Scaling Size and Parameter Spaces in Variability-Aware Software Performance Models (T)

In software performance engineering, what-if scenarios, architecture optimization, capacity planning, run-time adaptation, and uncertainty management of realistic models typically require the evaluation of many instances. Effective analysis is however hindered by two orthogonal sources of complexity. The first is the infamous problem of state space explosion — the analysis of a single model becomes intractable with its size. The second is due to massive parameter spaces to be explored, but such that computations cannot be reused across model instances. In this paper, we efficiently analyze many queuing models with the distinctive feature of more accurately capturing variability and uncertainty of execution rates by incorporating general (i.e., non-exponential) distributions. Applying product-line engineering methods, we consider a family of models generated by a core that evolves into concrete instances by applying simple delta operations affecting both the topology and the model's parameters. State explosion is tackled by turning to a scalable approximation based on ordinary differential equations. The entire model space is analyzed in a family-based fashion, i.e., at once using an efficient symbolic solution of a super-model that subsumes every concrete instance. Extensive numerical tests show that this is orders of magnitude faster than a naive instance-by-instance analysis

Runtime-guided mitigation of manufacturing variability in power-constrained multi-socket NUMA nodes

This work has been supported by the Spanish Government (Severo Ochoa grants SEV2015-0493, SEV-2011-00067), by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P), by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), by the RoMoL ERC Advanced Grant (GA 321253) and the European HiPEAC Network of Excellence. M. Moretó has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship number JCI-2012-15047. M. Casas is supported by the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and the Cofund programme of the Marie Curie Actions of the 7th R&D Framework Programme of the European Union (Contract 2013 BP B 00243). This work was also partially performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 (LLNL-CONF-689878). Finally, the authors are grateful to the reviewers for their valuable comments, to the RoMoL team, to Xavier Teruel and Kallia Chronaki from the Programming Models group of BSC and the Computation Department of LLNL for their technical support and useful feedback.Peer ReviewedPostprint (published version

A pH-based pedotransfer function for scaling saturated hydraulic conductivity reduction: improved estimation of hydraulic dynamics in HYDRUS

Hydraulic conductivity is a key soil property governing agricultural production and is thus an important parameter in hydrologic modeling. The pH scaling factor for saturated hydraulic conductivity (Ks) reduction in the HYDRUS model was reviewed and evaluated for its ability to simulate Ks reduction. A limitation of the model is the generalization of Ks reduction at various levels of electrolyte concentration for different soil types, i.e., it is not soil specific. In this study, a new generalized linear regression model was developed to estimate Ks reduction for a larger set of Australian soils compared with three American soils. A nonlinear pedotransfer function was also produced, using the Levenberg–Marquardt optimization algorithm, by considering the pH and electrolyte concentration of the applied solution as well as the soil clay content. This approach improved the estimation of the pH scaling factor relating to Ks reduction for individual soils. The functions were based on Ks reduction in nine contrasting Australian soils using two sets of treatment solutions with Na adsorption ratios of 20 and 40; total electrolyte concentrations of 8, 15, 25, 50, 100, 250, and 500 mmolc L−1; and pH values of 6, 7, 8, and 9. A comparison of the experimental data and model outputs indicates that the models performed objectively well and successfully described the Ks reduction due to the pH. Further, a nonlinear function provided greater accuracy than the generalized function for the individual soils of Australia and California. This indicates that the nonlinear model provides an improved estimation of the pH scaling factor for Ks reduction in specific soils in the HYDRUS model and should therefore be considered in future HYDRUS developments and applications

Optimizing the flash-RAM energy trade-off in deeply embedded systems

Deeply embedded systems often have the tightest constraints on energy consumption, requiring that they consume tiny amounts of current and run on batteries for years. However, they typically execute code directly from flash, instead of the more energy efficient RAM. We implement a novel compiler optimization that exploits the relative efficiency of RAM by statically moving carefully selected basic blocks from flash to RAM. Our technique uses integer linear programming, with an energy cost model to select a good set of basic blocks to place into RAM, without impacting stack or data storage. We evaluate our optimization on a common ARM microcontroller and succeed in reducing the average power consumption by up to 41% and reducing energy consumption by up to 22%, while increasing execution time. A case study is presented, where an application executes code then sleeps for a period of time. For this example we show that our optimization could allow the application to run on battery for up to 32% longer. We also show that for this scenario the total application energy can be reduced, even if the optimization increases the execution time of the code