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Memory-Aware Eager Co-Scheduling for Multi-Tenant GPU Environments
Jeongjae Kim, Yunchae Choi, Hwansoo Han
http://doi.org/10.5626/JOK.2024.51.3.210
In a multi-tenant GPU environment, multiple applications are co-located on a single GPU to maximize utilization and throughput. However, co-location can lead to out-of-memory errors. Previous research addressed this problem by scheduling tasks that do not exceed the total GPU memory capacity. Our research introduces two novel methods that allow the co-location of additional tasks on a GPU while effectively preventing out-of-memory errors. Our approach involves immediate deallocation of unused memory within tasks, freeing up memory early on the GPU. This enables additional concurrent execution of multiple tasks on the GPU. Furthermore, by over-subscribing Unified Memory, tasks are scheduled to tolerate memory usage that exceeds the total GPU memory capacity. With our proposed schemes, it is feasible to reduce the execution time of multiple tasks compared to previous scheduling approaches and each scheme shows performance improvement of 7.3% and 1.9%, respectively, compared to prior research.
Database Tuning Techniques to Mitigate SSD-internal Interference among Multi-tenant Databases
Seung-Jin Oh, Jong-Hyeok Park, Sang-Won Lee
http://doi.org/10.5626/JOK.2022.49.5.388
In a multi-tenant environment, multi-tenants share an SSD(Solid State Drive) as their storage device. Multi-tenants with different IO characteristics can interfere with each other at the channel level in terms of storage performance. In this paper, to harness the full potential of channel level parallelism of SSD, we proposed two tuning techniques: page size alignment and increasing readahead size. We measured transaction throughput and latency (execution time) while running Linkbench and TPC-H simultaneously in Docker container-based environment. Our evaluation showed that the page size alignment technique reduced unnecessary data padding/division overhead and prevented unnecessary IO requests from occupying the channel to mitigate interference, improving the performance of the Linkbench and the TPC-H. However, increasing readahead size raised SSD internal channel occupancy of sequential read requests and reduced the interference of the Linkbench, whose request size was small and access type was random. Thus, it only improved the TPC-H in terms of query execution performance.
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