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NURA: A framework for supporting non-uniform resource accesses in gpus

Darabi, S ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1145/3508036
  3. Publisher: Association for Computing Machinery , 2022
  4. Abstract:
  5. Multi-application execution in Graphics Processing Units (GPUs), a promising way to utilize GPU resources, is still challenging. Some pieces of prior work (e.g., spatial multitasking) have limited opportunity to improve resource utilization, while other works, e.g., simultaneous multi-kernel, provide fine-grained resource sharing at the price of unfair execution. This paper proposes a new multi-application paradigm for GPUs, called NURA, that provides high potential to improve resource utilization and ensures fairness and Quality-of-Service (QoS). The key idea is that each streaming multiprocessor (SM) executes Cooperative Thread Arrays (CTAs) belong to only one application (similar to the spatial multi-tasking) and shares its unused resources with the SMs running other applications demanding more resources. NURA handles resource sharing process mainly using a software approach to provide simplicity, low hardware cost, and flexibility. We also perform some hardware modifications as an architectural support for our software-based proposal. We conservatively analyze the hardware cost of our proposal, and observe less than 1.07% area overhead with respect to the whole GPU die. Our experimental results over various mixes of GPU workloads show that NURA improves GPU system throughput by 26% compared to state-of-the-art spatial multi-tasking, on average, while meeting the QoS target. In terms of fairness, NURA has almost similar results to spatial multitasking, while it outperforms simultaneous multi-kernel by an average of 76%. © 2022 ACM
  6. Keywords:
  7. Cloud computing ; Gpu ; Multitasking ; Quality of services ; Streaming multiprocessor ; System throughput ; Computer graphics ; Graphics processing unit ; Multiprocessing systems ; Program processors ; Quality of service ; Cloud-computing ; Gpu ; Graphics processing ; Multi-application ; Multi-kernel ; Processing units ; Quality-of-service ; Resources utilizations ; Streaming multiprocessors ; System throughput ; Multitasking
  8. Source: Proceedings of the ACM on Measurement and Analysis of Computing Systems ; Volume 6, Issue 1 , 2022 ; 24761249 (ISSN)
  9. URL: https://dl.acm.org/doi/10.1145/3508036