Fast Cloud Storage for AI Jobs via Grouped I/O API with Transparent Read/Write Optimizations

The emergence of AI workloads has placed rigorous bandwidth requirements on cloud storage, which are challenging to meet due to inherent hardware restrictions in cost-efficient disaggregated storage architectures, as well as the non-triviality of implementing application-tailored optimizations.

This paper presents AITURBO, a cloud storage system for AI jobs with high bandwidth demands. AITURBO first utilizes the high-bandwidth compute fabric between accelerators to meet AI applications' bandwidth demands without incurring additional storage cost. AITURBO further introduces a simple yet powerful grouped I/O API that allows AITURBO to automatically derive optimized read and write plans at the storage layer. These plans enable optimizations that are comparable or better than application-level ones, because they capture common I/O patterns in AI workloads and have a holistic view from the storage layer's perspective. Under common AI workloads such as checkpoint reads and writes and KV-cache reads, AITURBO achieves comparable or better performance than state-of-the-art systems, with and without application-level optimizations, including systems such as Megatron, Gemini, and Mooncake, typically with minimal application-level code changes. AITURBO has been deployed in training jobs in HUAWEI's production cloud to support efficient training workloads.