Hunseong Lim | Searcharxiv

arXiv2026

GriNNder: Breaking the Memory Capacity Wall in Full-Graph GNN Training with Storage Offloading

Full-graph training of graph neural networks (GNNs) is widely used as it enables direct validation of algorithmic improvements by preserving complete neighborhood information. However, it typically requires multiple GPUs or servers, incurring substantial hardware and inter-device communication costs. While existing single-server methods reduce infrastructure requirements, they remain constrained by GPU and host memory capacity as graph sizes increase. To address this limitation, we introduce GriNNder, which is the first work to leverage storage devices to enable full-graph training even with limited memory. Because modern NVMe SSDs offer multi-terabyte capacities and bandwidths exceeding 10 GB/s, they provide an appealing option when memory resources are scarce. Yet, directly applying storage-based methods from other domains fails to address the unique access patterns and data dependencies in full-graph GNN training. GriNNder tackles these challenges by structured storage offloading (SSO), a framework that manages the GPU-host-storage hierarchy through coordinated cache, (re)gather, and bypass mechanisms. To realize the framework, we devise (i) a partition-wise caching strategy for host memory that exploits the observation on cross-partition dependencies, (ii) a regathering strategy for gradient computation that eliminates redundant storage operations, and (iii) a lightweight partitioning scheme that mitigates the memory requirements of existing graph partitioners. In experiments performed over various models and datasets, GriNNder achieves up to 9.78x speedup over state-of-the-art baselines and throughput comparable to distributed systems, enabling previously infeasible large-scale full-graph training even on a single GPU.

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arXiv2025

PathWeaver: A High-Throughput Multi-GPU System for Graph-Based Approximate Nearest Neighbor Search

Graph-based Approximate Nearest Neighbor Search (ANNS) is widely adopted in numerous applications, such as recommendation systems, natural language processing, and computer vision. While recent works on GPU-based acceleration have significantly advanced ANNS performance, the ever-growing scale of datasets now demands efficient multi-GPU solutions. However, the design of existing works overlooks multi-GPU scalability, resulting in naive approaches that treat additional GPUs as a means to extend memory capacity for large datasets. This inefficiency arises from partitioning the dataset and independently searching for data points similar to the queries in each GPU. We therefore propose PathWeaver, a novel multi-GPU framework designed to scale and accelerate ANNS for large datasets. First, we propose pipelining-based path extension, a GPU-aware pipelining mechanism that reduces prior work's redundant search iterations by leveraging GPU-to-GPU communication. Second, we design ghost staging that leverages a representative dataset to identify optimal query starting points, reducing the search space for challenging queries. Finally, we introduce direction-guided selection, a data selection technique that filters irrelevant points early in the search process, minimizing unnecessary memory accesses and distance computations. Comprehensive evaluations across diverse datasets demonstrate that PathWeaver achieves 3.24$\times$ geomean speedup and up to 5.30$\times$ speedup on 95% recall rate over state-of-the-art multi-GPU-based ANNS frameworks.

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