Fairness Notions on Hardware Resource Configuration
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To meet performance and energy efficiency demand of modern workloads, specialized hardware accelerators implemented on FPGAs or ASICs have found adoption in modern servers and Systems-on-Chip (SoC). These hardware accelerators cater to a wide range of applications such as Machine Learning, databases, analytics and networking. Prior studies have shown excessive underutilization, utilization ranging from 60% to as low as 20%. Sharing hardware among multiple users can mitigate underutilization but makes achieving QoS requirements a challenge. In this work, we explore hardware sharing on a GPU and employ game theory to present a formal model of sharing. We present notions of fairness, a performance model for heterogeneous hardware systems, and present a novel market-based mechanism to allocate and configure hardware resource. We implement the design on the open source Vortex GPU, and evaluate the system with 5 machine learning workloads – Resnet, AlexNet, YoloNet, K-Means clustering and multi-layer perceptron training. The evaluation showed that the market-based mechanism meets proportional QoS and we provide a theoretical guarantee. We observe up to 2x speedup and up to 10% higher utilization compared to traditional methods of providing equal resources. The system also chooses hardware configuration that maximizes the collective welfare of the users.
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Aravind Vellora Vayalapra (2023). Fairness Notions on Hardware Resource Configuration. UWSpace. http://hdl.handle.net/10012/20063