Designing a highly-scalable operating system: the Blue Gene/L story
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
The blue gene/L supercomputer: a hardware and software story
International Journal of Parallel Programming
Tuning parallel applications in parallel
Parallel Computing
From source code to runtime behaviour: Software metrics help to select the computer architecture
Knowledge-Based Systems
Using computer simulation to predict the performance of multithreaded programs
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
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Consistently growing architectural complexity and machine scales make the creation of accurate performance models for large-scale applications increasingly challenging. Traditional analytic models are difficult and time consuming to construct, and are often unable to capture full system and application complexity. To address these challenges, we automatically build models based on execution samples. We use multilayer neural networks, because they can represent arbitrary functions and handle noisy inputs robustly. In this paper we focus on two well-known parallel applications whose variations in execution times are not well understood: SMG 2000, a semicoarsening multigrid solver, and HPL, an open-source implementation of LINPACK. We sparsely sample performance data on two radically different platforms across large, multidimensional parameter spaces and show that our models based on these data can predict performance within 2% to 7% of actual application runtimes. Copyright © 2007 John Wiley & Sons, Ltd.