To alleviate the overhead of moving massive data in modern data centers, Data Processing Units (DPUs) are increasingly deployed to offload NVMe-over-Fabrics (NVMeoF) remote storage access from the CPU. However, current heterogeneous CPU-DPU systems suffer from inflexible resource partitioning, leading to suboptimal utilization of computational power and network bandwidth. In this paper, we present DPIO, a unified I/O processing stack designed to harmonize the collaboration between CPU and DPU in NVMeoF environments. By implementing fine-grained task orchestration, DPIO effectively bridges the performance gap between DPU offloading and CPU processing, achieving near-optimal system performance across diverse workloads. Our real-world implementation demonstrates that DPIO delivers over 2 × higher throughput compared to conventional DPU-centric NVMeoF approaches, significantly enhancing the efficiency of data-intensive applications.
Wenhao Gu, Xuchao Xie, Yujuan Tan et al.· IEEE International Symposium...· 0 citations
Graph-based approximate nearest neighbor search (ANNS) is increasingly used in vector databases and retrieval-augmented generation services, but large vector indexes often exceed the memory capacity of a single GPU server. NVMe over Fabrics (NVMe-oF) provides an attractive storage-disaggregation substrate, yet existing remote storage paths are still largely CPU-centered: the CPU forms I/O requests, drives transport progress, and determines when GPU computation can resume. This organization is poorly matched to graph ANNS, where the next data access is discovered inside GPU graph traversal. This paper presents GORIO, a system study that extends GPU-centered local I/O to remote storage and specializes the resulting substrate for graph ANNS over NVMe-oF. GORIO keeps query evolution, page-miss generation, pending-query state, and resume decisions on the GPU, while the CPU acts only as an NVMe-oF transport and completion proxy. The design has two layers: a GPU-direct remote I/O path that turns local page-cache misses into split-phase remote operations, and ANNS-specific scheduling mechanisms that overlap graph traversal with remote page service. On a SIFT1M DiskANN-style graph workload over an RDMA NVMe-oF path, GORIO is 1.31X faster than the state-of-the-art remote-I/O reference path and 4.89X faster than the direct remote page-cache path. These results demonstrate a concrete GPU-centered remote I/O substrate for graph ANNS.
Gen Zhang, Wenhao Gu, Shang-Rong Huang et al.· 0 citations
