Auto-Scaling Heterogeneous Neural Processing Units for Energy and Cost-Efficient LLM Serving
Hardware Architecture
Summary
The authors studied different versions of specialized chips called NPUs, which help run large language models in the cloud. They found that mixing different NPU types can save energy and reduce costs, but it's hard to manage them well. So, they created NeuScale, a system that automatically picks and adjusts these chips to best fit the needs of the language models. Their tests showed NeuScale improves efficiency and keeps services running smoothly.
Authors
Yuqi Xue, Jichuan Chang, Jian Huang
Abstract
To meet the ever-increasing computing demands of large language model (LLM) services, modern cloud platforms have widely deployed neural processing units (NPUs). These NPU chips have been developed and evolved at an incredibly fast pace, this inevitably produces heterogeneous compute pools backed by different versions of NPU chips. Unfortunately, due to the lack of system and architecture support for managing NPU heterogeneity in the cloud, it is unclear how to best utilize heterogeneous NPUs to maximize the energy and cost efficiency for LLM services. In this paper, we first conduct a characterization study of various generations of real NPU chips to demonstrate the potential benefits on energy/cost efficiency and performance by utilizing heterogeneous NPU chips. To realize these benefits, we present NeuScale, an auto-scaling framework to automatically exploit heterogeneous NPUs for cloud platforms. NeuScale manages heterogeneous NPU resources with a new vPod abstraction, which abstracts the core hardware parameters of different NPU versions and provides compatibility with existing ML frameworks. It makes the best-fit vPod allocations for different LLM inference requests using an intuitive and lightweight roofline-based analysis. It supports fine-grained dynamic NPU resource provisioning by adjusting both the vPod configuration (i.e., scaling up/down) and the number of vPods (e.g., scaling in/out). To validate the benefits of NeuScale at scale, we implement it with a production-level NPU simulator. Our evaluation with popular LLMs shows that NeuScale can significantly improve cost efficiency and service-level objective (SLO) satisfaction rate by best utilizing heterogeneous NPU resources.