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Title: Exploring Machine Learning Techniques For Dynamic Modeling on Future Exascale Systems

Future exascale systems must be optimized for both power and performance at scale in order to achieve DOE’s goal of a sustained petaflop within 20 Megawatts by 2022 [1]. Massive parallelism of the future systems combined with complex memory hierarchies will form a barrier to efficient application and architecture design. These challenges are exacerbated with emerging complex architectures such as GPGPUs and Intel Xeon Phi as parallelism increases orders of magnitude and system power consumption can easily triple or quadruple. Therefore, we need techniques that can reduce the search space for optimization, isolate power-performance bottlenecks, identify root causes for software/hardware inefficiency, and effectively direct runtime scheduling.
Authors:
; ;
Publication Date:
OSTI Identifier:
1178908
Report Number(s):
PNNL-SA-105672
KJ0402000
DOE Contract Number:
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: Modeling & Simulation of Exascale Systems & Applications: Workshop on Modeling & Simulation of Exascale Systems & Applications, September 18-19, 2013, Seattle, Washington
Publisher:
US Department of Energy, Office of Advanced Scientific Computing Research, Washington DC, United States(US).
Research Org:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English