STAR: Sub-Entry Sharing-Aware TLB for Multi-Instance GPU
Bingyao Li, Yueqi Wang, Tianyu Wang, Lieven Eeckhout, Jun Yang, Aamer Jaleel, Xulong Tang · 2024
NVIDIA's Multi-Instance GPU (MIG) technology enables partitioning GPU computing power and memory into sep-arate hardware instances, providing complete isolation including compute resources, caches, and memory. However, prior work identifies that MIG does not partition the last-level TLB (i.e., L3 TLB), which remains shared among all instances. To enhance TLB reach, NVIDIA GPUs reorganized the TLB structure with 16 sub-entries in each L3 TLB entry that have a one-to-one mapping to the address translations for 16 pages of size 64 KB located within the same 1 MB aligned range. Our comprehensive investigation of address translation efficiency in MIG identifies two main issues caused by L3 TLB sharing interference: (i) it results in performance degradation for co-running applications, and (ii) TLB sub-entries are not fully utilized before eviction. Based on this observation, we propose STAR to improve the utilization of TLB sub-entries through dynamic sharing of TLB entries across multiple base addresses. STAR evaluates TLB entries based on their sub-entry utilization to optimize address translation storage, dynamically adjusting between a shared and non-shared state to cater to current demand. We show that STAR improves overall performance by an average of 28.7% across various multi-tenant workloads.