The impact of paravirtualized memory hierarchy on linear algebra computational kernels and software

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The impact of paravirtualized memory hierarchy on linear algebra computational kernels and software

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High Performance Distributed Computing archive
Proceedings of the 17th international symposium on High performance distributed computing table of contents

Boston, MA, USA

SESSION: Virtual machines table of contents

Pages 141-152

Year of Publication: 2008

ISBN:978-1-59593-997-5

Authors

Lamia Youseff	University of California, Santa Barbara, Santa Barbara, CA, USA
Keith Seymour	University of Tennessee, Knoxville, TN, USA
Haihang You	University of Tennessee, Knoxville, TN, USA
Jack Dongarra	University of Tennessee, Knoxville, TN, USA
Rich Wolski	University of California, Santa Barbara, Santa Barbara, CA, USA

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ACM: Association for Computing Machinery
SIGARCH: ACM Special Interest Group on Computer Architecture

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ACM New York, NY, USA

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Downloads (6 Weeks): 17, Downloads (12 Months): 167, Citation Count: 1

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ABSTRACT

Previous studies have revealed that paravirtualization imposes minimal performance overhead on High Performance Computing (HPC) workloads, while exposing numerous benefits for this field. In this study, we are investigating the memory hierarchy characteristics of paravirtualized systems and their impact on automatically-tuned software systems. We are presenting an accurate characterization of memory attributes using hardware counters and user-process accounting. For that, we examine the proficiency of ATLAS, a quintessential example of an autotuning software system, in tuning the BLAS library routines for paravirtualized systems. In addition, we examine the effects of paravirtualization on the performance boundary. Our results show that the combination of ATLAS and Xen paravirtualization delivers native execution performance and nearly identical memory hierarchy performance profiles. Our research thus exposes new benefits to memory-intensive applications arising from the ability to slim down the guest OS without influencing the system performance. In addition, our findings support a novel and very attractive deployment scenario for computational science and engineering codes on virtual clusters and computational clouds.

REFERENCES

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