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@INPROCEEDINGS{Calotoiu_ea:2014:performance_models, author = {Calotoiu, Alexandru and Hoefler, Torsten and Wolf, Felix}, month = aug, title = {Mass-producing Insightful Performance Models}, booktitle = {Workshop on Modeling & Simulation of Systems and Applications, University of Washington}, year = {2014}, address = {Seattle, Washington}, url = {http://hpc.pnl.gov/modsim/2014/index.shtml}, abstract = {Many parallel applications suffer from latent performance limitations that may prevent them from scaling to larger machine sizes. Often, such scalability bugs manifest themselves only when an attempt to scale the code is actually being made -a point where remediation can be difficult. However, creating performance models that would allow such issues to be pinpointed earlier is so laborious that application developers attempt it at most for a few selected kernels, running the risk of missing harmful bottlenecks. By automatically generating empirical performance models for each function in the program, we make this powerful methodology easier to use and expand its coverage. This article gives an overview of the method and assesses its potential.} }
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