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  1. Proceedings of the 6th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments (VEE '10)
  2. Novel online profiling for virtual machines
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Capability wrangling made easy: debugging on a microkernel with valgrind
Improving compiler-runtime separation with XIR
Neon: system support for derived data management
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Efficient runtime tracking of allocation sites in Java
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Transistors to toys: teaching systems to freshmen
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VMKit: a substrate for managed runtime environments
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Supporting soft real-time tasks in the xen hypervisor
Evaluation of a just-in-time compiler retrofitted for PHP
Dynamic binary translation specialized for embedded systems
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Novel online profiling for virtual machines

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Novel online profiling for virtual machines

Content Provider ACM Digital Library
Author Namjoshi, Manjiri A. Kulkarni, Prasad A.
Abstract Application profiling is a popular technique to improve program performance based on its behavior. Offline profiling, although beneficial for several applications, fails in cases where prior program runs may not be feasible, or if changes in input cause the profile to not match the behavior of the actual program run. Managed languages, like Java and C\#, provide a unique opportunity to overcome the drawbacks of offline profiling by generating the profile information online during the current program run. Indeed, online profiling is extensively used in current VMs, especially during selective compilation to improve program startup performance, as well as during other feedback-directed optimizations. In this paper we illustrate the drawbacks of the current reactive mechanism of online profiling during selective compilation. Current VM profiling mechanisms are slow -- thereby delaying associated transformations, and estimate future behavior based on the program's immediate past -- leading to potential misspeculation that limit the benefits of compilation. We show that these drawbacks produce an average performance loss of over 14.5% on our set of benchmark programs, over an ideal offline approach that accurately compiles the hot methods early. We then propose and evaluate the potential of a novel strategy to achieve similar performance benefits with an online profiling approach. Our new online profiling strategy uses early determination of loop iteration bounds to predict future method hotness. We explore and present promising results on the potential, feasibility, and other issues involved for the successful implementation of this approach.
Starting Page 133
Ending Page 144
Page Count 12
File Format PDF
ISBN 9781605589107
DOI 10.1145/1735997.1736016
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2010-03-17
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Java Online profiling Virtual machines
Content Type Text
Resource Type Article
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