System-level virtualization has been a research topic since the 70's but regained popularity during the past few years because of the availability of efficient solution such as Xen and the implementation of hardware support in commodity processors (e.g. Intel-VT, AMD-V).However, a majority of system-level virtualization projects is guided by the server consolidation market. As a result, current virtualization solutions appear to not be suitable for high performance computing (HPC) which is typically based on large-scale systems. On another hand there is significant interest in exploiting virtual machines (VMs) within HPC for a number of other reasons. By virtualizing the machine, one is able to run a variety of operating systems and environments as needed by the applications. Virtualization allows users to isolate workloads, improving security and reliability. It is also possible to support nonnative environments and/or legacy operating environments through virtualization. In addition, it is possible to balance work loads, use migration techniques to relocate applications from failing machines, and isolate fault systems for repair.This document presents the challenges for the implementation of a system-level virtualization solution for HPC. It also presents a brief survey of the different approaches and

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Abstract. Virtualization technology has been gaining acceptance in the scientific community due to its overall flexibility in running HPC applications. It has been reported that a specific class of applications is better suited to a particular type of virtualization scheme or implementation. For example, Xen has been shown to perform with little overhead for compute-bound applications. Such a study, although useful, does not allow us to generalize conclusions beyond the performance analysis of that application which is explicitly executed. An explanation of why the generalization described above is difficult, may be due to the versatility in applications, which leads to different overheads in virtual environments. For example, two similar applications may spend disproportionate amount of time in their respective library code when run in virtual environments. In this paper, we aim to study such potential causes by investigating the behavior and identifying patterns of various overheads for HPC benchmark applications. Based on the investigation of the overhead profiles for different benchmarks, we aim to address questions such as: Are the overhead profiles for a particular type of benchmarks (such as compute-bound) similar or are there grounds to conclude otherwise?

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