S
Sally A. McKee
Researcher at Chalmers University of Technology
Publications - 151
Citations - 6278
Sally A. McKee is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Cache & Memory controller. The author has an hindex of 33, co-authored 151 publications receiving 5811 citations. Previous affiliations of Sally A. McKee include Cornell University & University of Utah.
Papers
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Journal ArticleDOI
Hitting the memory wall: implications of the obvious
William A. Wulf,Sally A. McKee +1 more
TL;DR: This work proposes an exact analysis, removing all remaining uncertainty, based on model checking, using abstract-interpretation results to prune down the model for scalability, and notably improves precision upon classical abstract interpretation at reasonable cost.
Proceedings ArticleDOI
Reflections on the memory wall
TL;DR: The short Computer Architecture News note that coined the phrase "Memory Wall" is reviewed, including the motivation behind the note, the context in which it was written, and the controversy it sparked.
Proceedings ArticleDOI
Efficiently exploring architectural design spaces via predictive modeling
TL;DR: This work builds accurate, confident predictive design-space models that produce highly accurate performance estimates for other points in the space, can be queried to predict performance impacts of architectural changes, and are very fast compared to simulation, enabling efficient discovery of tradeoffs among parameters in different regions.
Journal ArticleDOI
Real time power estimation and thread scheduling via performance counters
TL;DR: This work analytically derive functions for real-time estimation of processor and system power consumption using performance counter data on real hardware, and leverages the model to implement a simple, power-aware thread scheduler.
Proceedings ArticleDOI
Methods of inference and learning for performance modeling of parallel applications
TL;DR: This work constructs and compares two classes of effective predictive models: piecewise polynomial regression and artifical neural networks, and applies statistical techniques such as clustering, association, and correlation analysis, to understand the application parameter space better.