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E-AMOM: an energy-aware modeling and optimization methodology for scientific applications.

, , , , , , , and . Comput. Sci. Res. Dev., 29 (3-4): 197-210 (2014)

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Performance Analysis and Optimization of Parallel Scientific Applications on CMP Cluster Systems., , , and . ICPP Workshops, page 188-195. IEEE Computer Society, (2008)Power and performance characteristics of CORAL Scalable Science Benchmarks on BlueGene/Q Mira., and . IGSC, page 1-6. IEEE Computer Society, (2015)A Java-based Distributed Debbuger Supporting MPI and PVM., and . Scalable Comput. Pract. Exp., (1999)Performance Analysis, Modeling and Prediction of a Parallel Multiblock Lattice Boltzmann Application Using Prophesy System., , , , and . CLUSTER, IEEE Computer Society, (2006)Integrating ytopt and libEnsemble to Autotune OpenMC., , , , , , , , , and . CoRR, (2024)An Autotuning-based Optimization Framework for Mixed-kernel SVM Classifications in Smart Pixel Datasets and Heterojunction Transistors., , , , , and . CoRR, (2024)MuMMI: multiple metrics modeling infrastructure for exploring performance and power modeling., , , , , , , , and . XSEDE, page 36:1-36:8. ACM, (2013)Modeling Performance of Data Collection Systems for High-Energy Physics., , and . CoRR, (2024)Autotuning Apache TVM-based Scientific Applications Using Bayesian Optimization., , and . SC Workshops, page 29-35. ACM, (2023)MuMMI: Multiple Metrics Modeling Infrastructure., , , , , , , and . Parallel Tools Workshop, page 53-65. Springer, (2013)