J
Jennifer M. Anderson
Researcher at VMware
Publications - 29
Citations - 3952
Jennifer M. Anderson is an academic researcher from VMware. The author has contributed to research in topics: Compiler & Virtual machine. The author has an hindex of 26, co-authored 29 publications receiving 3909 citations. Previous affiliations of Jennifer M. Anderson include Stanford University & University of California, Irvine.
Papers
More filters
Journal ArticleDOI
Maximizing multiprocessor performance with the SUIF compiler
Mary Hall,Jennifer M. Anderson,Jennifer M. Anderson,Saman Amarasinghe,Saman Amarasinghe,Brian R. Murphy,Brian R. Murphy,Shih-Wei Liao,Shih-Wei Liao,Edouard Bugnion,Edouard Bugnion,Monica S. Lam,Monica S. Lam,Monica S. Lam +13 more
TL;DR: In this paper, the authors describe automatic parallelization techniques in the SUIF (Stanford University Intermediate Format) compiler that result in good multiprocessor performance for array-based numerical programs.
Journal ArticleDOI
SUIF: an infrastructure for research on parallelizing and optimizing compilers
Robert P. Wilson,Robert S. French,Christopher S. Wilson,Saman Amarasinghe,Jennifer M. Anderson,Steve W. K. Tjiang,Shih-Wei Liao,Chau-Wen Tseng,Mary Hall,Monica S. Lam,John L. Hennessy +10 more
TL;DR: The SUIF compiler is built into a powerful, flexible system that may be useful for many other researchers and the authors invite you to use and welcome your contributions to this infrastructure.
Journal ArticleDOI
Continuous profiling: where have all the cycles gone?
Jennifer M. Anderson,Lance M. Berc,Jeffrey Dean,Sanjay Ghemawat,Monika Henzinger,Shun-Tak Albert Leung,Richard L. Sites,Mark T. Vandevoorde,Carl A. Waldspurger,William E. Weihl +9 more
TL;DR: The Digital Continuous Profiling Infrastructure is a sampling-based profiling system designed to run continuously on production systems, supporting multiprocessors, works on unmodified executables, and collects profiles for entire systems, including user programs, shared libraries, and the operating system kernel.
Proceedings ArticleDOI
Global optimizations for parallelism and locality on scalable parallel machines
TL;DR: A compiler algorithm that automatically finds computation and data decompositions that optimize both parallelism and locality that is designed for use with both distributed and shared address space machines.
Proceedings ArticleDOI
Data and computation transformations for multiprocessors
TL;DR: This work has developed the first compiler system that fully automatically parallelizes sequential programs and changes the original array layouts to improve memory system performance, and shows that the compiler can effectively optimize parallelism in conjunction with memory subsystem performance.