A Survey of Adaptive Optimization in Virtual Machines Export

@article{citeulike:218259, 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.}, author = {Arnold, M. and Fink, S. J. and Grove, D. and Hind, M. and Sweeney, P. F.}, booktitle = {Proceedings of the IEEE}, citeulike-article-id = {218259}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/JPROC.2004.840305}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1386662}, doi = {10.1109/JPROC.2004.840305}, journal = {Proceedings of the IEEE}, keywords = {compilers, jit}, number = {2}, pages = {449--466}, posted-at = {2009-06-27 18:05:27}, priority = {0}, title = {A Survey of Adaptive Optimization in Virtual Machines}, url = {http://dx.doi.org/10.1109/JPROC.2004.840305}, volume = {93}, year = {2005} }

TY - JOUR ID - citeulike:218259 L3 - citeulike-article-id:218259 N2 - 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. IS - 2 JF - Proceedings of the IEEE EP - 466 TI - A Survey of Adaptive Optimization in Virtual Machines VL - 93 T2 - Proceedings of the IEEE SP - 449 KW - compilers KW - jit AU - Arnold, M AU - Fink, SJ AU - Grove, D AU - Hind, M AU - Sweeney, PF PY - 2005/// UR - http://dx.doi.org/10.1109/JPROC.2004.840305 ER -