{"ID":2842724,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.09143","arxiv_id":"2511.09143","title":"Flex-MIG: Enabling Distributed Execution on MIG","abstract":"GPU clusters in multi-tenant settings often suffer from underutilization, making GPU-sharing technologies essential for efficient resource use. Among them, NVIDIA Multi-Instance GPU (MIG) has gained traction for providing hardware-level isolation that enables concurrent workloads without interference. However, MIG's hardware rigidity and the conventional one-to-one allocation model jointly lead to severe fragmentation and cluster-wide underutilization. We present Flex-MIG, a software-only framework that replaces one-to-one with a one-to-many allocation model and enables host-shared-memory collectives across MIG instances without hardware modification. Flex-MIG eliminates drain-required reconfiguration, reduces fragmentation, and improves makespan by up to 17% across diverse traces, showing that rethinking MIG's operational model as a software-coordinated layer substantially improves cluster efficiency.","short_abstract":"GPU clusters in multi-tenant settings often suffer from underutilization, making GPU-sharing technologies essential for efficient resource use. Among them, NVIDIA Multi-Instance GPU (MIG) has gained traction for providing hardware-level isolation that enables concurrent workloads without interference. However, MIG's ha...","url_abs":"https://arxiv.org/abs/2511.09143","url_pdf":"https://arxiv.org/pdf/2511.09143v2","authors":"[\"Myeongsu Kim\",\"Ikjun Yeom\",\"Younghoon Kim\"]","published":"2025-11-12T09:29:50Z","proceeding":"cs.DC","tasks":"[\"cs.DC\"]","methods":"[]","has_code":false}