Meta的大规模AI存储蓝图

Over the past several years, model capabilities and training dataset sizes have experienced exponential growth. During the past year or so, the time between new-frontier-model releases has gone down from months to weeks. Reliable and fast access to storage is important to both the speed and computational cost of this AI innovation. If AI is the brain, storage is the memory: Capability and speed are highly dependent on the size of memory and speed of retrieval.

在过去几年里，模型能力和训练数据集规模经历了指数级增长。在过去一年左右的时间里，新一代前沿模型的发布间隔从几个月缩短到了几周。可靠且快速的存储访问对于这项 AI 创新的速度和计算成本都至关重要。如果 AI 是大脑，那么存储就是记忆：能力和速度高度依赖于内存的大小和检索的速度。

Yet while AI compute performance has roughly tripled every two years, storage and interconnect performance growth have been more modest. As a result, storage bottlenecks continue to be one of the primary contributors to GPU stalls for AI workloads, directly impacting expenditures and time to market. Aside from GPU utilization, storage architecture also directly impacts the speed of iteration in AI research; with GPUs increasingly becoming geo-distributed and dataset sizes increasingly becoming massive, researchers spend a significant amount of time ingesting and moving data across regions, thus impacting research velocity. In this blog post, we discuss how Meta’s BLOB-storage architecture evolved to address two primary challenges: maximizing GPU utilization and maximizing research velocity.

然而，尽管AI计算性能大约每两年增长至原来的三倍，但存储和互连性能的增长却相对温和。因此，存储瓶颈仍然是导致AI工作负载中GPU停滞的主要原因之一，直接影响支出和上市时间。除了GPU利用率之外，存储架构还直接影响AI研究的迭代速度；随着GPU日益呈现跨地域分布式部署，数据集规模日益庞大，研究人员花费大量时间摄取和跨区域移动数据，从而影响了研究速度。在这篇博客文章中，我们将讨论Meta的BLOB存储架构是如何演进以应对两个主要挑战的：最大化GPU利用率和最大化研究速度。

Storage Architecture Overview

存储架构概述

Meta operates hundreds of exabyte-scale storage clusters that serve all of Meta’s external and internal products, including Facebook, Instagram, Reality Labs, Meta AI, Ads, Data Warehouse, and internal Databases. Our storage service exposes object storage, file systems, and block-device APIs, and these API abstractions are built on top of a horizontally scalable fo...