Candidate: Aravind Vellora Vayalapra
Date: October 6, 2023
Time: 1:30 PM
Place: Remote Attendance
Supervisor(s): Nachiket Kapre, Seyed Majid Zahedi
Abstract:
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 and speedup as high as 2x. The system
also chooses hardware configuration that maximizes the collective welfare of the users.