, and readily deployable for GPU clusters.
• Score: 0.8299593158757622
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent innovation in large language models (LLMs), and their myriad use-cases have rapidly driven up the compute capacity demand for datacenter GPUs. Several cloud providers and other enterprises have made substantial plans of growth in their datacenters to support these new workloads. One of the key bottleneck resources in datacenters is power, and given the increasing model sizes of LLMs, they are becoming increasingly power intensive. In this paper, we show that there is a significant opportunity to oversubscribe power in LLM clusters. Power oversubscription improves the power efficiency of these datacenters, allowing more deployable servers per datacenter, and reduces the deployment time, since building new datacenters is slow. We extensively characterize the power consumption patterns of a variety of LLMs and their configurations. We identify the differences between the inference and training power consumption patterns. Based on our analysis of these LLMs, we claim that the average and peak power utilization in LLM clusters for inference should not be very high. Our deductions align with the data from production LLM clusters, revealing that inference workloads offer substantial headroom for power oversubscription. However, the stringent set of telemetry and controls that GPUs offer in a virtualized environment, makes it challenging to have a reliable and robust power oversubscription mechanism. We propose POLCA, our framework for power oversubscription that is robust, reliable, and readily deployable for GPU clusters. Using open-source models to replicate the power patterns observed in production, we simulate POLCA and demonstrate that we can deploy 30% more servers in the same GPU cluster for inference, with minimal performance loss

Related papers

• SplitLLM: Collaborative Inference of LLMs for Model Placement and Throughput Optimization [8.121663525764294]
  Large language models (LLMs) play a crucial role in our daily lives due to their ability to understand and generate human-like text. In this report, we design a collaborative inference architecture between a server and its clients to alleviate the throughput limit. We show in the experiments that we are able to efficiently distribute the workload allowing for roughly 1/3 reduction in the server workload.
  arXiv  Detail & Related papers  (2024-10-14T17:38:41Z)
• Search for Efficient Large Language Models [52.98684997131108]
  Large Language Models (LLMs) have long held sway in the realms of artificial intelligence research. Weight pruning, quantization, and distillation have been embraced to compress LLMs, targeting memory reduction and inference acceleration. Most model compression techniques concentrate on weight optimization, overlooking the exploration of optimal architectures.
  arXiv  Detail & Related papers  (2024-09-25T21:32:12Z)
• FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model [48.33280660752336]
  Large language models (LLMs) show amazing performance on many domain-specific tasks after fine-tuning with some appropriate data. Many domain-specific data are privately distributed across multiple owners. We introduce FedBiOT, a resource-efficient LLM fine-tuning approach to federated learning.
  arXiv  Detail & Related papers  (2024-06-25T16:45:47Z)
• Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloads [0.2389598109913753]
  Training and using Large Language Models (LLMs) require large amounts of energy. This paper addresses the challenge of reducing energy consumption in data centers running LLMs. We propose a hybrid data center model that uses a cost-based scheduling framework to dynamically allocate tasks across hardware accelerators.
  arXiv  Detail & Related papers  (2024-04-25T11:24:08Z)
• Distributed Inference and Fine-tuning of Large Language Models Over The Internet [91.00270820533272]
  Large language models (LLMs) are useful in many NLP tasks and become more capable with size. These models require high-end hardware, making them inaccessible to most researchers. We develop fault-tolerant inference algorithms and load-balancing protocols that automatically assign devices to maximize the total system throughput.
  arXiv  Detail & Related papers  (2023-12-13T18:52:49Z)
• Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly [62.473245910234304]
  This paper takes a hardware-centric approach to explore how Large Language Models can be brought to modern edge computing systems. We provide a micro-level hardware benchmark, compare the model FLOP utilization to a state-of-the-art data center GPU, and study the network utilization in realistic conditions.
  arXiv  Detail & Related papers  (2023-10-04T20:27:20Z)
• FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs [57.12856172329322]
  We envision a decentralized system unlocking the potential vast untapped consumer-level GPU. This system faces critical challenges, including limited CPU and GPU memory, low network bandwidth, the variability of peer and device heterogeneity.
  arXiv  Detail & Related papers  (2023-09-03T13:27:56Z)
• The MIT Supercloud Dataset [3.375826083518709]
  We introduce the MIT Supercloud dataset which aims to foster innovative AI/ML approaches to the analysis of large scale HPC and datacenter/cloud operations. We provide detailed monitoring logs from the MIT Supercloud system, which include CPU and GPU usage by jobs, memory usage, file system logs, and physical monitoring data. This paper discusses the details of the dataset, collection methodology, data availability, and discusses potential challenge problems being developed using this data.
  arXiv  Detail & Related papers  (2021-08-04T13:06:17Z)
• Power Modeling for Effective Datacenter Planning and Compute Management [53.41102502425513]
  We discuss two classes of statistical power models designed and validated to be accurate, simple, interpretable and applicable to all hardware configurations and workloads. We demonstrate that the proposed statistical modeling techniques, while simple and scalable, predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse Power Distribution Units (more than 2000) using only 4 features.
  arXiv  Detail & Related papers  (2021-03-22T21:22:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.