A Survey of Adaptive Optimization in Virtual Machines
Arnold, M.
Fink, S.J.
Grove, D.
Hind, M.
Sweeney, P.F.
IBM T. J. Watson Res. Center, Hawthorne, NY, USA;
This paper appears in: Proceedings of the IEEE
Publication Date: Feb. 2005
Volume: 93,
Issue: 2
On page(s): 449-466
ISSN: 0018-9219
INSPEC Accession Number: 8261077
Digital Object Identifier: 10.1109/JPROC.2004.840305
Posted online: 2005-06-27 11:10:51.0
Abstract
Virtual machines face significant performance challenges beyond those confronted by traditional static optimizers. First, portable program representations and dynamic language features, such as dynamic class loading, force the deferral of most optimizations until runtime, inducing runtime optimization overhead. Second, modular program representations preclude many forms of whole-program interprocedural optimization. Third, virtual machines incur additional costs for runtime services such as security guarantees and automatic memory management. To address these challenges, vendors have invested considerable resources into adaptive optimization systems in production virtual machines. Today, mainstream virtual machine implementations include substantial infrastructure for online monitoring and profiling, runtime compilation, and feedback-directed optimization. As a result, adaptive optimization has begun to mature as a widespread production-level technology. This paper surveys the evolution and current state of adaptive optimization technology in virtual machines.
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