Improving Auto-Tuning Convergence Times with Dynamically Generated Predictive Performance Models

Author(s):  
James Price ◽  
Simon McIntosh-Smith
2021 ◽  
Author(s):  
Tong Shu ◽  
Yanfei Guo ◽  
Justin Wozniak ◽  
Xiaoning Ding ◽  
Ian Foster ◽  
...  

2014 ◽  
Vol 81 ◽  
pp. 255-269 ◽  
Author(s):  
Franci Pusavec ◽  
Ashish Deshpande ◽  
Shu Yang ◽  
Rachid M'Saoubi ◽  
Janez Kopac ◽  
...  

1989 ◽  
Vol 33 (2) ◽  
pp. 96-100 ◽  
Author(s):  
Christopher D. Wickens ◽  
Inge Larish ◽  
Aaron Contorer

This symposium presents five models that predict how performance of multiple tasks will interact in complex task scenarios. The models are discussed, in part, in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are empirically validated in a multitask helicopter flight simulation reported in the present paper. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks. The potential gains to be made multiple resource assumptions remain uncertain.


2009 ◽  
Vol 19 (04) ◽  
pp. 619-639 ◽  
Author(s):  
KEI DAVIS ◽  
KEVIN J. BARKER ◽  
DARREN J. KERBYSON

We present predictive performance models of two of the petascale applications, S3D and GTC, from the DOE Office of Science workload. We outline the development of these models and demonstrate their validation on an Opteron/Infiniband cluster and the pre-upgrade ORNL Jaguar system (Cray XT3/XT4). Given the high accuracy of the full application models, we predict the performance of the Jaguar system after the upgrade of its nodes, and subsequently compare this to the actual performance of the upgraded system. We then analyze the performance of the system based on the models to quantify bottlenecks and potential optimizations. Finally, the models are used to quantify the benefits of alternative node allocation strategies, and to quantify performance degradation resulting from inter-process competition for network resources.


2000 ◽  
Vol 147 (3) ◽  
pp. 61 ◽  
Author(s):  
V. Cortellessa ◽  
G. Iazeolla ◽  
R. Mirandola

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