Niraj Tolia

GViM: GPU-accelerated virtual machines

The use of virtualization to abstract underlying hardware can aid in sharing such resources and in efficiently managing their use by high performance applications. Unfortunately, virtualization also prevents efficient access to accelerators, such as Graphics Processing Units (GPUs), that have become critical components in the design and architecture of HPC systems. Supporting General Purpose computing on GPUs (GPGPU) with accelerators from different vendors presents significant challenges due to proprietary programming models, heterogeneity, and the need to share accelerator resources between different Virtual Machines (VMs). To address this problem, this paper presents GViM, a system designed for virtualizing and managing the resources of a general purpose system accelerated by graphics processors. Using the NVIDIA GPU as an example, we discuss how such accelerators can be virtualized without additional hardware support and describe the basic extensions needed for resource management. Our evaluation with a Xen-based implementation of GViM demonstrate efficiency and flexibility in system usage coupled with only small performance penalties for the virtualized vs. non-virtualized solutions.

A cyber-physical systems approach to energy management in data centers

This paper presents a new control strategy for data centers that aims to optimize the trade-off between maximizing the payoff from the provided quality of computational services and minimizing energy costs for computation and cooling. The data center is modeled as two interacting dynamic networks: a computational (cyber) network representing the distribution and flow of computational tasks, and a thermal (physical) network characterizing the distribution and flow of thermal energy. To make the problem tractable, the control architecture is decomposed hierarchically according to time-scales in the thermal and computational network dynamics, and spatially, reflecting weak coupling between zones in the data center. Simulation results demonstrate the effectiveness of the proposed coordinated control strategy relative to traditional approaches in which the cyber and physical resources are controlled independently.

Opportunities and challenges to unify workload, power, and cooling management in data centers

Independent optimization for workload and power management, and active cooling control have been studied extensively to improve data center energy efficiency. Recently, proposals have started to advocate unified workload, power, and cooling management for further energy savings. In this paper, we study this problem with the objectives of both saving energy and capping power. We present the detailed models derived in our previous work from experiments on an blade enclosure system that can be representative of a data center, discuss the optimization opportunities for coordinated power and cooling management, and the challenges for controller design. We then propose a few design principles and examples for unified workload management, power minimization, and power capping. Our simulation-based evaluation shows that the controllers can cap the total power consumption while maintaining the thermal conditions and improve the overall energy efficiency. We argue that the same opportunities, challenges, and designs are also generally applicable to data center level management.